ASCE Inspire 2023: Infrastructure Innovation and Adaptation for a Sustainable and Resilient World

Saltwater intrusion—where saline water is introduced to a system (i.e., aquifers, soil, buildings) via processes like sea level rise—is becoming a severe and prevalent problem among coastal communities worldwide as climate change causes sea levels to rise. The collapse of the 12-story Surfside Condominium in Miami, FL, USA, on June 24, 2021, served as an alarm to coastal zone managers of the potential implications sea level rise can have on building foundations. To improve the climate-readiness of communities, civil engineers need to identify the possible degradation mechanisms of long-term saltwater intrusion exposure in existing and new building materials to enhance the longevity of designs. This research investigates the current literature related to saltwater intrusion and infrastructure globally with a focus on experimentation on building materials. Through a systematic review utilizing the Web of Science database, 164 papers were selected and evaluated via keyword search. This material was then further narrowed for this study to reflect experimentation on infrastructure (n = 28) and finally to highlight experimental testing of building materials (n = 14). Thirty-five experiments were conducted to study decay morphologies, deterioration mechanisms, mechanical properties, or resilience potential. Results indicated a need for standardization of saltwater intrusion testing and database creation to inform designers of risks presented by saltwater intrusion. Based on the frequency of occurrence, the research team recommends mercury intrusion porosimetry (MIP), optical microscopy, ultrasonic pulse velocity (UPV), X-ray diffraction (XRD), and water absorption capacity to determine decay morphologies from prolonged saltwater intrusion exposure. Standardization of experimental testing would allow streamlined comparison of materials both regionally and globally to inform designers and communities on how to improve resilience and climate readiness.

Prioritizing the adaptation of infrastructure and territories is an emergency, especially given that the cost of inaction now significantly exceeds the cost of prevention. However, if indicators can be found in the literature to evaluate physical and socio-economic vulnerability of critical infrastructure, there is no existing tool intended for decision-makers to measure the “performance” of measures, policies, or investments aiming to improve the resilience of infrastructure in their territorial context. For this purpose, Resallience has developed the Resilience Performance Assessment (RPA). It is an innovation composed of (1) a GIS-based platform allowing a comprehensive visualization of climate change impacts; and (2) an analytical dashboard to support stakeholders in prioritizing adaptation strategies and investments. This paper explains how the RPA has been applied for two entire Caribbean islands to provide a comprehensive visualization of current and future climate change impacts and a ranking of the most performant investment projects to reach national and international resilience targets.

Compound flooding conditions present a significant challenge for civil engineers in their pursuit to design for and maintain the integrity of a structure’s entire life cycle. Coupled with non-stationary processes due to a changing climate and land use change, risk is a moving target. Through the support of ASCE’s Task Committee on Compound Flooding, the Hydroclimatology Engineering Adaptation (HYDEA) sub-committee is developing a Manual of Practice (MOP) to provide a synthesis of available tools and methods of best practice for civil engineers designing for compound flooding conditions. This paper presents a primer for practicing civil engineers on this work. Hydrodynamic process-based models such as rainfall-runoff, riverine, and coastal modeling, as well as statistical models including multivariate statistical models, will be addressed. In addition, the importance of linking statistical and process-based models and their various approaches is identified. The MOP also discusses addressing nonstationarity due to changing local and regional conditions and tools to assess risk and uncertainty.

Coal is a critical commodity for US rail transportation and the larger economy. This study evaluates the impacts of the energy transition on railroads due to a decreasing share of coal in electricity generation. The impacts are measured by three metrics: annual revenue, node criticality, and network efficiency. Current impacts for the period 2005–2020 are assessed using the weighted network analysis method and Waybill data. Future impacts for the period 2020–2050 are predicted considering six scenarios, including the Energy Information Administration’s reference pathway, and five shared socioeconomic pathways adopted by the sixth assessment report of the Intergovernmental Panel on Climate Change (IPCC). The results suggest that annual revenues from coal shipments could decrease by up to 79% or increase by more than twofold between 2005 and 2050, depending on the scenario. The impacts of the energy transition are uneven on the freight rail, with some stations, represented by network nodes, being particularly sensitive to changes in coal shipments. Moreover, network efficiency could decrease by 19% or increase by 70% between 2005 and 2050, depending on the scenario.

Carbonation and chlorination are two major deterioration mechanisms that threaten the structural integrity and serviceability of reinforced concrete bridges. How the changing climate will affect the bridge deterioration and conditions in the long run is yet to be understood. Although Markov-based models are widely adopted in bridge condition deterioration modeling and asset management, these data-driven models are developed solely based on historically observed data (e.g., the NBI data) under the stationarity assumption and cannot robustly accommodate future climate changes. In this regard, this research aims at integrating mechanics-based deterioration modeling, which is physics-based and can better capture the environmental stressor variations, for bridge condition deterioration prediction under the changing climate. Specifically, material-level corrosion damage risks are first estimated by coupling mechanics-based deterioration models with climate data. The influence of existing concrete crack width on corrosion initiation and propagation is for the first time investigated in this context. Risk quantification is carried out via Monte-Carlo simulations by considering various sources of uncertainties. Finally, we established and calibrated the mapping from material-level corrosion damage risks to bridge component-level NBI ratings.

This study proposes a “Directional” Rate of Spread (ROS) adjustment factor approach, which applies different ROS adjustment factors depending on the location of the wildfire spread region, even for the same fuel model. Using the observation data gathered by geostationary satellites, the directional ROS adjustment factor is estimated based on the least-squares method. In addition, a new methodology is proposed to indirectly improve the prediction accuracy of regional-scale wildfire spread simulators by using a directional ROS adjustment factor. Since geostationary satellites can obtain observation data at short time intervals, we identify the relationship between the directional ROS adjustment factor and correct the adjustment factors based on the locations and times of the newly detected pixels in the geostationary satellite data and the fuel model distribution of the surrounding pixels. The proposed methodology is demonstrated through the 2020 Creek Fire in California, United States. The results confirm that the proposed methodology and the directional ROS adjustment factor can significantly increase the wildfire spread prediction accuracy.

Arlington County, Virginia, is developing a Stormwater Program Risk Assessment and Management Plan (RAMP). This paper summarizes how flood modeling and risk assessment were conducted considering climate change for urban interior flooding, riverine flooding, and coastal flooding. The modeling results were used to determine which areas of the county, critical facilities, vulnerable groups, and environmental resources are most vulnerable, based on current and projected flooding with climate change. Detailed risk assessments were then conducted for the watersheds with the highest number of vulnerable receptors. FEMA Hazus tool was applied to assess current and project future damages at neighborhood and watershed scale. In addition, more site-specific risk assessments were conducted for selected public facilities. Flood mitigation alternatives and prioritized strategic investments were developed for mitigating risk, based on economic, environmental, and social equity criteria and quantified project benefits and co-benefits with a measurable benefit-cost analysis methodology. Strategies included storage, conveyance, overland relief, as well as policy and programmatic alternatives.

Extreme wind and snow loads can be critical structural design parameters for Alaska. Understanding these extreme climate conditions under climate change conditions is valuable for adaptation and community resilience. Wind or snow loads can be the principal climatic loads, and the other can be the companion load. The effect of climate change on these two extreme climatic variables deserves investigation. Moreover, how climate change impacts both extreme climatic variables is still being determined; it is also worth understanding the spatial effect of climate change over Alaska for each hazard. For this purpose, this study investigated a large ensemble of simulations under a high-emissions climate change scenario to analyze the uncertainties of the future extreme wind climate and snow loads for Alaska. The impact of climate change on the design level of extreme events was investigated. The nonstationarity of the extreme wind speeds and ground snow loads under climate change scenarios were examined. The effects of the nonstationarity on determining the design wind speeds and ground snow loads are discussed. The correlation between the extreme wind speeds and ground snow loads is explored to understand how to consider the companion climatic loads in climate change scenarios. Uncertainties associated with future projections are explored. Structural design for climate change adaptation and community resilience in extreme wind and snow hazards is discussed.

Various climate change effects pose increasing risks to the nation’s infrastructure. Available methodologies address the risk-management problem primarily through cost-benefit analysis frameworks, which evaluate a comprehensive set of protection strategies against a wide range of simulated possible future scenarios. However, due to the substantial climate model uncertainties present over the future planning horizon, such strategies can often lead to less informed policies that might be optimal in an average sense, over the mean of anticipated future scenarios, but cannot offer adaptive solutions based on the actual climate effects evolving in time. To address these limitations, in this research, climate risk mitigation is instead formulated as a decision-making problem within a closed-loop stochastic control-based framework using Markov decision processes (MDP), taking real-time data into account, for evaluating the evolving conditions, and selecting the best possible, most informed life-cycle actions in time. Although broadly applicable, the merit of the framework will be illustrated through coastal risk mitigation against storm surge and sea-level rise in an idealized coastal city setting.

Hurricanes and other tropical cyclones (TCs) are a major natural hazard that pose significant threats to countries and communities along coastlines. Understanding the spatiotemporal patterns of TCs is essential to predict the potential hazards and risk. Most of the previous TC genesis models were based on parametric estimation methods that estimated the probability density of TC occurrence and genesis locations by assuming a parametric probability function. However, historical TC data may not satisfy the assumed function. To alleviate this challenge, a nonparametric estimation method, KDE, is applied. Accordingly, there is no need to assume a specific probability function. This study developed the TC genesis locations and time based on both historical data and global climate modeling data. First, kernel density estimation was applied to obtain the probability density of annual occurrence number and genesis location and time of TCs in the Atlantic Ocean using historical TC data. A standard normal density function was applied as a kernel function. Then, Monte Carlo simulation was applied to generate 10,000 years of TC events. This genesis model has been validated by comparing the probability density obtained from historical TC data, in the dimension of genesis time and location. This TC genesis model lays a solid foundation for future full-track model development of TCs in the North Atlantic basin that will incorporate climate change.

One of the regions that are most heavily impacted by changing climate is the place where land and water meet. With increasing water levels and risks from storm events, the need for resilient approaches that address the interaction of critical built infrastructure and ecosystems along shorelines has never been more important. Understanding the hazards and the potential impacts while incorporating design approaches that are adaptable to future changes is the challenge confronting these areas. Erosion, growing flood risks, and intense storms are damaging infrastructure and endangering coastal communities. Understanding and assessing these risks, working with nature and stakeholders to develop comprehensive solutions that consider physical risk reduction, ecology, and community together, is key to success. These solutions are increasingly multipurpose to gain the needed political and funding support. Resilience should be a key component of waterfront planning and infrastructure designs, new and rehabilitations. True resilience looks at the interaction with interconnected systems. While progress has been made, there are opportunities to do better developing holistic, innovative, and resilient solutions, especially at the interface of land and water.

In conventional teaching environment, complex civil engineering problems are taught with little visual cues given to the students mostly in the form of 2D drawings or textbooks and PowerPoint slides. The importance of visual aids cannot be downplayed in an educational environment as they increase student understanding of the course content. In this paper, the effects of desktop-based virtual reality systems on student learning in a civil engineering education environment have been investigated. The students considered as a case in this study belong to civil engineering department of a top-rated university of Pakistan. This research used interactable computer animations as a complementary tool to traditional teaching methods to enhance student learning of complex structures. The considered participants were split between the two groups, i.e., Group A (a simulation group) and Group B (a control group). Both groups were taught the same course content by the same full-time instructor. The control group studied the same topic in a conventional learning environment with textbooks and PowerPoint slides being taught, and the simulation group was taught with an interactive animation (addition of desktop virtual reality system) in addition to the traditional classroom teaching techniques. Student performance between the two groups was then compared through a knowledge assessment test. The knowledge assessment test result showed a mean value of 6.51 for Group A and 5.25 for Group B with a mean difference of 1.26. A t-test, using the Statistical Package for the Social Sciences, was further performed between the two groups’ results to determine if the results were significantly different. The test was found to be statistically significant at 5% with a t value of 3.54, thus indicating that the simulation group benefited from the desktop VR program. Hence, the assessment showed statistically significant improvement in understanding and learning of Group A as compared to Group B.

This paper will describe new technology that through nondestructive remote sensing and placement of scanning boxes (gravitational platforms), we can search for any element in the periodic table. We will describe the practical uses of it, streamlining the ability to locate subsurface water, underground utilities, geotechnical exploration of rock, soil, and mineral including precious metals without limited if any drilling. This paper describes two forms of new technology that passively and nondestructively senses location and quantities of any element in the periodic table. The first technology uses existing satellite imagery. A technology group has developed the ability to remotely survey and detect the presence of oil, gas, mineral, or any other element on the periodic table of elements. It can also map all ground fault lines in a location. This is achieved using a physical property that occurs at the subatomic level of physics, called nuclear magnetic resonance (NMR). All elements have a distinct and unique NMR signature, as well as seven resonant harmonic frequencies. The second technology uses the natural gravitational resonances emitted from all matter. A single sensor at close range can monitor impurities in water, for example, while multiple boxes can be temporarily deployed at larger distances to triangulate the locations and concentrations of underground changes in density of all rock, mineral, and ore structures. These gravitational wave surveys can see many hundreds of feet below the surface sensors to locate and quantify groundwater, locate the soils rock interface, locate underground caverns or voids, as well as changes in rock layers that may impact structures (without drilling). Included is a very brief overview of the physics and some references for those interested, followed by examples of some ways this technology has been successfully used.

