Geo-Extreme 2021: Infrastructure Resilience, Big Data, and Risk

The paper presents a novel approach and innovative engineering solutions in seismic retrofit of wharves in areas with risks of lateral spreading during an earthquake, using a case study of seismic retrofit of a historic wharf in Oakland, California. The wharf was built in the 1930s and located on a rock dike over the soft Young Bay Mud. The presence of the rock dike increases the seismic risk of lateral spreading and nonductile shear failure of piles driven through the dike. The wharf must be retrofitted and upgraded to conform to the current seismic engineering requirements. This paper describes the geotechnical investigation and structural condition assessment, site-specific seismic hazard assessment, and presents seismic retrofit strategies for stabilizing the slope stability against lateral spreading due to rock dikes over weak clay. The focus of this paper is the performance-based seismic design methods to achieve the wharf in a cost-effective, constructible, and reliable manner.

In this article, a recently developed nonlocal poromechanics model for geomaterials is applied to analyze the dynamic response of the Lower San Fernando Dam during the 1971 San Fernando earthquake. During the earthquake, the dam experienced a major flow failure in the upstream slope. Field investigations in the immediate aftermath indicated that the horizontal movement in the upper slope of the San Fernando Dam was caused by the liquefaction and weakening of the hydraulic fill materials in certain zones in the dam. Large blocks of mostly intact soil from the upstream section of the dam moved into the reservoir, riding over on the liquefied soil. Field analysis suggested that a significant pressure buildup occurred in the central region of the dam due to cyclic loading that later migrated to the upstream “toe” where it triggered failure and subsequently a sliding flow. To demonstrate that the proposed nonlocal poromechanics is realistic and relevant to the solution of large-scale, nonlinear, and coupled analysis, we conduct a two-stage seismic analysis of the Lower San Fernando Dam. First, an elastic analysis under quasi-static condition was carried out to determine initial effective stress and water pressure distributions. Second, a fully nonlinear dynamic analysis was performed with the earthquake load modeled as a base excitation following recorded acceleration profiles. The results have demonstrated that the proposed nonlocal poromechanics model is robust in modeling the localized failure of geomaterials under seismic loading by capturing the location and mode of failure observed in the field.

One of the major factors contributing to the damage of levees and other coastal structures during a strong earthquake is liquefaction of the foundation soil and levee embankment soil. To mitigate this risk, Japan’s central government and a regional government are improving a total of 23 km of coastal levees on the east and west sides of the southern city of Kochi which faces the Pacific Ocean. The existing levee structures are 30–40 years old and made of earthen fill materials with concrete linings and parapets on top. Liquefaction of levee foundations has been a great concern due to its underlying sandy soil with a high-ground water level. Sheet pile and pipe pile-reinforced structures are being constructed inside the existing earthen levees to minimize the detrimental effects of liquefaction and tsunami forces on the postevent flood protection of the levees. Both types of piles are being constructed with the press-in piling method without removing the existing structures during construction. This paper provides a case study of the effectiveness of the press-in piling method to construct these improvements.

Rayleigh damping is widely used to approximate frequency independent damping in time-domain seismic deformation analyses. Unfortunately, Rayleigh damping has serious drawbacks: it typically requires a drastically reduced numerical time step; it introduces spurious body forces for motion at a constant velocity; and the frequency range of approximately constant damping is quite narrow, with damping increasing rapidly outside of this range. An improved form of damping can be developed by replacing the dashpot of stiffness damping, with one or more Maxwell components (a spring in series with a dashpot). We describe here the numerical implementation of a Maxwell damping scheme for the geotechnical modeling program FLAC3D. Relations are derived for the complex stiffness, damping and phase velocity as functions of frequency. The performance and accuracy of the Maxwell damping scheme is illustrated for various wave-propagation problems.

Community resilience, as a heterogeneous set of components, includes many interconnected systems that actively interact with each other. These sociotechnical networks in their default forms are not always aligned, and therefore the resultant move or change may not be as expected. One system that has a significant role in the community resilience’s comprehensiveness is the educational system, which provides a considerable input for workforce required to design, develop, execute, and maintain resilient infrastructure and communities. To explore to what extent civil engineering and construction programs provide their students with adequate training about infrastructure and community resilience, a study was conducted in 2019–2020. The study had two primary research questions. First, how do construction and civil engineering male and female students perceive different aspects of infrastructure and community resilience? Second, how can relevant academic programs increase the subject interest and knowledge of students and pave their paths to become community resilience professionals and managers? A quantitative research methodology was used to gather, model, and analyze data. After preliminary studies, a survey was designed and distributed to collect civil engineering students’ perceptions. A total of 382 students from 15 higher education institutions in the US participated in the survey. While the results indicated a gap in participants’ perceptions toward resilience definition, factors, scope, and tools, students showed undeveloped potentials that can be exploited by community resilience educators and administration.

