Journal of Infrastructure Systems

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal.

Unmanned aerial vehicles (UAV), or drones, have become popular tools for practitioners and researchers alike. Recent years have seen a significant increase in UAV uses for many applications in the fields of science and engineering. A broad array of research development in UAVs has been reported in the literature. This paper provides a summary review of efforts related to UAV development with a focus on civil infrastructure applications. First, guidance is provided for researchers looking to newly incorporate UAVs into their research efforts. The advantages and disadvantages between different UAV types are outlined and performance characteristics discussed. Examples of different sensor payloads that demonstrate expanded functionality are provided. The review also provides an overview of research efforts in the emerging domain of wireless sensor networks and data processing algorithms specific to UAV-collected data. Highlights of recent achievements of UAVs in post-disaster reconnaissance, infrastructure component monitoring, geotechnical engineering, and construction management are presented. Lessons learned from UAV implementation and considerations for good practice are also discussed. The paper concludes with a discussion of the emerging and future research domains that address the most pressing knowledge gaps in current practice.

There is an increasing need for the reduction of greenhouse gas (GHG) emissions within pavement management decision-making. Pavement maintenance activities, such as resurfacing, account for millions of tons of the GHG emissions annually in the United States. Optimizing pavement resurfacing activities allows for the potential to reduce the carbon footprint of pavement maintenance. A framework is proposed for estimating the relationship between GHG emissions from pavement resurfacing activities and pavement cracking-threshold policies, where cracking is the trigger distress. Cracking threshold is the maximum percentage cracking level a pavement is allowed to reach before an asphalt overlay is applied. The data set used in the case study was obtained from the Washington State Department of Transportation. The results show that for a planning horizon of 10 years, the optimal cracking thresholds for minimizing costs and GHG emissions are very close to each other.

Reestablishment of housing is a crucial component of the recovery process and has a domino effect on the overall timing of recovery. Anchors of social networks, such as schools and churches, on the other hand, are perceived to be influential in housing recovery decisions. This study provides a model for indexing households’ anchors of social network awareness based on publicly available data. This model uses individual-level data to develop a county-level index of anchors of social network awareness. This allows devising recovery strategies that are tailored to the needs of residents within a given county. Data were collected through an internet survey targeting New York and Louisiana, which were highly impacted by Hurricanes Sandy and Katrina. The survey asked participants to draw a polygon around their perceived neighborhood area in Google Maps. Then, follow-up questions were asked to identify key anchors driving this perception. Latent class analysis (LCA) and regression revealed the existence of multiple latent classes, each corresponding to a certain demographic and socioeconomic group. Finally, a county-level index of anchors of social network awareness was developed using individual-level latent classes. This index can be used by policyholders as a decision support tool for prioritizing anchors that are deemed to be important in a given county for receiving recovery assistance, which can then lead to a more enhanced recovery.

This paper addressed an important question of selection of wheel path(s) for International Roughness Index (IRI) or Mean Roughness Index (MRI) estimation. Twenty-one IRI/MRI alternatives of wheel path position(s) (single path and two parallel paths) were estimated. Sixteen parallel records of longitudinal profiles and a road network with a total length of approximately 12,000 km were processed. The correlation between simulated vehicle vibration response and IRI/MRI alternatives was evaluated. Vibration response was calculated on a passenger full-car model. The influence of vibration response category (ride comfort and vehicle controllability and safety), road category (motorways, first-class roads, and second-class roads), and report interval of IRI (20 and 100 m) on the results was analyzed. The best correlation was established for parallel profiles that were in the lateral distance of 15–30 cm of vehicle excitation profiles. A stronger correlation was identified for single path near the right (outer) wheel path for motorways. A weaker correlation was identified for the left (inner) wheel path or two parallel profiles localized further than 30 cm from excitation profiles. The same correlation coefficients were calculated for most of the IRI/MRI alternatives and the 100-m report interval of IRI.