Most of the infrastructure constructions in the industrialized countries were erected between 1960 and 1990 and are therefore now between 30 and 60 years old. In the course of recalculations of bridges, deficits in load-bearing capacity frequently arise due to aging of the structures, the increasing loads on these structures on the one hand and the changes in the design codes on the other hand. Studies showed deficits in the load-bearing capacity of existing structures especially regarding the shear load capacity. Often, a complete replacement of these structures is planned, which leads to high investment costs but also to massive traffic disruptions due to the complete or partial closure of the structure. Studies carried out showed the environmental impact of closing a bridge for one day and for one and a half years compared to the impact of demolition and rebuilding and the impact of strengthening of the existing structure. The comparison shows clearly that strengthening of existing structures is far more sustainable than building a new bridge. It can also be seen that the impact of pure demolition and replacement construction on the global warming potential is negligible compared to the impact of affected traffic. Therefore, in the last decade, research has been conducted at the University of Innsbruck, Austria, on new, efficient post-installed reinforcement systems with the help of which the lifetime of existing infrastructure buildings can be significantly extended. These new systems have to be installed in the existing structure as quickly as possible and without disturbing the traffic. This is the only way to keep traffic disruption and environmental impact to a minimum. In particular, the focus was on shear reinforcement with post-installed concrete screw anchors as shear reinforcement. The paper thus aims to show the positive effects of strengthening measures compared to rebuilding on global warming and minimizing the use of natural resources.

Resilience and sustainability of civil infrastructure have become major problems for about four decades. The concept of resilience and sustainability rose to notice in the late 1980s and has become a central issue worldwide. Most researchers, engineer practitioners, as well as construction industry are focused either on resilience or sustainability operating only without a mutual consideration of the findings, which leads to severe ineffective and inefficient sustainability of civil infrastructure. Though big data and the sustainability of civil infrastructure analysis are remaining unsolved and lagging, several engineering sectors have applied artificial intelligence (AI) to solve problems and analyze big data using machine learning (ML). However, the civil infrastructure industry is lagging to integrate AI, remote sensing (RS), and 3D printing (3DP) in the development of sustainability of civil infrastructure, and no developed process on how to desegregate these new applications. The main purpose of this research is to review these new applications of artificial intelligence, remote sensing, and 3D printing techniques for the sustainability of civil infrastructure. Results indicated that artificial intelligence, remote sensing, and 3D printing technology can increase the reliability and sustainability of civil infrastructure and the level of automation and standardization of civil construction engineering and pavement maintenance engineering leading to effective and efficient improvement in worker safety, reliability, and sustainability of construction materials, climate adaptability, and maintenance and rehabilitation repair accuracy and flexibility.

Recent studies in project management have indicated the importance of project selection criteria in organizational performance. This study proposes a machine learning approach to predict the impact of project selection criteria on organizational performance. This relationship was investigated using a dataset containing data from reputable organizations and applying different machine learning algorithms including multiple linear regression, k-nearest neighbor, and support vector machine. Our results demonstrated that there is a significant relationship between the parameters of project selection criteria and organizational performance. Further, it was identified that most significant relationship exists between project time, project return, technical risks, and sales, and the weak relationship exists between TechAvail and MarShare. This study contributes to the relationship between project selection criteria and organizational performance and provides insights into the application of emerging tools in data science and machine learning in operations management and project management research.

The use of Fiber Reinforced Polymer (FRP) composites has increased significantly in infrastructure applications due to their superior load-bearing capacity, high specific stiffness, and corrosion-free characteristics. One inherent drawback of FRP composites is their limited ductility, which hinders their widespread use in resilient infrastructure. The ductility of structural elements is required in resilient infrastructure to minimize loss of capacity, ensure appropriate robustness, and maintain structural integrity after extreme events. If FRP can show ductile behavior, it would make an attractive alternative for resilient infrastructure. 3D printing technology offers great potential for manufacturing ductile FRP composites with large freedom in design, high precision, lower material waste, and rapid manufacturing. One major advantage of 3D printing is its precise control of discrete fiber angles. This capability is unavailable when using conventional FRP manufacturing techniques such as vacuum-assisted hand layup. 3D printing offers an unprecedented opportunity to create the next generation of ductile FRP. This paper discusses the use of 3D printing technology in developing pseudo-ductile multi-angle FRP laminated composites. The unique capabilities of 3D printing were utilized to vary fiber orientation angles, the thickness of layers, and stacking sequence to produce several novel designs. The FRP composite designs were evaluated regarding stiffness, strength, and ductility. The results highlight the significance of using low and high fiber angle orientations to obtain proper strength and failure strain and thus maximize ductility. This investigation also introduces a new approach in FRP composite manufacturing by producing mixed fiber-angled composite layers to obtain moderate behavior with respect to layers with discrete fiber orientation. We demonstrate the nonlinear behavior and progressive failure of 3D printed FRP. We also suggest the design of combinatorically angled 3D printed Glass Fiber Reinforced Polymer (GFRP) composites using optimization algorithms to carefully select design parameters such as the fiber angle, stacking sequence, and fiber layer thickness to allow suitable load distribution within the 3D printed FRP composite leading to ductile failure.

Structural designs using artificial neural networks (ANNs) are relatively new even though the ANN has been an evolutionary technology in many areas, such as automotive, financial, and medical areas. The ANN-based Hong-Lagrange algorithm shows prospects for real-world applications, providing an auto design and optimizations that substantially increase design effectiveness while considerably reducing human efforts. Holistic designs of prestressed multi-story frames are performed in this study. Big datasets following American standards were generated based on the ABBA (auto-design-based building applications) frame generator, which was developed and verified by commercial software (ADAPT builder and MIDAS). ANNs are trained on big datasets, mapping 38 input parameters to 111 output parameters, to derive weight and bias matrices for the formulation of ANN-based objective and constraint functions of piperack frames. Costs, CO₂ emissions, weights, and energy consumption are minimized by applying sequential quadratic programming (SQP) to ANN-based Lagrange functions constrained by equalities and inequalities, which are imposed by architectural and code requirements. The present study provides an application based on ANN technologies for structural designs, which not only enhances the design efficiencies but also the sustainability of building construction by reducing CO₂ emissions and energy consumption.

An economical and tactical transition to renewable energy sources is critical for social welfare and national security. Construction of utility-scale solar photovoltaic (PV) plants is accelerating this transition. Solar PV structural behavior differs from typical building behavior in many ways. However, these structures are designed under the prescriptive provisions of existing building codes that do not recognize, or account for, the unique characteristics and risks associated with solar PV structures. The motivation for this study is to calculate an optimal solar-specific reliability index and compare it to the existing building code reliability targets to either validate or to suggest improvements to current structural design practice. The methodology involves application of a recently published target reliability index procedure where cost optimization and human safety are considered separately. This approach also permits the unique life safety implications of utility-scale solar PV plants to be explicitly captured by the reliability index and, therefore, the structural design. There are no occupant-related life safety risks associated with utility-scale solar PV plants. However, there is a power generation function life safety risk. That risk is captured herein as excess mortality due to solar PV structural failure-related power outage. The resulting solar-specific reliability index is then compared with the current prescriptive building code reliability targets. The results demonstrate that for the current inventory of utility-scale solar PV plants, the prescriptive building code reliability targets are excessively conservative. The provisions require a structural safety expenditure that does not result in measurable increases to social welfare. The associated cost penalties decelerate the transition to renewable energy by limiting the scope and scale of new solar infrastructure investment. The advantages commensurate with the solar-specific reliability indices presented in this paper can be captured by applying the performance-based procedures in the existing building codes to solar PV structures. This approach includes flexibility to adjust the solar-specific reliability indices proportional to the national inventory size and individual project scale as the transition progresses, thus, promoting the fastest transition to renewable energy with explicit management of life safety risk, while ensuring economy, social welfare, and national security.

Concrete cracking in bridges significantly endangers their safety and integrity. Traditional crack detection methods, reliant on human visual inspection, are labor-intensive and prone to errors. This paper introduces a unique framework for bridge crack detection and integration with building information models (BIM), trialed on a 423-ft bridge in Atlanta, Georgia. The framework comprises two main stages: (1) creating a BIM model using drone-captured images and structure from motion (SFM) photogrammetry, and (2) utilizing a deep learning-based encoder-decoder network to segment bridge cracks from orthomosaic images and superimpose these segmented cracks onto the BIM model. The suggested method showed robust performance, achieving a mean intersection over union (mIoU) of 0.787, precision of 0.751, and recall of 0.742. These results underline the potential of the proposed framework to improve the efficiency of bridge crack inspection processes.

In masonry cavity walls, the outer clay brick veneer experiences moisture/temperature-induced strains throughout its service life, while the concrete block backup, protected from temperature/moisture fluctuations by the building envelope, is prone to drying shrinkage. These long-term volume fluctuations, which may become more severe with climate change, cause differential movements between the veneer and backup and also cause distress in cavity walls and affect their durability if not properly accounted for in design. In Canada, masonry cavity wall designs rely on outdated experimental data that does not consider the interaction between mortar and masonry, while lacking substantial evidence from long-term field tests on modern structures. In this paper, preliminary results from a novel 2-step testing method for drying shrinkage on concrete masonry prisms are discussed. Future research includes moisture expansion tests on clay brick prisms, as well as mechanical tests on tie-masonry assemblies and wall-shelf angle assemblies.

Originally implemented in 1995 to communicate bathing water quality to the public, Auckland Council’s water quality management program provided an assessment based on retrospective water quality sampling. Safeswim (www.safeswim.org.nz) was deployed in 2017 to overcome limitations related to sampling data availability and to maximize the use of water monitoring data by integrating such data with real-time modeling, artificial intelligence, big-data analytics, visualization, and information dissemination. Safeswim is shaped around a single, central data platform Moata that gives Safeswim its capacity for managing and analyzing large, diverse datasets while running multiple models to provide Aucklanders with information updated in 15-min intervals. The platform has facilitated Safeswim’s greatly increased reliability and prediction performance over five years on from its first implementation, to close to 90% overall accuracy, and as a result has grown into a reliable and efficient public decision support system for the Auckland Council. The success of Safeswim in Auckland has led other communities around the world to consider and are inspired to implement similar programs.

Wildfires cause severe damage to land and infrastructure resulting in the federal government spending billions of dollars annually. Climate change, along with changes in land cover and socioecological conditions, has intensified the frequency and size of wildfires, primarily due to worsening hot, dry, and windy weather conditions. The ability to model these wildfires analytically will play a crucial role in the development of resiliency strategies through risk and fragility analysis of infrastructure components. Up-to-date wildfire and wildland-urban-interface data for Oklahoma, acquired through the US Department of Agriculture, was analyzed to understand the changing wildfire risk due to the changing climate, fuel sources, and encroachment dynamics. The data was analyzed to determine critical factors for future wildfire modeling efforts. Additionally, wildfire hazard maps for Oklahoma were developed.

Riparian zones are important transitional areas between upland and stream ecosystems that improve water quality, provide ecological habitat and corridors, maintain natural hydrologic processes, and provide other important ecosystem goods and services. Riparian management has grown in prominence, as these systems have become important foci of stream restoration efforts, stormwater best management practices, greenspace corridors, and nature-based solutions for climate mitigation. Restoration practitioners, regulators, designers, and researchers require models for assessing the ecological outcomes of different riparian management actions. While many models exist, they often are challenging to find in grey literature and may not have analytical tools and calculators available for other users. In this study, we compile nine riparian assessment procedures and models, construct a web application for accessing these tools, and demonstrate the utility of this application for informing stream restoration. Our project embodies the principles of Open Science by increasing the accessibility and practicality of existing scientific models. For instance, our web interface helps practitioners locate hard-to-find models, select among models for a particular application, and execute the models with error-checked calculators. The application also increases the long-term shareability of these riparian models through platforming that publicly shares computational code (e.g., through online repositories), reduces barriers to entry for analysts (e.g., programming literacy), and increases ease of use, all of which save time and money for stakeholders.

The transportation industry is entering a new frontier with three major simultaneous transformations: electrification, automation, and on-demand services. All three directly affect the same spaces of vehicle technology, infrastructure, and system operational performance. Further, each area of change adds its own driving forces of complexity to the others’ design challenges. This paper explores these transformations, framing the implications for vehicle technology and infrastructure and their impact on operating concepts and strategies, which in turn impact the sustainability of the transportation infrastructure in the built environment. Specifically, this paper examines battery-electric vehicles operating in a fully automated fleet within an “automated mobility district” (AMD) setting with multiple on-demand services. From this examination, the paper assesses the implications for transportation electrification, comparing and contrasting traditional fixed-route services with those of on-demand transit and automated transit networks, including the trade-offs between vehicle range (battery capacity), charging parameters, power availability, and operations predictability with respect to policy and infrastructure configurations. The paper also addresses the configuration and management of boarding and alighting zones (also called pickup and drop-off zones), and the need for complementary infrastructure intelligence at intersections and curbs. Finally, conclusions are drawn with respect to the system integration and analysis needs for AMDs regarding resilience and sustainability of mobility infrastructure.

This paper presents a summary of the research needs expressed during the 2022 Natural Hazards Research Summit. A key outcome of the summit was a strong call to build on the strengths of the existing research community to establish a network of long-term, interdisciplinary community test beds to drive the next era of discovery in natural hazards and community resilience research. The test beds would provide diversity in geographical region, hazard exposure, and population to enable a broad range of longitudinal studies on hazard impact, mitigation strategies, community engagement, risk communication, and the critical interplay between them. With sustained, multi-agency support, community test beds would engage engineers, social scientists, and community stakeholders to observe the long-term impacts of hazards, including the amplifying effects of climate change, and the evolution of recovery. Coupled with new and existing shared research infrastructure, test bed-generated data would enhance complementary experimental and simulation efforts while paving the way for effective deployment of technology solutions. Test beds would benefit from the scale of the operation; instrumentation, survey instruments, and data management tools would be standardized for efficient and repeatable deployment.