The loess tends to behave in an instable manner when subjected to external loads since it is characterized by metastable structure, high porosity, and water sensitivity. Therefore, the Chinese Loess Plateau has considered as a landslide-prone area in northwest China. This study explores the potential of using the loess-straw mixture as an alternative backfill material to improve the stability of loess against shallow slope failure and functionalize the agricultural solid waste. The displacement-controlled direct shear tests aim to document the baseline shear strength parameters of the loess specimen with and without straw waste inclusion, whereas the stress-controlled direct shear tests reveal the temporal relations of horizontal and vertical displacement under given shear stresses. The mechanism of causing the increase in shear strength is revealed. Furthermore, the stress state at failure in a displacement-controlled direct shear test and a stress-controlled direct shear test for same specimen under equal testing conditions show good correspondence to each other irrespective of testing method, and their results approximate a single failure envelope.

The stone column is one of the effective ground improvements that is increasingly used to support different infrastructures and buildings built on soft clay soil. Many studies have proven that a single column incorporates three different failure modes: bulging, punching, and shear. The load-carrying capacity and settlement are linked to the column’s behavior and deformation. These parameters are affected by the column configuration and properties, type and condition of loading, and the passive resistance of tributary soil. Studies that analyzed this technique under the serviceability limit state assumed that the effective length to diameter ratio of the column ranged between 4 and 6, and identified bulging as the common mode of deformation. Most studies conducted small scale laboratory works. Therefore, the results may not represent reality or may be appropriate only for shallow foundations supporting a light load. Thus, this project conducted an FE series of analyses to study a stone column’s behavior under a full state response considering the additional factors related to the geometry and properties of the column. Three distinct types of backfill material have been investigated in supporting Bangkok soft clay soil. The results of the investigation showed various failure modes, and the sand column was not appropriate to improve Bangkok soft clay. Also, it was observed that the effective L/H ratio used to improve the Bangkok soft clay is 0.5 with a column length equal to 6 m when made from aggregate material.

Data-driven methods have recently shown their potential in engineering problems. In this paper, a data-driven model is proposed and tested for 2D granular column collapse, a typical problem involving flow-like behavior of granular material. Although many theoretical and numerical models have been developed to investigate granular flows, data-driven methods have yet to be explored. This model can predict granular flows based on the sequential relations over time with data collected from numerical solutions. Two frameworks, a Lagrangian framework and an Eulerian framework, are proposed to give rules for collecting data, and radial basis function networks are used to infer the sequential relations. It is shown that the data-driven model gives reasonable predictions compared to results obtained through direct solution. When the size of data in both frameworks are close, the performance in the Eulerian framework is better, especially for predicting states of columns with different geometric parameters. A sensitivity analysis to explore the influence of time increment and grid spacing is also conducted.

p–y curves have been widely used to design laterally loaded drilled shafts and driven piles, which has been a reliable and predominant method of analysis for the past few decades. Research and practice provide enriched data that have been made continuous updates possible. So far, the developed p–y curves for different soils have been successfully incorporated into numerous software to aid the design of laterally loaded drilled shafts and driven piles. However, up to date, the design of drilled shafts/driven piles cannot consider the interaction between vertical and horizontal loads, if both loads are present. Namely, vertical and horizontal loads are considered independently in the current design practice. This assumption works reasonably well when either vertical or horizontal load is the dominant one; however, if both vertical and horizontal loads are significant, the validity of the existing p–y curves under these circumstances has not been systematically verified. This research concentrates on the behavior of drilled shafts under combined vertical and lateral loads, which uses experimental and numerical approach to investigate the effect of vertical loads on a laterally loaded shaft. The experimental study involves a lab-scale test of the drilled shaft built in a test chamber. The results from the lab-scale test were then used to calibrate a numerical model that was utilized to perform the parametric analysis to study the effects of many possible factors such as drilled shaft geometry, soil friction angle, and soil stiffness. The results from this study showed that the effect of the vertical load can be significant on a laterally loaded drilled shaft, and, in general, the presence of vertical load reduced the lateral deflection. The effect of the vertical load is less salient as the length of the shaft is increased.