Abutting land-use patterns always play an important role in the performance of a road section. Land use along rural highways in developing countries is significantly different from that in developed countries. Existence of side friction in rural highways is a common characteristic in developing countries like India. In India, substantial investment has been made to improve the performance of various categories of roads. Widening and strengthening projects have been taken up and condition of road surfaces has been improved, but travel speeds and service levels have not been improved as expected because of prevailing land-use patterns. One important factor has been the existence of side friction along rural roads in India. Roadside markets are generally found at a frequent intervals along rural highways. Interactions among fast moving vehicles, pedestrians, and slow-moving local vehicles occur in a unique and random manner. The objective of the present work is to quantify the impact of roadside friction generated by roadside markets on travel speed, capacity, and level of service (LOS) of rural highways in India. A roadside friction index (RSFI) has been developed to quantify side friction. Using data collected from three study sections, speed-flow curves were developed for various side friction levels. The k-mean cluster analysis algorithm was applied for determining threshold values for LOS. Five threshold limits for LOS are recommended for a stable flow zone considering percentage speed reduction (PSR) and volume:capacity ($\mathrm{V}:\mathrm{C}$) ratio as measure of effectiveness. Impacts of side friction on the capacity of rural highway are also investigated.

Value-for-money (VFM) drivers and their interrelationships have large impacts on increasing the VFM of public–private partnership (PPP) projects. However, very few studies have explored the potential interrelationships among VFM drivers. Using a questionnaire survey and structural equation modeling (SEM), the current study aims (1) to refine a VFM driver framework for PPP projects; and (2) to determine and verify the interrelationships among VFM drivers for PPP project implementation. This research has observed that the determinants of cost and effectiveness are provided for financial or performance sustainability. Moreover the better the overall cooperative environment, the better the participants’ capabilities and characteristics and the more effective the cooperation between the public and private sectors. Furthermore, the level of cooperation favorably influences cost and effectiveness directly. Finally, a stable macroeconomic condition has the most significant impact on the cooperative environment. The determined interrelationships among VFM drivers will contribute to the increased sustainability and successful delivery of PPP projects.

This research identified factors influencing the accuracy and variability of pavement surface evaluations and ratings (PASER). Survey and PASER data obtained from workshop participants were used to develop statistical models estimating three definitions of rater accuracy: (1) bias, which is the absolute value of the difference between a participant’s rating and the segment’s true rating as determined and agreed upon by PASER-certified instructors; (2) average participant bias and bias standard deviation; and (3) good/fair/poor category identification. The results indicate that pavement in good condition was rated more accurately, while pavement on the poor/fair condition boundary was rated less accurately. Participants were more accurate in assigning PASER ratings after receiving PASER-specific training. Additionally, raters that were more accurate were also more consistent in performing PASER ratings. Participants with engineering roles, such as engineer, engineer technician, or engineer assistant, were more accurate in assigning PASER ratings. In contrast, participants with leadership roles, such as supervisor, manager/foreman, or team leader/elected official, or less than 1 year of PASER rating experience were less accurate in assigning PASER ratings.

The replacement of deteriorating distribution pipes is an important process for water utilities. It helps reduce capital spending on water main breaks and improves customer satisfaction. To assist with the development of an effective renewal plan, statistical models that forecast future breakage rates have been used to guide planning for asset management. However, this process is difficult for older utilities that lack readily available pipe network data. We examined whether accurate and useful predictive models can be built in the absence of pipe-feature data. Using the historical break record from a mid-Atlantic utility, two data sets at different spatial scales were created using publicly available demographic and environmental information. Empirical results suggest that although accuracy suffers from the lack of pipe-level details, it is still possible to create a model that provides useful information for prioritization of high-risk regions for management.

A novel approach and program are developed for deterioration and predictive modeling of concrete bridge decks based on nondestructive evaluation (NDE) data. Through an iterative process—combined with data processing, bridge deck segmentation, regression analysis, data integration, and deterioration and predictive modeling—the developed program aids estimates of the remaining service life of bridge decks. Data collected on an actual bridge deck during a period of five and half years are used to illustrate the operation and performance of the developed program. Based on evaluation of condition maps, condition indices, and deterioration curves developed for a range of input parameters, the proposed method quantifies progression of deterioration in bridge deck. By reviewing the predictive models, combined with segmentation of the bridge deck area, a more realistic and practical estimation of the deck’s remaining service life can be made. It is anticipated that the proposed method will provide objective and comprehensive evaluation and prediction of bridge deck condition based on data from multiple NDE technologies.