As solar energy generation is becoming more prevalent and requires more land usage, many real estates with uneven terrain that were deemed too costly for solar generation facilities years ago could potentially be utilized for future development. The challenge of installing solar arrays on these premises is due to the allowable pile reveal height of the solar arrays and the consistent slope of the PV tracker, which conflicts with the natural undulating surface of the earth. One solution is to use a tracker pile slope optimization technique. This technique was developed by applying an x and y intercept concept to the overall pile array alignment and setting up limiting parameters as set forth by the tracker manufacturer and pile structural engineer. This application has proven efficient in many utility scale solar generation development projects and has saved countless hours during the design and construction phase in comparison to previous design methodology.

Geotechnical asset management (GAM) is in the early stages of adoption within transportation agencies, especially in Mississippi (MS). Geotechnical assets in central Mississippi are vulnerable to excessive deformation due to the high volume changing Yazoo clay, further impacted by the high amount of rainfall and seasonal variations. In a changing climate, high-intensity rainfall events add excessive loads on the geotechnical and transportation infrastructure assets causing catastrophic failures. Therefore a GAM program that addresses resiliency and climate adaptiveness is much needed. Infrastructure climate resilient design is geared toward recovering quickly from shocks from adverse effects due to natural hazards fueled by ever-increasing significant climate events. Geotechnical infrastructure assets should be built and maintained such that they are not only resilient but also adaptive to evolving climatological conditions. Highway authorities have attempted to incorporate resilience into their asset management programs in terms of uninterrupted performance under shifting conditions. However, there is a need to identify the damage and loss metrics for geotechnical assets and include them in the GAM. Furthermore, evaluation of climate-resilient geotechnical assets requires a precise examination of the effects of new extreme patterns on short- and long-term behavior. This is particularly accurate for geotechnical assets such as highway slopes built on high-volume changing clay whose performance and strength are impacted by long-term and short-term precipitation and temperature fluctuation. Without proper monitoring, the geotechnical assets are susceptible to failures causing significant social and economic impact. Integrating technologies to implement advanced monitoring and assessment techniques are much needed for a climate-resilient GAM. To this end, this study focuses on incorporating advanced imaging technologies such as electrical resistivity imaging (ERI) and uncrewed aerial vehicles (UAVs) with optical and thermal imaging sensors into geotechnical asset performance assessment. Furthermore, this study presents a methodology for using advanced imaging technologies in implementing a decision support structure to test and investigate the resilience and adaptiveness of geotechnical assets such as highway slopes in central Mississippi. The findings will benefit the decision-makers to implement robust geotechnical asset management solutions in light of the increased intensity and frequency of significant climate events.

Building integrated photovoltaics (BIPVs) have become popular in recent years. Their availability and economic benefits to reduce the buildings’ net energy demand are studied in the literature. Photovoltaic integrated shading devices (PVSD) are one of the major components among BIPVs. However, only a limited number of studies investigated them for office buildings in humid subtropical climates. Therefore, this paper presents an investigation into the optimization of PVSD for an office building in Atlanta, GA. Shading depth, number of slats, shading angle, and PV ratio design variables are used to find their optimum combination to reduce building energy consumption, improve occupant comfort, and increase electrical energy generation. Ladybug tools are used for energy and daylight simulation, and the NSGA-II algorithm together with the Pareto frontier is applied for multi-objective optimization and to determine the optimum models. The results benefit architects, engineers, and decision-makers in support of cost-effective decarbonization strategies for existing/new buildings.

Vulnerabilities in healthcare equipment and medical facility infrastructure (mechanical, electrical, plumbing, and control systems) provide an opportunity for loss of business operations and possibly a severe impact to patient care activities. Upon a review of the published literature, cybersecurity vulnerabilities appear to be a secondary concern to facility managers, engineering design professionals, and contractors. This is likely due to siloed approaches amongst multiple stakeholders during the design, construction, and operation of healthcare facilities. This paper will utilize a mixed-methods approach to analyze quantitative survey results and qualitative interviews focused on medical facility infrastructure systems and will present feedback from a group of design professionals, contractors, and healthcare facility owners/operators. In turn, we aim to inform stakeholders regarding best practices used across industry to harden control systems in healthcare facilities.

The increasing size and complexity of modern systems present engineers with the inevitable challenge of developing more efficient, yet comprehensive computational tools that enable sound analyses and ensure stable system operation. This work proposes the continuous-state survival function (CSSF), and the concept of the diagonal approximated signature (DAS) is introduced as a corresponding surrogate model. The proposed concept is based on combinatorial decomposition adapted from the concept of survival signature. This allows for the advantageous property of separating topological and probabilistic information. Potentially high-dimensional coherent structure functions are the foundation. The proposed approach enables direct computation of the CSSF by means of an explicit formula and a stored DAS, avoiding costly online Monte Carlo simulation (MCS) and overcoming the limitation of binary component and system state consideration during resilience optimization for sub-structured systems. A proof of concept is provided for multidimensional systems and an arbitrary infrastructure system.

Pipeline infrastructure is crucial to help economic growth across the country. Pipelines are used to gather, transport, and distribute water, oil, or gas to end-use consumers. More than half of the pipelines in the US have been in service for more than 60 years. Cured-in-place pipe (CIPP) structural liner is a trenchless rehabilitation method to repair aged underground pipelines. Carbon fiber-reinforced polymer (CFRP) is a popular liner material used to rehabilitate and improve the service of pipelines. To investigate the effect of CFRP liner on pipeline maintenance and rehabilitation, we simulate the mechanical properties of CFRP liner for damaged pipeline rehabilitation through three-dimensional finite element analysis. A parametric study is conducted to demonstrate the effect of liner thickness and in-pipe pressure on the performance of the CFRP liner rehabilitation. The computational modeling results exhibit the relationship between liner thickness and rehabilitation effect, and the stress concentration location at the damaged area. The results of the paper can be applied for the optimal design of CIPP liner and enhanced sustainability and resilience of the US pipeline systems.

The 2021 Midwest tornado outbreak resulted in the collapse of the Amazon warehouse in Edwardsville, Illinois, which led to six fatalities. It is a large-box tilt-up building. To evaluate the performance of this type of building under tornado hazards, it is important to establish the fragility curve of the large-box tilt-up buildings under tornadic wind loads. The research objective of this study is to develop the fragility curve of large-box tilt-up buildings to tornadoes. To improve the high accuracy of the fragility analysis, the combined CM1 and CFD simulation will be applied to simulate tornado-building interaction to obtain the tornadic wind loads. Monte Carlo simulations will be applied to develop the fragility curves.

Megathrust subduction earthquakes, capable of triggering strong ground motions and tsunamis, significantly affect coastal infrastructure. Efficient disaster response and resource allocation require rapid and accurate assessments of economic consequences following such events. This study presents the development of a rapid multi-hazard loss estimation tool, utilizing simulated strong motion and offshore tsunami wave amplitude data to compensate for the scarcity of historical earthquake-tsunami data for calibration purposes. The research focuses on Japan’s Tohoku region, which has existing strong motion and tsunami monitoring networks. By adopting a multi-hazard catastrophe model and employing statistical methods, such as multiple linear regression and random forest regression, the study explores several predictive models for multi-hazard impact forecasting. The comparison of these models’ performances aids in identifying the most effective approach for rapid loss estimation. The findings contribute to improving immediate disaster response and recovery efforts, enabling better resource allocation and support for severely affected coastal communities.

Situational Awareness (SA) is the perception of the environment and events in time and space. It comprises three stages: perception, comprehension, and projection. In rapidly evolving situations with limited resources, disaster agencies need fast and reliable information to understand SA and support decisions. This paper proposes a social media-based approach to investigate SA using data from the 2020 western US wildfire season. The study uses the Twitter Search API with a key search term “wildfire” to scrape related tweets from September 2 to October 4, 2020. Subsequently, it applies Name Entity Recognition (NER) to identify geo-information. Next, it optimizes and applies transformer-based topic modeling (BERTopic) to classify topics from the tweets. Evolution of the topics over time can help identify phases of wildfire propagation. Using the identified time and space information, the study investigates SA stages across geographic regions and validates the results against wildfire response acts by responders. The results suggest that the proposed approach can provide consistent and comparable real-time estimation of wildfire situations. The study suggests guidelines for enacting timely and appropriate decisions and prioritizing operational needs.

Passive vibration mitigation using nonlinear stiffness couplings is a potential area for enabling structures to mitigate unwanted vibrations. Nonlinearity in stiffness is important in passive vibration mitigation because it can lead to additional vibration modes, which may be used to dissipate energy efficiently. Typically, the nonlinear stiffness is implemented using a combination of partially compliant positive and negative stiffness elements resulting in high mechanical complexity, poor compactness, high friction, and high maintenance cost. There is a need to develop fully compliant nonlinear stiffness devices with low mechanical complexity, compactness, and maintenance cost. First, a high aspect ratio serpentine beam configuration is selected. Second, a parametric study on a simplified computational model is conducted to match the desired nonlinearity in stiffness. The optimum design is fabricated, and the nonlinear stiffness response is experimentally validated. These nonlinear devices have potential applications in civil, aerospace, automotive, and energy harvesting.

Transportation systems are vital for cities’ emergency response activities and for long-term recovery during and after natural or man-made hazards. While their networked nature enables the mobility of humans, goods, and services, the same characteristic exposes transportation systems to complex failures, which can impact the economic system at multiple scales beyond the immediate location of the hazard. A series of economic methods have been used for the assessment of the economic impacts of transportation disruptions. However, previous studies have not adequately investigated the indirect economic impacts of transportation disruptions in terms of passenger flow disturbances. Especially, the indirect economic impacts of changes in households’ available time (such as work and leisure) due to transportation disruptions were overlooked. This paper introduces an explicit estimation model that was constructed based on the computable general equilibrium model with consideration of the time constraint. The relationships between the households’ time spent on a series of activities and household utility are illustrated. This model can be integrated into the interdisciplinary framework proposed in the authors’ previous research based on hazard characterization and damage assessment, analyses of transportation systems, and economic models, that achieved a more comprehensive understanding of the economic impacts of transportation disruptions.

This paper examines the performance of wood-based informally constructed houses in Puerto Rico, where resource-limited communities are vulnerable to climate-related hazards including hurricanes. Informally constructed housing is potentially vulnerable to hurricane and earthquake events but with significant variation depending on housing characteristics. This paper assesses the hurricane performance of representative housing typologies through a component-based performance-based wind engineering assessment framework. Results show that strengthening the roof envelope and roof-to-wall connections is essential for increasing the resilience of informally constructed wood houses in Puerto Rico. These upgrades are feasible for many builders and households, but challenges related to cost, appropriate construction, and material availability remain.

The average intensity and frequency of wildland fires have been on the rise over the years due to climate change. This, in combination with recent expansion and growth of rural areas, has led to an increased risk of wildfire damage for structures in the wildland-urban interface (WUI). This paper proposes a permanent passive fire protection system that is built into the structure. A flame-resistant material would be attached to the sheathing underneath the roofing and siding material. This system would allow the easily replaceable exterior components of the structure to burn while leaving the interior of the structure protected. Small-scale models were tested to compare the performance of five different flame-resistant materials, including structural wrap, ceramic fiber, Pyrogel, Kaowool, and intumescent paint. Post-burn charring evaluations and temperature analyses were conducted to determine which types of material and siding were most effective at protecting the small-scale models. Pyrogel outperformed the other materials but is by far the most expensive. The ceramic fiber material was the second most effective flame-resistant material overall and might have the potential to be as effective as Pyrogel if used in conjunction with the other materials tested, though further testing of material combinations would be needed to validate this supposition. The results of this project clearly demonstrated the feasibility of using a permanent passive fire protection system to protect structures in the WUI.

Building information modeling (BIM) and geographic information systems (GIS) serve as valuable tools for digitizing information related to the physical, functional, and geographical aspects of structures. The integration of these technologies can provide timely and accurate information to facilitate quick recovery from disasters. However, challenges such as loss of semantic information, data incompatibility, and redundancy persist. This study identifies key benefits and challenges of BIM-GIS integration using Delphi studies with industry experts and a questionnaire administered to smart city infrastructure users. Benefits include effective information management, enhanced visualization, and improved coordination, while challenges encompass loss of semantic information, data incompatibility, and redundancy. Results highlight the importance of developing strategies to ensure smooth integration for effective disaster management.

Modeling community resilience to flood hazards has gained substantial interest over the past two decades due to the increased risk from climate change and urbanization. Climate change increases both the frequency and intensity of some natural hazards, which is further exacerbated by urbanization. Post-hazard functionality assessment of buildings and communities following flood hazards is crucial for risk- and resilience-informed decisions. Although modeling building damage is essential to quantify the post-hazard functionality, it is insufficient for a comprehensive community resilience assessment. In this research developed a comprehensive flood post-hazard functionality model by chaining engineering models for damage prediction with socio-economic models. This approach accounts for the complex physical, social, and economic interdependencies between the different systems within the community. This approach is intended to be the first step in a comprehensive socio-physical community resilience model that can inform resilience-based decisions for buildings and communities.

Water infrastructure systems are interconnected with social systems—water services are essential for sanitation, drinking, and emergency response, among other societal needs. However, the level of water service delivered is not equal across all communities, especially in disasters. For example, water service disruptions may disproportionately impact low-income neighborhoods and other marginalized communities that lack resilient infrastructure improvements. As such, we must consider the socio-technical impacts of water service disruptions, specifically understanding how inequities emerge in the provision of water services during disasters. Here, we perform a systematic literature review, identifying challenges utilities face in the equitable provision of water services in disasters. Findings show few articles that study water infrastructure management post-disaster use an equity lens. We conclude that further research is needed to spur equitable water-sector preparedness, planning, and response to disasters.

Transportation is a critical sector within emergency and disaster response, and timely communication is necessary. Social media has gained popularity as both a means to communicate and to gather information during emergencies; however, it is rarely leveraged to any significant extent within transportation. Given that some social media platforms have billions of active monthly users, the issue of barriers to leveraging this data source within transportation has been raised and is addressed within this study. The primary objective is to identify the most significant barriers to social media use before, during, and after emergencies within transportation agencies. A state-of-the-art review of social media use within transportation is first presented, in which situational awareness, natural language processing, and image-based machine learning techniques have emerged with transportation applications. This is supplemented by a survey of transportation officials to gather insight into the current use of social media data and perceived barriers within emergency contexts. Policies, information accessibility, and a lack of specialized tools emerge as common threads that prevent further implementation of social media within transportation. Recommendations are provided for further research to expedite implementation and to positively impact disaster response.

Reinforced concrete (RC) frames have been an indispensable part of the construction industry for several decades because they resist both gravity and lateral loads such that the entire structure achieves a certain level of performance against seismic loads as per the performance-based design philosophy. The construction procedures for RC frames often encounter many non-engineering defects, especially in developing countries where the quality assurance check is cumbersome and usually ignored. Among these non-engineering defects, the presence of insufficient rebar splice length can affect the seismic performance of RC structures and can compromise their integrity. To this aim, this research gives new insight into the effects of inadequate rebar splice length on the seismic behavior of RC frames. For that, the experimental approach was adopted by using the micro-concrete concept because it demonstrates a simple, cheap, and evident experimental method to assess the seismic performance of properly designed RC frames. The shake table test was conducted to study the behavior of two scaled RC frames (1/16 scale): one standard frame and the other with an inadequate rebar splice length. The experimental result demonstrated that the frame with insufficient rebar splice length had a significant capacity reduction, and the structure was extremely susceptible to seismic loading conditions. Furthermore, the crack initiation and propagation in the standard frame were at the beam-column joints and the base connections. However, in the defective frame, the presence of insufficient rebar splice length altered the frame’s damage points and led to the collapse of the entire structure at the location of the splice. In this way, this study evaluates the impact of high-risk structural components due to non-engineering defects. This study can be used to spread awareness among the construction workers and site engineers who are responsible for quality assurance and quality control on-site to circumvent non-engineering defects and associated strength issues.

Validation of community resilience models is a critical step for the development of these models. This paper provides a preliminary literature review of model validation in general and validation of resilience models in particular, discusses challenges associated with validation of community resilience models considering cascading and compounding hazards, and presents a case study focusing on validation of a fire following earthquake (FFE) model. The FFE model, previously developed by the lead author, was built by leveraging similarities between the underlying physics of failure of FFEs and normal condition ignitions. Validation of this FFE model enables realization of the research contributions of the model, including estimating fire risk for areas with limited data of historical FFE occurrences, identifying infrastructure and social factors that exacerbate fire risk following an earthquake, and enhancing realism in fire risk estimates through consideration of the effect of social factors on risk associated with FFE.

The pile-founded floodwalls are critical civil infrastructure for the protection and mitigation of hazards due to flooding and sea level rise. The stability of the floodwall under potential flooding hazards is a very critical problem for the risk management of such infrastructure. However, most existing studies focus on the deterministic stability evaluation of floodwalls under hazard conditions. The geotechnical uncertainties, especially the soil spatial variability due to the complicated depositional environment, are generally not explicitly considered. This paper presents a study to explicitly consider the spatial variability of soil parameters for the reliability assessment of a pile-founded T-wall system. This reliability assessment was implemented in an advanced probabilistic framework integrating the subdomain sampling method, random field theory, and finite difference method. A case study of floodwalls is employed to demonstrate the proposed framework for the reliability assessment of pile-founded floodwalls. The results from this study can aid in the risk-informed decision for practitioners to assess the reliability of floodwalls in the face of flooding hazards.

This paper describes a study that assesses the functional recovery performance of existing buildings in earthquakes, exploring opportunities to improve post-earthquake building function through seismic retrofit. The results show that life safety retrofits can significantly improve functional recovery, but also may not. Rather, direct consideration of building function can be used to identify retrofit solutions that lower function disruption as well as life safety risks. The paper also describes qualitative research conducted to define the barriers and opportunities to achieving functional recovery seismic performance for existing buildings. The barriers include costs and a lack of guidelines to inform building design for functional recovery. However, functional recovery goals have significant alignment with sustainability upgrades and other owners’ goals, presenting an opportunity. These barriers and opportunities provide insights into research needs and changes in building practices that can support functional recovery after climate hazard events, which are described in the final section.

Pipeline failures caused by aging, corrosion, and damage may result in irreparable societal, economic, and environmental consequences. Therefore, pipeline safety and integrity are crucial to the robustness of modern societies. However, current pipeline risk analysis and management have some limitations in predicting pipeline holistic failure for integrity management. In this paper, we develop a machine learning-based risk model to predict the failure type of pipelines. We train and test the risk models based on the historical data from a report covering 50 years of spillage data in Europe. Various factors, such as pipeline diameter, age, pipeline location, and land use, are considered in the models. We propose nine risk models and apply the support vector machine (SVM) approach to obtain the best model type with the highest prediction accuracy. The best risk model will provide pipeline operators with an efficient and effective way to evaluate pipeline conditions and guide pipeline rehabilitation and maintenance procedures.

Transportation infrastructure systems provide operations to support the mobility of goods and people. Following flooding, the blocked road access would prevent people from accessing essential services; thus, it requires efficient restoration. Efficient infrastructure restoration cannot be achieved without rapid damage identification. However, due to the difficulties of road access, only a limited number of damage inspection data are available immediately following flooding. Aimed at reliably predicting infrastructure flood damage status in a timely manner, this research integrates geospatial correlation (i.e., closer things are more alike than things that are farther apart) to address the issues with incomplete damage inspection data. To achieve this, a Bayesian generalized linear geostatistical model was adopted. The model has been demonstrated to provide reliable prediction of highway flood damage with limited damage assessment data. In practice, the specialized model is expected to alleviate the effort and cost associated with infrastructure damage identification and facilitate the rapid execution of restoration operations.

Ensuring the security of coastal infrastructure and users’ safety in the Great Lakes region is crucial to maintaining sustainability and resilience against frequently occurring inclement climates. To assess the risk of flood hazards for certain types of infrastructure, we have developed risk assessment models based on machine learning to generate predictions of risk scores. The risk score is based on multiple components, such as exposure, vulnerability, criticality, and sensitivity to flood hazards. The dataset is from the Southeast Michigan Council of Governments (SEMCOG). Gaussian process regression (GPR) and support vector machine (SVM) are applied to train (75%) and test (25%) data, respectively. We find the model predictions are accurate compared to the original data points, and the GPR model exhibited better performance than the SVM model. The risk assessment model can be helpful to identify the degree to which infrastructure assets are at risk and provide recommendations for decision-makers on minimizing the effects of future hazardous flood events.

About 6% of 19,284 bridges in Indiana are rated structurally deficient. The deck area of structurally deficient bridges accounts for 3.3% of the total deck area on all structures. This deteriorating infrastructure impedes the development of a sustainable and resilient society. Therefore, there is a significant need to evaluate the risk for optimized infrastructure investment. We conduct a risk assessment for Indiana’s bridges by applying machine learning-based deterioration models with the support vector machine (SVM) and Gaussian process regression (GPR) algorithms. The machine learning-based risk assessment is able to generate a reliable risk analysis tool for bridge infrastructure in Indiana. Results from the study can provide stakeholders and decision-makers with an efficient and effective way to evaluate the performance and status of the bridge infrastructure. The outcomes of the study can be used to prioritize investment in repairing bridges based on different risk levels, save the cost of restoration, and extend the service life of the bridge infrastructure.

This paper examines the application of machine learning (ML) techniques in the prediction of the compressive strength of recycled aggregate concrete (RAC). The ML models are trained on a comprehensive dataset composed of 981 different RAC test results. Four algorithms of multiple linear regression, lasso regression, random forest, and histogram-based gradient boosting are investigated. The ML training workflow consists of careful feature selection and rigorous hyperparameter tuning based on cross-validation on the training set. The prediction accuracy of ML models was measured by calculating R-squared and root mean squared error metrics. Lastly, sensitivity analysis was performed to measure the impact of input features on the RAC compressive strength. The results indicate that histogram-based gradient boosting model results in the highest accuracy among the studied ML algorithms where water to cement ratio and cement content were found to be the most influential parameters.

Asphalt continues to be reclaimed and reused more than any other product in the US, and cold asphalt pavement recycling takes sustainability one step further. As its name implies, cold recycling is an asphalt pavement rehabilitation method without the application of heat during the construction process. This economical technique is not only effective in eliminating distresses of asphalt pavements but also conserves non-renewable resources and energy. In this case study, cold central plant recycling (CCPR) was used for its benefits and applicability to rehabilitate parking lots. The objective of this document is to present energy and materials saving results, and emissions reduction of a CCPR base asphalt course produced for parking lots of a major retailer in the US. On average, for a 23,226 m² (250,000 ft²) parking area, the built pavement yielded 50% in production energy savings, 42% reduction in GHG emissions, and 5,195 t of material were saved rather than mined, which adds up to 346 truck trips.

Plastic waste is a non-biodegradable material that poses challenges for easy disposal and can lead to environmental hazards. In this study, waste plastics were added to 80/100 bitumen at a weight ratio of 0%–1.5%, and several tests were conducted to evaluate the performance of the modified bitumen, including softening point, ductility, flash and fire points, and penetration grade tests. The results showed that the ductility value decreased significantly when the plastic content exceeded 0.5% by weight of bitumen. Therefore, it was recommended that the optimal amount of waste plastic in bitumen should be equal to or less than 0.5% by weight to maintain the required ductility for flexible pavement construction. To test the practical application of the modified bitumen, a pothole on the road was repaired using bitumen with 0.5% plastic content, and it showed satisfactory performance.

Reliability assessment is a powerful tool in evaluating the safety level of civil structures and infrastructure systems. The underlying concept is to probabilistically compare the relative magnitudes of resistance and load effect. During the past decades, engineers and asset owners have been expanding their focus from reliability to resilience, in an attempt to adapt and manage risks in such a manner that the impacts of hazardous events on structures/systems are minimized. With this, one would naturally ask: Can we establish a unified framework for reliability and resilience analyses? This paper addresses this question, and the answer is “yes.” To this end, a new concept of “resilience capacity” is proposed, which is a generalization of the traditional load bearing capacity (resistance). By definition, the resilience capacity is a random variable that represents the structural (or system’s) ability of absorbing, recovering from and adapting to a load effect. Applying the concept of resilience capacity, this paper shows that the reliability of a structure/system is a special case of resilience.

Dealing with existing structures, either for assessing and/or judging the structural qualities or for revamping, mending, or strengthening, inevitably provokes a dilemma: the dilemma of existing, decade-long successfully used structures which exhibit no signs of damage, overstraining, or fatigue, not complying with the rules to be followed when taking the actual standards and regulations as a base for the deliberations. This disaccord is in many cases resolvable by addressing the fundamental hierarchy of standards and by using structure-specific, that is, individualised assessments methods based on the principles laid out, for example, in EN 1990:2021 “Basis of Structural Design.” Instead of using lump-sum safety factors, the standard procedure to be found in the structural engineer’s toolbox, this strategy leads, via the determination of structure-related failure probabilities and the comparison of these values with the generally accepted reference value, 10⁻⁶ per year for typical building constructions, to a bespoken and more generalised formulation of the structure’s quality.

Vacant properties have increased and become a problem in cities in the US. Few studies addressed how to utilize vacant properties for affordable housing despite they can be a great resource for affordable housing. To improve affordable housing sustainability, researchers recommended incremental development because it can satisfy residents’ dynamic demands over time and engage them in house design. This can be implemented by utilizing vacant buildings to maximize their potential for affordable housing. Through the literature review, critical sustainability criteria for affordable housing are yielded. This paper demonstrates how the incremental development of vacant buildings satisfies the critical sustainability criteria. A development model and a guideline for site selection are provided to show how vacant buildings should be modified. The incremental development model and the guideline can contribute to the understanding of relevant stakeholders to address vacant properties and affordable housing at the same time. Future studies can leverage the outcomes to develop a detailed framework including an analytic hierarchy process.

The terms resilience and sustainability have been interpreted in multiple ways across fields of study, and the meanings of these terms continue to evolve. The use of these terms and the impetus for associated research in engineering and other fields impact government decisions, business decisions, and multinational agreements. To better understand the evolution of sustainability and resilience and interlinkages, a timeline of major natural (i.e., climate and weather-associated) disasters, US-based and international policy agreements, engineering standards, and other key geopolitical events related to the concepts were developed. The timelines are synthesized and cross-referenced by analyzing disaster-related spending and research outcomes over time. This evidence is used to understand trends that relate to sustainability and resilience across research, funding, and policies. This research project seeks to understand the direction of causation between science and policy, the differences in objectives and usage between the terms resilience and sustainability, and the evolution of these concepts.