A fast recovery of infrastructure functioning is important to the well-being of residents and the economy following interruption or disaster. Assessing the chances of recovery, or creating plans to enable it, is difficult due to the many interactions between components and operations. From a modeling perspective, addressing this challenging problem requires a capability for constructing representations of the dynamic interactions between elements that addresses how hazard and failure effects cascade and how recovery efforts propagate. Here, a geospatial resilience assessment platform is proposed containing a modeling approach comprising the capabilities necessary to address the challenge of urban infrastructure and operation recovery assessment and planning. It is designed to be reusable and integrate with reusable damage assessment tools such as Hazus. The approach combines a novel means to construct geospatial dependency models that can assess element-by-element recovery over time through integration with a computational recovery assessment engine called the graph model for operational resilience. A sample model and assessment that illustrates recovery time assessment of infrastructure services to the buildings of a neighborhood subject to varying infrastructure failures are provided. The case provides indications of the degree of burden on emergency management sustainment resources that may exist and how risk treatments can improve recovery times. In particular, the impact of the order of component recovery is examined in a multi-infrastructure setting.

Community resilience to natural disasters depends to a large extent on the adequacy of infrastructure services provided through multidimensional interdependent systems during postdisaster recovery. In the aftermath of a disaster, enormous and diverse community recovery needs emerge, necessitating an effective interplay of cyber-physical-social systems during emergencies and throughout the phases of short- and long-term recovery. With recognition of the important roles played by multidimensional infrastructure, considerable research and development has been undertaken in each discipline to support communities in the aftermath of disasters. These multiple domains of infrastructure systems are heterogeneous in their structure and operation but are nevertheless interrelated. Therefore, the absence of an integrated platform on which emergency personnel from different disciplines could coordinate resilience planning often hampers the smooth and effective recovery process of communities during postdisaster recovery. This study proposes a new approach for analyzing capacity needs of a wide range of critical infrastructure during postdisaster recovery, framed within seven interrelated infrastructure layers: civil, civic, social, financial, environmental, educational, and cyber/communication. This research employs node analysis to deal with the complexity of the infrastructure layer networks and visualize the interplay among them. The proposed seven-layer framework facilitates an understanding of the interdependencies within and between the infrastructure layers, and an analysis of various communities’ needs specific to the phases of postdisaster recovery. Finally, the implementation and the benefits of this seven-layer conceptualization are demonstrated through case studies of past postdisaster recovery operations.

In this paper, we consider a problem in which the decision maker only has access to outage reports from service receivers in critical infrastructures with dependencies among them, and our objective is to determine inspection plans to identify the damaged components that lead to the reported outages. This situation could occur, for example, if a cyberattack on the human-machine interface of the infrastructures occurs at the same time a natural hazard damages physical components. The demand nodes within these infrastructure networks may have an outage either from damage that occurred within their infrastructure or due to cascading failures across infrastructures. We present network interdiction-based approaches to identify a set of components to inspect that replicates the service outages reported across the infrastructures. These approaches can be applied in an iterative nature where a particular approach first identifies a set of components across infrastructures to inspect, and then, based on the feedback received from the inspections, determines an updated set of components to inspect. Performance criteria of the proposed approaches are the percentage of successful inspections, which represents the percentage of inspection resources used to successfully identify damaged components. As a benchmark, we applied a breadth-first search (BFS) approach to the problem, which mimics a top-down inspection policy for identifying damaged components within an infrastructure. We tested our approaches on realistic infrastructure systems and demonstrated that they far outperform the BFS approach.