The paper describes a new model of socio-technical systems (STS) that adequately takes into account about the relationship between cyber-physical infrastructure (CPI) and the society that serves as the indispensable element of the feedback chain management of the STS, according to generalized criteria of regional risk. In this context, supra- and sub-resilience of STS are introduced and discussed, and an example of both is given, as related to a municipality/region.

The Red Hook Coastal Resiliency project (RHCR) will provide flood protection to the Super Storm Sandy impacted Red Hook neighborhood of Brooklyn, New York. The capital project is a Federal Emergency Management Agency (FEMA)/City of New York joint funded project, whose design efforts are being led by the New York City Department of Design and Construction (NYCDDC) Infrastructure Division and their design consultant, NV5. In general, a flood resiliency project promotes sustainability, but as with any infrastructure project, there are inherent challenges. Yet, when the Envision Sustainable Infrastructure Framework (Institute for Sustainable Infrastructure, 2018) is used as a planning and design tool to assist in identifying and overcoming inherent challenges, it results in a sustainable, equitable, and resilient project that is beneficial to the community. The sustainable infrastructure team at NYCDDC consolidated its varied Infrastructure Division’s projects into 10 typologies. Referencing the Division’s General Requirements for Engineering Design and Related Services, the Design Guidelines and Directives, and lessons learned from earlier applications, baseline Envision verification ratings were calculated and assigned to each of the typologies. One of the difficulties in implementing the Envision framework into a capital project is encouraging the project team to address sustainability, resiliency, and equity while not sacrificing the main goals of the project. A strategy the design team implemented was to weave Envision-related discussions into every project meeting. This ensured that the Envision principles were evaluated in all discussions, which helped educate the design team and stakeholders. The application of the Envision framework on the RHCR project has initiated discussions focused on the reuse of materials, installation of alternative energy sources, multi-modal transportation connections, net embodied carbon analysis, and other topics that have bolstered the Envision rating for the RHCR project from a Coastal Resiliency Typology Gold Award baseline to a potential Envision Verification Platinum Award Level. Using the Envision framework, NYCDDC has influenced policy changes on a project scale in that the RHCR project will pilot NYCDDC’s new Sustainable Construction Specifications—incentivizing the contractor to implement strategies that support the project’s sustainable efforts. On an agency scale, NYCDDC’s Blueprint (NYCDDC, 2022) established a sustainability task force to review all incoming infrastructure projects and identify sustainable and resilient opportunities. The RHCR project is currently in the final design phase with many sustainable efforts identified and actively being examined for feasibility and implementation. As the first NYCDDC Coastal Resiliency Typology project using the Envision framework during the design phase, the foundation is being set for the future of New York City’s Coastal Resiliency Program.

The changing climate is impacting society, the natural and built environment, and system processes from our global supply chain to our transportation infrastructure network. Recent events such as Hurricane Ian and the tragic building collapse in Florida have raised even more concern for the vulnerability of infrastructure and civil infrastructure systems near the coast when confronted with natural hazards. Climate change is exacerbating these effects, particularly on residential construction, roads and bridges, and the effect that saltwater intrusion has on the integrity of infrastructure. With Delaware being a low-lying state, it is no stranger to flooding and how the structural integrity of infrastructure has been compromised and, in some cases, destroyed due to relentless amounts of water due to rising tides and compound inundation. As such, there is a need to predict these new forces on structures like low-rise wood frame residential buildings, and how to quantify the effects of deterioration rates onset by saltwater intrusion on its strength resistance due to imposed loads. Specifically, saltwater intrusion can impact groundwater salinity and nearby structures, thereby weakening foundations and compromising the overall structural integrity of building materials like concrete, steel, and wood. In this study, anticipated water levels due to expected sea-level rise (SLR) and inclusion of deterioration constitutive material models defined within a suite of structural (building) models are analyzed. To represent the degradation of wood structure over time, strength reduction factors of 15% and 40% are considered for the years 2030 and 2072, respectively. The effects on the in situ structural capacity of a suite of archetypes for residential construction are explored and compared to intended design capacities. The results are used to assess the structural vulnerabilities and predict the risk of failure of these deteriorated structures when subjected to varying water forces because of SLR and compound inundation.

While bridges on the National Highway System are inspected and actively maintained by state departments of transportation in the US, the maintenance of county-owned bridges is the responsibility of local governments. They need simplified and improved metrics to better assess the service life and sustainability of their bridges. It is not a sustainable practice to seek state or federal funds when their bridges are posted or closed. Thus, actionable metrics are required to better maintain locally owned bridges, particularly those that are more vulnerable and/or located in rural areas. Local governments aim to make a budgetary plan and conduct timely repairs, but they need decision aids because communicating priorities on multiple bridge items can be challenging. By using deep learning-based computer vision techniques to automatically detect distress in the five benchmark maintenance categories, local governments can better understand maintenance urgency and work toward enhancing the sustainability of their bridges.

The rainfall pattern in Mississippi has been affected by climate change, leading to increased precipitation that can cause the failure of highway slopes made of highly plastic clay soil. This soil is highly expansive and experiences repeated shrink-swell behavior, which softens the soil, reduces its shear strength, and makes it vulnerable to failure. To address this problem, vetiver grass was used as a nature-based solution to stabilize a highway slope along I-20 in Jackson, Mississippi. The grass’s deep and fibrous roots hold the soil in place, extract water, and thus act as a drainage medium. Monitoring equipment, such as rain gauges and moisture sensors, was installed to track rainfall, moisture content, and matric suction. The study showed that the vetiver grass reduced the perched water zone, reinforced the slope with increased suction, and prevented slope movement. This transformative and climate-resilient approach provides an effective solution for highway slope repair.

Heron’s Head Park is located on an artificial fill peninsula in San Francisco Bay owned by the Port of San Francisco (“Port”). In 1998, the Port conducted a project to rehabilitate and enhance tidal marsh habitats on approximately 10 acres of the peninsula. Since that time, there has been extensive wave-driven erosion along the peninsula’s southern shoreline, significantly reducing the area and quality of the marsh habitat. In 2022, the Port completed a shoreline restoration project that constructed a coarse gravel beach, rock shoreline stabilization structures, and other habitat enhancement elements. The design intent was to create an ecologically functional shoreline that will resist erosion, improve the habitat value of the marsh, and be resilient to sea level rise for the next 30 years. The project provides a demonstration of a nature-based design approach, and the Port plans to monitor the site’s physical and ecological evolution over the next 10 years to inform future nature-based shoreline management.

Evidence suggests natural and nature-based features (NNBFs) such as coastal wetlands and islands can reduce maximum water levels during storms but the efficacy of these measures is highly variable. Combined storm surge and wave modeling results are presented to (1) quantify the effects of coastal wetlands on current and future water levels for frequent events; (2) determine how coastal wetland management can be used to minimize wetland degradation; and (3) simulate potential wave attenuation benefits of small targeted NNBF designs. Wave attenuation accounted for the majority of water level reduction and NNBF elevation was correlated with wave attenuation benefits. Minimal levels of coastal wetland management reduced wetland loss and lowered water levels compared to no-action. Limited sediment resources should be targeted towards to the most vulnerable areas of the back bay to optimize wave attenuation benefits of existing wetlands into the future.

Living shorelines for coastal protection have favored engineering with reef-forming bivalves, such as oysters, because of their capacity to form dense living breakwater structures that provide both coastal protection and ecological benefits. Various constructed oyster reef (COR) designs have emerged, and investigations of their wave attenuation performance are needed to validate designs and compare design performance. The wave attenuation properties of (1) a novel emergent COR, called “QuickReef”; and (2) oyster shell bag breakwaters were measured in field experiments across coastal North Carolina. Results demonstrate that the emergent reef-type breakwaters performed comparably to oyster shell bag breakwaters in terms of wave attenuation. Wave attenuation is maximized at low tide when freeboard is the greatest. Field data were also compared to an existing empirical formulation for predicting wave attenuation of emergent rubble-mound breakwaters. Results demonstrate that wave attenuation performance of reef-type breakwaters and oyster shell bag breakwaters are not well predicted by existing breakwater theory, suggesting a need for empirical formulations for living shoreline-type breakwaters to inform effective design.

In this study, cost drivers of urban stream restoration practices are evaluated by performing life-cycle cost analyses on common restoration actions. The study’s objective is to examine the relative contributions of capital and operations and maintenance (O&M) costs to life-cycle costs over a range of restoration actions and sizes. Ultimately, this study will inform restoration practitioners of the importance of accounting for O&M expenses and help weigh the relative merits of different management actions. Capital and O&M costs were compiled from literature for typical urban stream restoration actions, such as bank stabilization, channel rehabilitation, and riparian restoration. Both costs were normalized by project extent to estimate typical ranges of unit costs and the frequency of actions. Representative costs were then estimated for a hypothetical project size range, life span, and other factors. Although capital costs are often a primary decision factor to select restoration actions, O&M costs are often underemphasized. This study showed that O&M expenses could rival capital costs in some contexts and that complete life-cycle cost analysis is crucial to decision-making about the long-term efficacy of urban stream management actions.

With increasing coastal hazards and ecosystem deterioration, there is interest in the US and internationally in restoring wetlands to reduce coastal flooding and adapt to sea level rise. Restoring diked and drained former wetlands, such as the salt ponds in San Francisco Bay, California, represents a promising strategy but requires specific analyses to evaluate performance and effectiveness. It is widely acknowledged that wetlands can reduce flood risk, but under some conditions, marsh restoration may also amplify flooding. These effects can be controlled by the restoration design and should be included early in restoration planning. Here, we explore management scenarios in one of the salt ponds of San Mateo County with both a numerical and a simplified hydrodynamic model to understand how different restoration scenarios influence water levels with sea level rise. We provide guidance on key factors for restoring coastal wetlands now and into the future.

The aftermath of Hurricane Sandy in 2012 accentuated the vulnerabilities of low-lying communities to extreme hydraulic events. However, Sandy also made it clear that these vulnerabilities were not equally experienced. Communities on the Rockaway Peninsula in Queens, New York, were particularly devastated by Hurricane Sandy with more than 1,000 structures destroyed and 10 ft of storm surge flooding the area and recovery continues to this day. The COVID-19 pandemic further exacerbated these vulnerabilities with significant impacts on employment. The Lily Pad is the study of a New York City Housing Authority (NYCHA) prototype mobility hub program to address these vulnerabilities. The mobility hub’s goal is to promote climate change impact readiness and inform government agency operations and maintenance such as NYCHA’s. This was an NSF planning grant. This paper will focus on the first goal of the planning grant which is, through the use of community engagement and data gathering, the creation of feasible pilot mobility programs to connect the community, neighborhoods, and vulnerable populations with affordable housing, jobs, and critical services.

This study derives a tsunami F-N curve for Tofino, British Columbia, subjected to the tsunami threats from the Cascadia subduction megathrust earthquakes. An F-N curve is an exceedance frequency (F) plot in terms of the number of anticipated fatalities (N) and can be used for evaluating the adequacy of current tsunami risk situations, compared with the acceptable risk tolerability limits. To evaluate the tsunami fatality risks for residents and visitors in Tofino, a tsunami catastrophe risk model is developed by integrating stochastic rupture models of the Cascadia subduction events, high-resolution tsunami inundation simulations, and tsunami fatality rate functions. The obtained F-N curves for Tofino intersect with the local scrutiny line set by the UK Health and Safety Executive, indicating that the tsunami risks for Tofino residents and visitors are concerning. Thereby, when effective risk mitigation measures can be implemented, such actions should be pursued.

The Environmental Protection Agency (EPA) often waives certain Clean Air Act requirements regarding ultra-low sulfur diesel (ULSD) fuel use after disasters. Under the ULSD fuel waiver, regular or high-sulfur diesel fuel can be used in vehicles and equipment involved in disaster recovery efforts. This research examines the impact of the ULSD fuel waivers on sulfur dioxide (SO₂) concentration in the regional air after Hurricane Sandy. The EPA waived the ULSD fuel regulations only for New Jersey, New York, and Pennsylvania, among the northeastern states affected by Hurricane Sandy. Panel data models were used to estimate the EPA waivers’ impacts on the SO₂ concentration while controlling for disaster effects and other confounding factors. The empirical results showed that the EPA waivers significantly increased the SO₂ concentration by 0.57 ppb. The findings can assist policymakers and decision-makers in understanding the potential environmental effects of environmental policies implemented during the disaster periods.

Global cities are expected to face more severe climate crises in the future; therefore, it becomes increasingly acute to better understand urban microclimates, especially the urban thermal environments. This paper proposes an innovative urban microclimate digital twin framework for integrating multi-sourced spatiotemporal data, investigating effective modeling approaches, and exploring the mitigation potential of health risks and environmental harms resulting from extreme microclimate conditions. We examined our framework in the case of Phoenix to simulate its fine-scale thermal conditions. Our evaluation of multiple models shows that the GNN model outperforms other regression models by 36.3% and suggests that planting trees could be an effective strategy for heatwave mitigation. We also tested different urban greening strategies for their overall cooling efficiency, indicating that our framework can be used to assess fine-scale urban policy interventions and thus generate insights to inform the design and implementation of efficient mitigation plans.

Economists found that post-disaster assistance programs often reduce the demand for flood insurance. This crowding-out effect, also called “charity hazard,” is relatively underexplored in the US. To bridge that gap, this paper presents a causal model that analyzes the impact of post-disaster federal payouts on flood insurance enrollment using historical data from 1,158 US counties between 2016 and 2020 with positive flood damage where a major flood-related disaster had been declared. To develop the causal model, this paper utilizes the propensity score matching method to assess the impact of receiving federal payout in a county in a year on the percentage change in the number of flood insurance policies and the total insured value of the flood insurance policies in the subsequent year. It was found that the availability of the federal payout in a county in a year increased the number of flood insurance policies by 5.2% and the total insured value of those policies by 4.6% in the following year.

The paper describes a new approach to quantifying social unrest that occurs after an accident or natural disaster associated with the loss of operation of urban life support infrastructure. This innovative framework based on social networks’ statistics: (1) allows real time monitoring of the relationship between the material and non-material parameters (between physical consequences of an STS accident and the behavior and mood of the society that uses the services provided by this STS); (2) provides tools for constructing dynamic probability density functions (PDFs) that permit description of the evolution of social differential entropy after the disaster; and (3) provides a correlation between measurable damage to cyber-physical infrastructure (CPI) and poorly measurable (intangible) social damage to the community. This work contributes to (1) accurate assessing the restoration of the resilience of a community exposed to hazards; and (2) competent socio-economic elimination of the consequences of urban accidents by the city decision-makers.

According to rail shipping commodity waybill data compiled by the Surface Transportation Board, the US freight railroads transported around 432 million tons of hazardous materials (hazmat) in 2020, which constituted 12% of the total commodity volume. Of the total hazmat tonnage, 54.8% were chemicals and allied products, 24% were petroleum and coal products, and 13.8% were crude petroleum, natural gas, and gasoline. Considering the large amount of hazmat transported by rail, weighting rail networks by hazmat tonnage can provide useful insights with multiple operational, economic, and safety implications to enable providing effective services to a growing population market needs. Therefore, population distribution can be an important factor in railroad analysis for safety reasons. In this study, the US freight railroads are represented by a network of links and nodes. Hazmat data and population data are aggregated to determine node weights. A connectedness efficiency model is created based on node weights and network topology to identify nodes and links, if disabled, that would have significant impacts on network connectedness efficiency. This study also enables the consideration of scenarios when a network is subjected to natural or human-made disasters, resulting in the loss of function of nodes or links. These results help determine key nodes and links for safety, commerce, and economic activity, and inform planning practices related to the safety and resilience of freight transportation.

Given the large number of aging dams in the Central and Eastern United States (CEUS), a risk framework is needed to characterize the seismic risk posed to a downstream community from a system of dams in the region. This study proposes a Bayesian network (BN) approach to system-level seismic risk assessment in the CEUS. A BN was previously developed to characterize the joint seismic hazard at multiple dams. This research extends that BN by characterizing the resulting potential seismic failure modes. The formulation of this scalable framework is discussed, and an illustrative example problem is used to illustrate how the BN can provide insights useful to risk-informed decision-making.

This paper presents three case studies of thrust restraint analyses using a recently developed analytical solution that is modified from the current approach from the AWWA M9 manual, including the effect of temperature and earthquake loads, as well as the effect of Poisson’s effect of internal pressure-induced circumferential strains on longitudinal stresses. These three case studies represent typical scenarios for a small diameter Glass Fiber Reinforced Plastic (GFRP) pipe in a power plant, a large diameter Prestressed Concrete Cylinder Pipe (PCCP) in a water transmission main, and a medium diameter steel pipe in a transcontinental natural gas transmission pipeline, respectively, when subjected to moderate to high internal pressures, temperature, and earthquake loads. The resulting demands on the pipe segments adjacent to the pipe bends are compared to the demands obtained from using linear superposition method to combine the demands from the current AWWA M9 manual and temperature and earthquake loads. The case studies indicate that the linear superposition method results in unconservative estimates of the stresses in the pipeline.

Faced with frequent and disastrous flood impacts, resistance to flood events is critical for bridge infrastructure in flood-prone areas. This paper presents a computational approach on evaluating flood impacts on bridge infrastructure in the Great Lakes region. By employing fluid-structural interaction (FSI) modeling, the study integrates computational fluid dynamics (CFD) and finite element analysis (FEA) to quantify the interaction between flood and bridge piers. Ansys Fluent, a CFD software, is utilized to simulate the flood wave and estimate the flow impact on bridge piers. The CFD results are then imported into FEA software to analyze the response of the bridge pier to floods. A parametric study is conducted to determine the critical factors, including the wave height, length, and speed on the bridge infrastructure. Based on the simulation results, an optimal pier design to enhance the flood resilience of bridge infrastructure in the Great Lakes region is proposed. The study provides a quantifiable computational model that assists the coastal communities and organizations in evaluating bridge infrastructure resilience, making pre-flood preparations, mitigating the long-term risk to life and property from future flood events, and enhancing overall infrastructure resilience.

This paper focuses on network resilience-based design, which involves carrying out a priori analysis during the design phase to optimize the topology of the current network to increase its resilience against disruptive events. However, solving this problem is computationally expensive as it requires searching through numerous topology structures and conducting multiple resilience analyses under different network topologies, which can become unaffordable for large-scale networks. To address this issue, we propose a new graph neural network that incorporates response flow characteristics to capture additional network features. This neural network is designed to deal with complex problems with high dimensions and nonlinear characterization, and it is integrated into an adaptive framework that combines it with a probabilistic solution discovery algorithm to solve network resilience design problems accurately and efficiently.

When wetted by heavy fog, light rain, or condensation, the contamination that accumulates over time on the surface of an insulator can form a conductive pathway that may result in flashover, consequences of which include insulator damage and unexpected power outages. Rain or manual washing can remove built-up contamination from an insulator’s surface, reducing the risk of contamination-induced flashover. However, regularly washing all insulators may be infeasible for electric power companies with large service territories. To inform long-term risk mitigation efforts, we propose a probabilistic method to assess the rate of contamination-induced insulator flashover using site-specific air quality model (AQM) and rainfall data. The model accounts for uncertainty in the amount of accumulated deposition on the surface of an insulator and in the flashover voltage of the contaminated insulator. We demonstrate one possible implementation of the method for two hypothetical insulators—one ceramic and one polymer—at an example site. The proposed method can also be adapted for short-term asset risk management.

The North American railroad system can be well represented by a network with 302,943 links (track segments) and 250,388 nodes (stations, junctions, and waypoints), and other points of interest based on publicly accessible geographical information obtained from the Bureau of Transportation Statistics (BTS) and the Federal Railroad Administration (FRA). From this large network, a slightly more consolidated subnetwork representing the major freight railroads and Amtrak was selected for analysis. Recent improvements in network and graph theory and improvements in all-pairs shortest path algorithms make it more feasible to process certain characteristics on large networks with reduced computation time and resources. The characteristics of networks at issue in this study to support network-level risk and resilience studies include node efficiency, node eccentricity, and other attributes derived from those measures, such as network arithmetic efficiency, network geometric central node, radius, and diameter, and some distribution measures of the node characteristics. Rail distance weighting factors, representing the length of each rail derived from BTS data, are mapped to corresponding links and are used as link weights for the purpose of computing shortest paths and subsequent characteristics. These network characteristics can be used to place resources and plan for natural hazard and disaster scenarios. They relate to many practical applications such as network efficiency to distribute traffic and a network’s ability to recover from disruptions.

Despite heavily depending on beachfront tourism, coastal real estate development in the Caribbean generally lacks sustainability and resilience. Conventional engineering approaches to address beach erosion and real estate vulnerability often focus on protecting specific properties without considering the surrounding coastal environment. There is also growing awareness that the Caribbean’s beach tourism industry is becoming increasingly vulnerable to the physical impacts of climate change and that new approaches are needed, as suggested by the industry’s growing demand for sustainable and resilient solutions. This paper proposes an alternative approach that accounts for coastal dynamics, incorporates ecological features, embraces the participation of the local community, and embeds climate adaptation in the engineering design. The paper provides examples of how these principles have been applied in beach restoration projects in the region and discusses how technical, regulatory, business, and operational constraints were managed. Ultimately, the paper argues that a comprehensive and integrated planning and design philosophy is necessary to ensure the long-term sustainability and resilience of the Caribbean’s beach tourism industry.

Infrastructural systems operate in increasingly dynamic environments that present a complexifying array of hazards. As legacy systems are further extended to respond to the demonstrated needs of urban dwellers, it becomes clear a conceptual deficit exists within graduate-level curricula for designing tomorrow’s resilient infrastructure solutions today. Resilient infrastructural solutions require a fundamental reconsideration of design requirements that is informed by original research and best explored first through project-based learning (PBL) design studios in academic departments including architecture, civil engineering, mechanical engineering, and urban design. How then ought interested infrastructural educators pivot their research attention and teaching assignments toward morphologically indeterminate infrastructural problem spaces, while positioning themselves as the conceptual leaders so desperately needed? This paper presents the direction of design studio curricula to infrastructural design problems, offers insight into how funded research in a hybridized infrastructural type can unlock new pedagogical opportunities, and shares pedagogical case histories for three graduate-level PBL design studios.

In this pressing time of climate change, civil infrastructure systems are increasingly challenged by more frequent and intensified natural disasters. Natural disasters cause damages to infrastructure systems, lead to system dysfunctionality and system topology change, and force system flows to redistribute. Without characterization and control, such flow redistribution can trigger the “domino effect,” causing the redistribution to cascade at the system level. To support the control of such cascading, this paper aims to characterize the flow redistribution, forced by disaster-induced infrastructure topology change, for civil infrastructure systems. The proposed characterization method includes two steps: (1) quantifying flow redistributions at the network link level, and (2) assessing the topological properties of each network link using graph theory. The quantified flow redistributions and assessed topological properties are linked to discover and characterize the behaviors and patterns of flow redistribution. The implementation of the proposed method in transportation infrastructure systems showed that (1) the flow redistribution behaviors and patterns varies across links with different topological properties, and (2) damaged links that are well-connected are more likely to have greater impacts on their neighboring links.

The paper is dedicated to application of artificial intelligence in the form of neural networks (ANN) to such critical infrastructure as urban water supply systems (WSS) that inherently are gray boxes as the information about their layout, structure, interconnection of its elements, their properties (and the properties of various loads and impacts on them) is incomplete and vague. For training the ANN, a dataset was used, consisting of 1,240 observations characterizing each pipeline of the Kamyshlov city WSS. The multilayer perceptron was chosen as the most suitable for modeling purposes because it had the highest performance at the stages of training, control, and testing. The convergence between the real and predicted values of the output parameters is quite satisfactory. As a result of the sensitivity analysis of the simulated ANN model, important conclusions about WSS reliability were obtained.

Environmental effects in strategic underground infrastructure, such as extreme water extraction, extreme floods, material deterioration due to water infiltration and ground movement under sustained and seismic loading, can significantly affect its short, medium, and long-term performance. Furthermore, these effects are critical in densely populated cities, where the overall safety of a particular structure can be jeopardized if those effects are ignored, and poor geotechnical conditions prevail, such as in Mexico City, where high plasticity and highly compressible clay is found. This paper presents a novel methodology based on a coupled field monitoring and numerical approach to assess the performance of strategic underground infrastructure under environmental effects. The method allows to establish, from a surrogate model derived from the analysis of several scenarios, an optimum range for the expected performance, establishing a lower and upper-efficiency curve as a function of the environmental parameters measured. The method is introduced through a case study corresponding to a 9.1 km long tunnel. Through the analysis of the results obtained, it was possible to evaluate the short to long-term behavior of a tunnel and eventually propose mitigation measures to enhance its expected performance.

This paper examines the differences in factors of social vulnerability and flooding risk between rural and urban areas in the United States. Using data from the FEMA National Risk Index, US Census Bureau, CDC, and BRIC Community Resilience index, we analyze the relationship between overall riverine flood risk, social vulnerability, and community resilience and statistically significant differences are reported. Our findings show that rural counties with a high risk of riverine flooding have higher percentages of vulnerable populations, such as those living in poverty, without a high school diploma, over 65 years old, with disabilities, and living in mobile homes, compared to their urban counterparts. The work presents a timely and significant contribution to the field and provides a comprehensive snapshot of the national differences in community risk associated with flooding between rural and urban areas in the United States.

This paper provides a case study involving the waterfront redevelopment of a decommissioned US Navy facility with a 1940s wharf repurposed as a waterfront pedestrian promenade and park. The Navy constructed the wharf in 1944 on the island of Alameda in the San Francisco Bay Area as part of the Naval Industrial Supply Center Facilities. They designed the wharf to support vessels, rail cars, cranes, trucks, and other loads associated with munitions movement and storage at a multimodal transportation hub. The Navy decommissioned the wharf in the 1990s, leaving the wharf as a lowly utilized waterfront industrial warehouse space. In 2017, a developer presented a plan to the City of Alameda to transform the nearly empty industrial wharf site and associated 40-acre lot into a mixed-use urban area combining residential and retail facilities with a public waterfront park. This paper uses this project as a case study to illustrate how existing waterfront infrastructure, even past its design life, can be repurposed into new spaces for public access.