Computer vision methods have the potential to detect rust defects in steel components of bridges. However, direct use of images collected by aerial means to identify such defects is currently difficult because of obstructions caused by other objects in the image field of view. In this context, an automated rust-defect-determination method that leverages aerial imagery, including both visible and infrared images, is presented in this investigation. The proposed method consists of three steps. The first step deals with image registration for which a binary information method is proposed to match the infrared images to their visible counterparts. In the second step, bridge components are retrieved from the captured images via automated segmentation obtained by fusion of visible and infrared images. Finally, rusted regions are identified in YCbCr colorspace, and a rust percentage is calculated. Experimental results obtained by aerial images collected on a real operating structure demonstrate that the proposed methodology can directly use the original captured images and can be successfully applied to real-world scenarios.

Nondestructive evaluation (NDE) can provide invaluable information about the condition of tendons in post-tensioned and cable-stayed bridges, which can facilitate preventative actions to mitigate further deterioration. To date, the use of nondestructive testing methods for routine maintenance of bridges has been relatively low, partly due to limitations of available technologies but mainly owing to a lack of know-how regarding existing NDE techniques and guidelines. This study aims to develop guidelines to assist bridge owners and engineers in selecting the most appropriate NDE method or combination of methods for assessing the condition of in-service post-tensioning and stay cable systems. An extensive experimental study is conducted on a full-scale post-tensioned girder specimen and four large-scale stay cable specimens to evaluate the performance of various NDE methods in detecting location and severity of numerous strand and grout defects. Decision metrics are developed considering multiple criteria such as precision, accuracy, ease of use, inspection requirements, and cost. The decision metrics are intended to facilitate selecting the most appropriate NDE method for assessing a particular defect condition. Overall scores for the tested NDE methods in identifying various defect conditions in different parts of the post-tensioning and stay cable systems are calculated using a weighted sum model. These scores are used to develop a list of ranked NDE methods for evaluating a particular defect condition within a specific location and for a specific duct material.

Seismic resilience of civil infrastructure systems is an essential property of modern communities. In this paper, an agent-based modeling framework to evaluate the seismic resilience of an integrated system consisting of the community and its civil infrastructure systems is proposed. Specifically, an agent-based model of the recovery process of civil infrastructure systems is incorporated into a previously developed compositional supply/demand seismic-resilience quantification framework. The proposed model represents the behavior of the operators of civil infrastructure systems as they strive to recover their functionality in the aftermath of an earthquake as well as their mutual interactions and interactions with the community to which they provide services. A case study of the seismic resilience of a virtual system consisting of electric power supply system, transportation system, and the community (EPSS–TS–community system) is conducted using the proposed framework. A parametric investigation is carried out to examine the effect of different earthquake magnitude scenarios as well as different behaviors of the involved agents and their interaction on the seismic resilience of the EPSS–TS–community system. It was demonstrated that the proposed agent-based modeling approach is effective in representing the interactions among different participants in the recovery process. It was also revealed that timely and well-planned intervention in the recovery process can be very effective in alleviating the postearthquake lack of resilience resulting from the insufficient supply of civil infrastructure services to meet the community’s demands. Therefore, the proposed framework could be employed to formulate the recovery trajectory of the intertwined sociotechnical system subjected to different earthquake scenarios. The interplay among different agents, as well as the interdependence among civil infrastructure systems, is found to profoundly shape the recovery path for this integrated EPSS–TS–community system.

This paper demonstrates how a combination of qualitative and quantitative approaches following the scenario planning methodology can be used to identify driving forces (drivers) that might influence the future of the transportation system over a 30-year period. A case study of the highway transportation industry in the United States is presented. A causal loop diagram and cross-impact matrix were combined to understand the interactions among the drivers identified through a literature review, and then a hierarchical cluster analysis was performed to determine how the drivers could be grouped to understand their behaviors in the future. These behaviors were described in a baseline scenario and in six multidriver scenarios representing alternative futures. Scenarios were used to determine key implications for transportation agencies when addressing underlying challenges and potential opportunities which the transportation industry might entertain. The authors showed how agencies may use these scenarios to assess the performance of their plans or assumptions against potentially radical shifts in the future and develop strategies that could make the transportation system resilient to a wide variety of possible future conditions.