Near-surface hurricane wind fields that interact with suburban coastal communities are often accompanied by storm surges that can radically transform the coastal landscape. With this transformation, the wind structure near the suburban canopy can undergo significant changes as the surge moves inland and flood levels increase. Current wind load provisions and standards (e.g., ASCE 7) neglect potential changes to surface roughness characteristics due to surge-driven flooding. To address this knowledge gap, this study attempts to numerically simulate surface roughness changes of suburban coastal communities resulting from hurricane-induced flooding. An initial series of steady Reynolds Averaged Navier–Stokes (RANS) simulations are carried out to systematically evaluate the impact of inland flooding on the near-surface mean wind structures for a representative suburban coastal community. Preliminary results indicate that coastal flooding can lead to distinctly different near-surface wind fields with the potential to increase the localized wind intensity and attenuate the flow structures in suburban communities.

This paper describes a sample of data-driven validation of the Hazus-MH combined hurricane and flood damage models. The validation was conducted based on a hindcast study of the impact by Hurricane Ian (2022) on buildings in Fort Myers Beach, FL. A comprehensive virtual damage assessment (VDA) study was conducted to estimate the first-floor elevation (FFE) and the overall damage state (DS) rating from DS0 (no damage) to DS6 (complete damage) for buildings. After comprehensive quality assurance and quality control (QAQC), the damage dataset was utilized to evaluate the uncertainty in model input. A Hazus-MH Level 2 analysis was carried out for two selected census blocks using coupled hurricane and flood models, and both the VDA and Hazus-MH damage state results were combined under one scale. The outcomes will be compared with the completed hindcast dataset, as well as those of other models, such as open-source Interdependent Networked Community Resilience Modeling Environment (IN-CORE) developed by the Center for Risk-Based Community Resilience Planning. Current results indicate the success of the VDA methodology and the potential for future model-to-data and model-to-model comparisons.

There has been increased interest in utilizing future climate projections to study the effects of climate change on pavement infrastructure. Statistical downscaling techniques from climatology research to have been applied to downscale atmosphere-ocean general circulation model (AOGCM) outputs to climate observations, therefore producing future climate projections at a spatial resolution appropriate for pavement applications. The research question was: Is there a difference in the predictive capacity between statistical downscaling procedures typically used for pavement applications? Subsequently, this research investigated this question using two procedures: the Asynchronous Regional Regression Model (ARRM) and the Delta Method. The statistical downscaling methods were used to produce future climate projections for daily maximum and minimum temperature 2 m above the ground surface for eight sites in the Seasonal Monitoring Program (SMP) of the Long-Term Pavement Performance Program (LTPP) across the United States. The resultant projections were truncated to 1 year target year period 45 years after the latest continuous year of SMP analysis site thermistor temperature data. Non-parametric two sample Kolmogorov–Smirnov (KS) testing was conducted to determine if the samples from the two methods represent the same underlying distribution on a site-by-site basis. There were few cases where the testing results indicated that the temperature samples were derived from the same distributions, therefore the conclusion was that the Delta Method is not interchangeable with the more complex ARRM procedure. This paper is a milestone to compare atmospheric temperature and precipitation future climate projections from different statistical downscaling methodologies to estimate temperature and moisture at depth through pavement structures for a larger population of SMP sites.

The need for smaller treatment facilities for more isolated or rural municipal entities and industrial water treatment facilities continues to grow. From environmental justice and energy needs in remote communities that have never been serviced by larger scale utilities to smaller onsite treatment requirements to meet discharge regulations, energy efficient treatment systems that can leverage renewable energy sources can be an important asset to owners. Climate change and population dynamics have changed the needs for distributed water treatment systems and the manner in which they function. New treatment technologies that can operate under dynamic conditions and leverage variable energy sources (e.g., renewable energy, RE) can provide substantial efficiencies and cost savings. The National Renewable Energy Laboratory (NREL) is embarking on research projects that will perform multi-scale studies to investigate treatment technologies on varying source water quality that can operate under unsteady state flow conditions (timing) in response to energy availability, supply, and demand. A pilot-scale system will be built to assess modularity and scaling of treatment system operating conditions and performance. Modeling will also be used to determine feasibility and integration into grid emulation platforms to understand the use and value of operation with RE. Optimization will be completed for the treatment and distribution systems to document strengths and weaknesses of using RE power sources. Results of this effort will advance dynamic water treatment understanding under variable conditions and integration of RE sources to power these systems. This will lead to further decarbonization of municipal and private treatment facilities while supporting a paradigm shift in water treatment process operation and system design to achieve net-zero goals and move toward energy-sensitive resilient water treatment systems.

Unethical behavior at work can be extremely costly, and unethical pro-organizational behavior poses a unique challenge. It suggests that there is a “dark side” to constructs thought to be productive, like organizational identification. Recent research suggests that individuals who identify highly with their organization are more likely to engage in unethical pro-organizational behavior and that moral disengagement mediates this relationship. Based on a sample of 281 professional engineers, this study suggests that organizational identification by itself does not predict unethical pro-organizational behavior. The study validates previous findings of a significant relationship between moral identity and moral disengagement and between moral disengagement and unethical pro-organizational behavior. It also provides the first empirical evidence of a significant relationship between work engagement and moral disengagement. While offering recommendations for workforce development to prevent unethical behavior among professional engineers, the study highlights the complexity of predicting and responding to the dark side of organizational identification.

With Justice40, the infrastructure bill, and other recent national policies, researchers and practitioners are exploring ways to approach community resilience through an equity lens. To operationalize a social equity-oriented paradigm, predictive tools and frameworks are needed to assess the uneven spatial distribution of disaster social impacts across a community. Arguing that schools are social institutions fulfilling a community’s social needs through educational services, this paper proposes a framework for measuring changes in accessibility to schools during and after disasters. The accessibility is quantified using a temporally based metric that evaluates access from four dimensions: proximity, availability, adequacy, and acceptability. The framework proxies the existing disparities among community members by estimating a social vulnerability value for each household. Considering accessibility and social vulnerability together, the framework enables resilience planners to forecast possible inequities in post-disaster accessibility to schools and address them in the community’s preparation, hazard mitigation, and disaster recovery plans.

Malaria is a vector-borne disease spread by the bites of female Anopheles mosquitoes. According to WHO, 247 million new cases of malaria worldwide were identified in 95 countries and territories only in 2021. There are an estimated 600,000 deaths caused by malaria worldwide each year. Thus, effective preventive measures are needed to control this threat. The geographic distribution of Anopheles mosquitoes at the community level is often lacking, and yet this knowledge is vital for malaria prevention efforts. Currently, field data sampling in remote rural areas is challenging due to accessibility issues and lack of people with skills to collect samples and report results. To this end, developing the methodology to collect information on larvae distribution in rural areas using simplified tools for the local population would be effective to predict and prevent the potential malaria outbreak. This study proposes a methodology for collecting larvae sample images using a smartphone microscope in collaboration with the local community in Lao People’s Democratic Republic (PDR) and briefly analyzes the Anopheles’ habitat and patient distribution for malaria case management by overlaying spatial maps. The methodology of collecting and identifying Anopheles larvae samples using a combination of smartphone microscopes and community-based sampling was verified. This methodology could be extended to other vector-borne diseases such as dengue, which requires quicker response against the outbreak. As dengue tends to spread in urban areas, the methodology for risk mapping could be improved by incorporating other data such as geo-tagged social media information. This study shows how integrating community-based data and information with other geospatial data can effectively work to predict and prevent the outbreak of communal disease.

Sea-level rise exacerbates the risk of road disruption on accessibility to facilities such as fire stations, which impacts vulnerable neighborhoods disproportionately. To address this issue, we propose an integrated framework that considers the physical and social vulnerabilities of communities to better understand critical facility accessibility in the face of flooding. Using fire stations in Delaware as a case study, we estimated the travel time of different neighborhoods that represented nodes in the network under different flood disruption scenarios, including the closest fire station and the station assigned based on coverage area. The probabilistic flooding scenarios are generated based on FEMA flood risk maps, and a Monte Carlo approach is adopted to simulate and measure reachability and travel time. The proposed metric will inform the facility siting and service boundary design to improve accessibility to fire stations for equitable flood risk management.

The Port of San Francisco is an enterprise agency within the city and county of San Francisco with an annual budget of approximately $110M. The Port of San Francisco manages 7.5 mi of waterfront and is home to approximately 35 over-water facilities, including popular destinations and attractions, historic districts, small businesses, and maritime opportunities. Recent changes in how the Port contracts for construction and professional services have had positive impacts on social justice issues such as diversity, equity, and inclusion. The changes in contracting practice are in conformance with city, state, and federal regulations and provide opportunities for smaller, local business entities (LBEs) to participate in Port projects. This paper provides some examples of how changes to the Port’s contracting practices can provide additional benefits to its stakeholder communities (i.e., tenants, contractors, professional service providers, and neighboring communities).

The Biden–Harris Administration’s decarbonization targets require strategic planning, coordination, and accelerated development of a nation-wide CO₂ transport infrastructure to support carbon capture, utilization, and storage (CCUS) and carbon dioxide removal (CDR) projects. Under the Infrastructure Investment and Jobs Act, Pub. L 117-58 (November 15, 2021), also known as the Bipartisan Infrastructure Law (BIL), and the Inflation Reduction Act, Pub. L 117-169 (August 16, 2022), the US Department of Energy (DOE) Office of Fossil Energy and Carbon Management (FECM) is supporting the development of multi-modal CO₂ transport options that consider societal impact and community engagement needs. To date, DOE FECM-sponsored workshops have identified CO₂ transportation research areas and recommendations related to CO₂ Freight Transport, New and Existing Infrastructure, and a Roadmap for CO₂ Transport Applied Research. Activities under the BIL include front-end engineering and design studies for CO₂ transport infrastructure, infrastructure deployment, and repurposing through the Carbon Dioxide Transportation Infrastructure Finance and Innovation Program, and investment in upsizing future infrastructure through the Future Growth Grants program. Capturing and leveraging lessons learned from CCUS projects and interagency and international collaboration efforts is desired to efficiently implement a nation-wide CO₂ transport infrastructure.

A large portion of global energy consumption and carbon emissions stems from the building operation and initial construction. Due to the operational-embodied energy and carbon emissions-energy trade-offs, and limited comprehensive software, developing an energy-efficient and environment-friendly building design necessitates a thorough yet automated and integrated framework. This study proposes a multi-phase and multi-objective genetic-based framework. The framework involves an add-in tool that automatically creates the multi-zone energy model of the building and then couples EnergyPlus to the embodied energy and carbon databases through a Python script to optimize the building in terms of energy and carbon emissions. The proposed framework is applied to a medium office building that is optimized in two phases under the College Station weather conditions. In the first phase of optimization, the operational and embodied energy is reduced by 33.26% and 50.93% from the baseline model, respectively. The second phase optimization decreased operational energy, embodied energy, operational carbon, and embodied carbon by 31.54%, 52.5%, 32.89%, and 51% from the baseline, respectively.

With the country facing a shortage of affordable homes, manufactured housing is an option offering significant cost savings over traditional homes. In addition, manufactured housing has the potential to lower the environmental impacts, in particular, the embodied carbon of residential buildings. Consequently, investigating the environmental performance of manufactured housing is significant and important. This study aims to evaluate the environmental impacts of a manufactured house as a case study and recommend strategies and methods to reduce the environmental impacts. This study utilizes the life-cycle assessment (LCA) method with a cradle-to-site scope of analysis to evaluate the environmental impacts of a manufactured house. The LCA model includes material production processes (A1/A2/A3) and construction process (A4/A5) according to EN 15978 and ISO 14040 LCA frameworks. The results find that the initial emissions related to the building materials have a significant impact on total emissions, and using low-carbon and materials with a low transportation burden to reduce the carbon emissions of manufactured housing is recommended.

Coastal communities are highly vulnerable to climate-change associated flood hazards. While hard-engineering solutions (i.e., gray infrastructure) have been widely used to address hazards posed by climate change, demand for natural and nature-based features (NNBFs) as flood risk management alternatives has grown more recently. However, life-cycle analysis frameworks used in traditional planning processes limit comparisons between gray infrastructure and NNBFs by failing to account for non-economic costs and benefits, such as environmental impacts or co-benefits. This study proposes the use of an expanded life-cycle analysis framework that better accounts for traditionally unaccounted benefits and costs. We apply the framework to assess the life-cycle needs of a gray infrastructure project alternative and those of a NNBF alternative to demonstrate how flood risk management alternatives can be compared more effectively and equitably. We leverage dredging and placement cost, post-placement monitoring, and modeling data from the Swan Island Restoration Project, which utilized sediment dredged from nearby navigation channels to restore a highly erosive island in the Maryland portion of the Chesapeake Bay, to describe the application of a life-cycle analysis framework.

The construction industry is pivotal in leading the global community toward alleviating carbon emissions. Previous research is focused on measuring carbon emissions, developing rating systems, replacing them with sustainable construction materials, and evaluating life-cycle costs. However, fewer studies talk about the importance of carbon emissions in the planning and designing phase of construction projects. This paper aims to assess the impact of procurement and the role of project delivery methods in carbon emissions reduction in the public construction sector. To achieve this research objective, authors have conducted semi-structured interviews of eight major stakeholders involved in the procurement process in public construction sectors in addition to a thorough literature review. The results revealed that project delivery methods with early stakeholder involvement have a positive impact on carbon emissions reduction compared to conventional delivery methods. The findings offer valuable insights for policymakers regarding the feasible approaches to achieve carbon emissions reduction. The results also shed light on the potential application of artificial intelligence (AI) in the construction industry, which can be further explored in future research.

The analysis of wind flow around buildings plays a significant role in terms of evaluating wind energy, pollutant dispersion, energy efficiency, and energy management. Careful consideration of the turbulence model is critical in the simulation since diverse Reynolds-averaged Navier-Stokes (RANS) models have varying potential and accuracy in every part of the computational domain. In the present study, a computational fluid dynamics (CFD) simulation of an isolated building in a wind tunnel is conducted using three different turbulence models, namely standard k–ε, SST k-ω, and k–ε realizable. The simulation results are validated by comparing them with experimental data obtained in multiple positions on the rooftop. Additionally, the characteristics of turbulence models in the backward region are investigated. The results revealed that the SST k-ω turbulence model has better performance on the top of the buildings.

Engineers are at the forefront of change. Presently we are working in a large, unstructured environment, characterized by uncertainty. We are presented with a great opportunity for us to facilitate the development of the frameworks, systems, and standards that today’s engineering practices require. We must cultivate engineers in our industry to go beyond, to ask the difficult questions, and to be leaders, wherever they are in their careers.

Urban mass rapid transit (MRT) systems are genuinely considered green infrastructure projects due to their inherited characteristics of reducing greenhouse gas (GHG) emissions by decreasing car dependence. In 2021, developing economies were responsible for 82% of megacities worldwide, and a quarter of them still do not have an MRT system. This paper examines the economic, social, and environmental impacts of metros in seven megacities located in different sub-regions of four continents: South America, Asia, Africa, and Europe. Data from elevated, underground, and surface-level metro lines in Shanghai, Tehran, Istanbul, Sao Paulo, Bangkok, Mexico City, and Cairo was gathered through detailed and in-depth data collection involving reports, peer-reviewed papers, and official data over the projects’ lifetime, reveals the need of investing in sustainable MRT systems in developing countries, and provides a framework to reach sustainability and carbon reduction targets by 2050.

The 2008 Category 2 Hurricane Ike event caused significant damage to appurtenance structures of three condominiums that lie approximately 2.5 mi to the southwest of the SW end of the infamous Galveston, Texas, seawall. This hurricane washed away the adjoining decks and swimming pools, undermined the shallow foundation, scoured away soils, and exposed the deep-piled foundations. The 2020 Category 4 Hurricanes Laura and Delta, which struck southwest Louisiana, caused further and accelerated the erosion of the beach fronting the condominiums and undermined the shallow foundations underneath the condominiums, exposing more of the deep-piled foundations. The 2021 collapse of the Champlain Towers South at Surfside, Florida, was the catalyst for the city of Galveston to address the conditions at these three condominiums. The homeowners associations for the condominiums hired one civil/structural engineer to assess the conditions. He deemed them unsafe per the 2018 International Existing Building Code. The city of Galveston moved to condemn them. Due to his lack of experience with ocean and coastal engineering, the HOAs retained Mark E. Haas, P.E., with greater than 30 years of coastal, offshore, and subsea engineering experience, to assess the condominiums. Mr. Haas, P.E., implemented a LiDAR assessment of the condominiums and showed that the deflections of the condominiums were well within the tolerances for construction and for operability per the appropriate governing codes. With these results, the city of Galveston delayed its condemnation orders to allow time for the construction of a fronting bulkhead and a thorough engineering analysis of the buildings. The construction of the bulkhead would be on the property of the state of Texas. Many structural and other engineered drawings for the three condominiums were lost due to the flooding of the offices during the 2008 Hurricane Ike event. With existing and re-created IFC structural drawings, the condominiums were re-analyzed for Dead, Live, and Wind Loads per ASCE 7-16 and analyzed for Dead, Live, Wind, Flooding, and Wave Loads, also per ASCE 7-16. Currently, two condominiums have been deemed safe per 2018 IEBC standards. The third condominium may yet face condemnation by the city of Galveston, as its final engineering review has not started at the time of the submission of this paper. The final review should be concluded before the November 2023 presentation at the 2023 ASCE Inspire Conference.

As a crucial component of water infrastructure, filtration is an imperative step in water treatment, removing particles to achieve targeted turbidity. Regular inspections of water filters are necessary to identify irregular, curved, or misaligned sections of gravel support and pipes within the filter. These geometric defects can lead to uneven water flow through the filtration layers, resulting in water quality that fails to meet established standards. Traditional filter inspection techniques involve puncturing or excavating the upper layers, can be time-consuming, and may necessitate plant shutdowns, negatively impacting operational efficiency. Aiming at addressing this issue, the authors used 3D laser scanning and time-series sensor data analysis for non-contact inspections, reducing time, costs, and errors associated with conventional field punctures of filtration media. The point clouds and time-series sensor data from six water filters before and after backwash were collected. The authors identified correlations between anomalous geometric change patterns on the surface and hidden geometric defects. Additionally, geometric information from point clouds and sensor data could be mutually interpreted, and uneven backwash processes and water flows caused by subsurface defects can produce irregular 3D geometric changes on the top surface and abnormal sensor data. Filter 2 exhibits higher surface elevations (a bump on one side of the filter surface) than the other five filters. According to the sensory time series analysis, the average production between each backwash of filter 2 is lower than that of the other five filters, Consequently, filter 2 is diagnosed as the outlier among the six filters.

Natural rubber latex (NRL), obtained from Hevea brasiliensis trees, is a biopolymer with excellent binding properties. Additionally, solidified rubber has some exciting characteristics, such as high elongation capacity, tensile strength, resilience, film formation, etc. Being extracted from a renewable natural resource, NRL is expected to be a sustainable, environmentally harmless material. The proposed research utilizes natural rubber latex for treating sand to improve its dynamic behavior. NRL treatment of ready-prepared sand specimens was done by permeating NRL solutions into sand specimens with the help of a fluid permeation setup. The dynamic response of NRL-treated sand was evaluated by performing strain-controlled cyclic triaxial tests. The pore pressure development and cyclic degradation of shear modulus were accessed for comparing the response of treated and untreated specimens. The results show that the NRL treatment helps to reduce the excess pore pressure development and cyclic degradation of shear modulus in the sand.

In this study, a numerical iteration method is proposed to analyze the behavior of tieback soldier pile walls sustaining complex external loads. The proposed iteration method integrates the analysis features of the common engineering analysis software LPILE and SAP2000, which is a refined 2D analysis approach and saves the effort of performing the complicated 3D finite element analysis or the cost of purchasing costly advanced analysis tools. Based on the case study on the soldier pile tieback retaining wall from the VDOT I-66 Expressway project, it is observed that the proposed numerical analysis method provides accurate predictions of the deflection, shear, and bending moment results for the selected tieback soldier pile wall, and the iteration process converges quickly. This proposed numerical iteration method is also expected to be applicable for modeling soil-structure interactions of piles and shafts for integral-abutment bridges, long-span bridge piers, and other complex civil infrastructure, etc.

Electric and gas investor-owned utilities operate in a regulated environment and are scrutinized by media and stakeholders for key strategic and operational decisions. Some decisions entail significant risk requiring a special attention to risk tolerances and attitudes. Utilities typically institute enterprise risk management programs to efficiently and effectively manage safety, reliability, and financial risks for their customers, employees, and communities in a changing climate with intensifying risks, such as wildfire. Consequences from such events could include human life and property losses, health effects, environmental damage, service loss, and other indirect financial and economic impacts. A spectrum of risk quantification and management methods are available for assessing these hazards. Varying risk tolerances and attitudes of stakeholders, typically ranging from neutrality to risk aversion, create situations that are central to decision-making where the safety, service delivery reliability, rate affordability, and the financial well-being of entities come together in a complex manner. This paper sets context, defines key terms, and develops an innovative approach for methodically reflecting risk tolerance and attitude in informing risk management decisions by offering flexibility to account for preferences by stakeholders in a structured manner. The proposed methods are illustrated in the context of wildfire risk management, including calibration and validation approaches.

Since 2014, the US Army Corps of Engineers (USACE) has been engaged in a multi-agency research and development effort called Forecast-Informed Reservoir Operations (FIRO), which is exploring how advances in weather prediction and observations can inform operations and policy decisions at federal, state, and local water management agencies with the collaborative engagement and support of researchers, engineers, operators, and stakeholders. FIRO represents a new phase of water management for USACE wherein forecast information can now be used for planning future operations, a significant change from the decades-long policy of operating solely to “water on the ground.” Phase I of FIRO developed a process for assessing the FIRO viability, and Phase II explored transferability of viability assessments to additional pilot sites. The FIRO R&D effort has now entered Phase III, the National Expansion Pathfinder, and over the next five years, will consist of a national forecast skill assessment and improvement campaign, completing ongoing and performing new viability assessments on individual as well as systems of reservoirs, and applying the FIRO screening process to the USACE portfolio of dams to prioritize future FIRO viability assessment efforts. Lessons learned from FIRO Phases I and II will be presented along with plans for how Phase III will be carried out over the next five years.

Any type of break or any significant amount of leak after seismic events can reduce the serviceability of a water network. Proactive seismic rehabilitation decision-making is essential to reduce the losses and increase the serviceability of a water pipe network after a seismic event. Although water pipelines are vulnerable to permanent ground deformation (PGD) due to earthquake-induced geotechnical instability (landslide and liquefaction hazards) in susceptible zones, a combination of damage due to earthquake-induced transient strains and geotechnical instability (landslide and liquefaction) has not been considered at network level in existing proactive seismic rehabilitation decision-making models of water pipe networks. This research presents a stochastic combinatorial optimization algorithm to determine the most critical pipes of a water pipe network vulnerable to seismic events considering a combination of PGD (damages due to landslide and liquefaction in susceptible zones), PGV (peak ground velocity), and PGA (peak ground acceleration) (damages due to induced transient strains) for a certain budget constraint. Once the earthquake characteristics (e.g., magnitude, epicenter) were identified using deaggregation analysis, PGA and PGV were calculated at the location of each pipe using the ground motion prediction equation (GMPE). The value of PGA and PGV was used to study landslide and liquefaction hazards providing a hybrid empirical-mechanical-based estimation of PGD. A three-phase geotechnical approach was used both for landslide and liquefaction hazards, representing (1) susceptibility analysis, (2) triggering analysis, and (3) estimation of PGD. The stochastic optimization problem was solved using a simulated annealing-based optimization algorithm. Modena network, a benchmark network, was assumed as the test network for the application of the methodology. The approach was verified by comparing the results with an existing method where the value of PGD was assumed to be zero. The comparative study results showed that the inclusion of landslide and liquefaction hazards significantly affects the identified critical pipes of the water pipelines. Also, the maximum achievable serviceability index for the selected rehabilitation budget was reduced significantly if landslide and liquefaction hazards were considered. The proposed methodology is a novel approach that considers a combination of earthquake-induced geotechnical instability and damages due to earthquake-induced transient strain for network-level proactive seismic rehabilitation decision-making.

Climate change is causing more intense and frequent extreme weather events, such as heavy rain, which can damage vital transportation infrastructure like roads, railways, and bridges. Therefore, it is essential to design and construct transportation infrastructure that can withstand current and future climate-driven flood frequency and amplitude increases. Flood detection using remote sensing approaches like Synthetic Aperture Radar (SAR) can help in monitoring natural hazards such as floods. In this study, the authors investigate the use of Sentinel-1 SAR imagery as a powerful tool for flood detection and monitoring transportation infrastructure. The study combines InSAR coherence and RGB composition methods to visualize multi-temporal changes and detect changes on the terrain surface via a temporal color image. The results were used to monitor Little River Watershed (LRW) flood events in central Oklahoma, and a Support Vector Machine classifier was used to extract flooded areas in the false RGB images. Overall, the proposed method showed promising results in accurately detecting flooded areas and can be useful for monitoring natural hazards and transportation infrastructure.

Critical transport infrastructure systems play a pivotal role in supporting society through provision of essential connectivity services to end users, alongside enabling economic growth. To be able to deal with an increasingly complex and interconnected world and uncertain future, critical transport systems need to be made more resilient. Being able to preserve critical services in ordinary as well as extraordinary circumstances is the resilience value ultimately delivered to customers. This paper argues that to be successfully implemented, resilience needs to be built/enhanced/embedded across the whole infrastructure lifecycle. It presents a summary of international research undertaken to identify tangible, practical, and relevant actions; tools; approaches; physical measures; and useful resources that decision-makers and practitioners can turn to when looking to implement infrastructure resilience. Finally, it is argued that a value-driven approach focusing on the resilience value delivered by infrastructure is needed to build the business case for resilience implementation.

The Earth is absorbing greenhouse gas (GHG) emissions driven by the combination of population growth and human activities. Construction and related activities are the world’s largest energy-consuming and polluting human activities and are wreaking havoc on the environment. Construction material usage or production produces a significant amount of carbon and consumes a large volume of water. The evaluation and calculation of environmental impacts in the construction industry are always challenging for every building with various functions, whether during or after the construction phase, during the operation phase, or at the time of demolition. This study intends to determine the carbon footprint of residential buildings in Mazandaran Province, Iran, from construction to operation of concrete structures. Therefore, a building was selected as a case study of concrete structures in Babolsar, Iran. Material consumption in the building was estimated using quantity take-off and estimation methods. The carbon footprint of different materials’ weight per unit was extracted from authentic references, and the total footprint was calculated. By dividing this value by the floor area of the building, carbon produced per surface area unit was calculated. According to the calculations, the carbon footprint for structure framing materials in a concrete building was 464 kg-CO₂eq/m² and 18.5 kg-CO₂eq/m²/year. The carbon footprint for building operations was determined using DesignBuilder software to calculate the annual energy-related kg-CO₂eq/m² production. Results show that 69 kg-CO₂eq/m²/year of carbon was produced during one year through energy consumption in the case study.