Ensuring the resiliency of critical infrastructures is essential in modern society, but much of the deployed infrastructure has yet to fully leverage modern technical developments. This paper intersects two unique fields—deep learning and critical infrastructure protection—and illustrates how deep learning can improve resiliency within the electricity sector. Machine vision is the combination of machine intelligence, or computer systems automatically learning patterns from exemplar data, and image analysis, or objects of interest being automatically segmented and identified from video image data. This technology has the potential to automate threat assessments in the context of securing critical infrastructures. Rather than traditional reactionary approaches, we present here a method of leveraging deep learning for the detection of threats to critical infrastructures before failures occur. This paper discusses the state-of-the-art in deep learning for creating machine vision systems, and the concepts are applied to increase the resiliency of critical infrastructures. The intersection between machine vision and critical infrastructures is discussed, as are key benefits and challenges of invoking such an approach, and examples within several fields of critical infrastructures are presented. Automated inspection of the power infrastructure using vehicle-mounted video acquisition equipment is explored, and a proof-of-concept implementation of a deep convolutional neural network is developed, achieving 95.5% accuracy in distinguishing power-related infrastructures within images largely typical of rural settings. These preliminary results show promise in the application of deep learning and machine vision to protecting critical infrastructures through preventative maintenance.

This paper presents a study aimed at developing zonal-specific semivariograms for zones with different climates using regionalized random variables for a strategic road weather information system (RWIS) network implementation and optimization in a large region. Zonal semivariograms modeled in this study were explicitly compared with regional semivariograms to demonstrate the (dis)similarity in their underlying spatial structures. Large-scale RWIS location and density optimizations were conducted with two groups of semivariograms developed in terms of their weather characteristics, namely regional and zonal, and were conducted to compare outcomes and illustrate their distinct features. A case study based on the existing RWIS network in Ontario, Canada, was used to show the application of the proposed method. The findings indicate that there are very different spatial autocorrelation patterns between regional and zonal-specific semivariograms, thereby emphasizing the need for a strategic zonal-specific RWIS implementation plan. The results of different planning scenarios for optimizing RWIS network also reveal that although the optimal locations are insensitive to the underlying spatial structure (i.e., semivariogram) used to optimize the network, the optimal density is found to be very sensitive to such, providing important yet useful decision-making guidance for improved efficiency and effectiveness of overall winter road maintenance programs.

The process of evaluation is pivotal for ensuring infrastructure assets meet the demands and needs of business and the wider community. An empirical ex post evaluation of the impact of regional heterogeneities for economic infrastructure assets in Australia is undertaken using a panel error-correction model. The paper provides governments with an invaluable insight into the nature of ex post evaluations, which is needed to develop and implement new performance measures to improve the public sector’s ability to future-proof their infrastructure assets.

Transportation greenhouse gas (GHG) emissions often account for the largest share of urban GHG emissions. Consequently, large-scale reductions in urban GHG emissions will not be possible without significant improvements in the transport sector. Increasing public transit mode share is widely promoted in efforts to reduce GHG emissions from transport. Large increases in public transit use will require the provision of new transportation infrastructure, which is itself GHG intensive. This paper presents a time-dependent analysis of the embodied GHG emissions associated with construction and reconstruction for the refurbishment and street redesign of the 510 Spadina streetcar route in Toronto, Canada during a 38-year period. From 1987 to 2015, the embodied emissions in the line’s civil infrastructure are calculated as 27.4 kilotons of ${\mathrm{CO}}_2$ equivalent (${\mathrm{ktCO}}_2\mathrm{e}$). It is expected that, by 2025, further reconstruction of the right-of-way (ROW) will increase embodied GHG emissions to $32.1{\mathrm{ktCO}}_2\mathrm{e}$. Overall, reconstruction projects increase GHG emissions by 25.9% beyond initial construction. When accounting only for at-grade infrastructure, reconstruction increases embodied emissions by 45.8% during the 38-year study period.