The most common mode of levee failure, breach due to overtopping, is generally considered as a function of a complex set of hydraulic and geotechnical factors. The US Army Corps of Engineers (USACE) actively tracks and compiles data related to such events across the United States within their portfolio of levee systems. Recent experience suggests that frequency of flood events is increasing, leading to both increased stress on levees, as well as the probability of overtopping. As such, data related to this failure mode is constantly evolving. This paper presents and discusses a dataset of more than 200 overtopping events of USACE levee systems throughout the nation. The dataset is employed to examine the effects of various factors and to establish correlations among parameters using both breach and nonbreach events. Generating a discussion based on empirical evidence, this research is aimed at furthering the profession’s collective understanding of levee behavior when overtopping occurs. The dataset can provide a valuable basis for planning, design, and risk assessment of levees systems.

Cohesionless materials are desired as backfill for retaining structures. These structures have 1–4 ft cohesionless backfill within the vertical stem of the wall for drainage purposes. The remaining backfill is typically from available borrow materials that may have a percent of fines content with some cohesion. There are reported walls that the backfill has up to 95 kPa cohesion. These mixed backfills may consist of primarily cohesionless backfills but with some silts and/or clays. These types of walls can be exposed to high-saturation levels due to rain or being at the port facilities. In this study, the effect of mixed backfills on the seismic behavior of cantilevered retaining walls with a saturated backfill is analyzed. A validated numerical procedure was used in this study to analyze the seismic response of the walls with mixed backfills that are statically stable against overturning, sliding, and global stability. A 3-m-high cantilevered retaining wall with different backfill cohesions was modeled numerically under scaled seismic record of Imperial Valley earthquake. Pore-water pressure generation and pore-water pressure ratios were assessed and compared for models with cohesionless and cohesive backfills.

In recent years, a problematic cycle of drought, forest fires, precipitation, and rain-induced debris flows has affected California, but limited research has been performed on how the underlying soil is potentially weakened after additional soil-like material (ash) is produced. Additionally, while a large amount of research has been performed on characterizing forest fire ash, fewer studies have been performed on geotechnical characteristics of soil/ash mixtures. This study focuses specifically on the characterization of ash samples from a brushfire and the large displacement and residual shear strength of ash/soil mixtures and pure ash, respectively. Two types of samples are tested: (1) brushfire ash samples collected from fire exposed areas, and (2) ash/soil mixtures of pure brushfire ash and laboratory sand. Characterization of ash indicated a silty sand grain size distribution with chemistries that were expected for ash derived from biomass. However, it was determined that carbon content is not equivalent to loss-on-ignition (LOI) for biomass materials with high calcium content. Direct shear testing of ash/soil mixtures indicated that large displacement strength behavior was governed by frictional resistance between sand particles until ash quantity reaches 10% by weight, at which point the friction angle decreased by an average of 2°. Additionally, the residual strength of normally consolidated pure ash was high (above 30°), and the residual strength was reached at strains less than 1%.

On 30^th July 2014, a devastating rainfall induced landslide happened in Malin located 110 km from Pune in West Maharashtra (India) wiping out an entire village along with a school building and causing nearly 160 casualties. The debris flow from the rainfall at Malin is numerically modeled by distinct element analysis (DEM) using the Particle Flow Code (PFC2D) computer software. The results of the numerical analysis in terms of the extent of the debris flow, depth of debris at different locations, movement of debris from different locations, and velocity of the debris flow are presented and compared with the information available from field measurements and observations. A reasonably good match between the numerical results and the field observations indicates that the developed numerical methodology may be effectively used to predict the areas affected by debris flows during a landslide.

Rainfall-induced landslides pose significant risks to urban communities and infrastructure. The combination of transient rainfall, unsaturated soil properties, and spatially varying topography makes them difficult to forecast over large areas. This contribution describes a computational framework to evaluate landslide susceptibility over regional landscapes. To this aim, a description of a case study is presented, in which more than 40 shallow flowslides were triggered after 48 h of continuous precipitation over a region of 9 km². Several hypotheses have been proposed to explain the widespread distribution of slope instabilities, such as soil liquefaction, pore-pressure pulses due to layering, bedrock exfiltration, and antecedent hydrologic conditions, among others. Here, using available field and laboratory data to constrain the input parameters, different model scenarios are tested to back analyze the spatial and temporal occurrence of the events, namely, (1) slope failure caused by infiltration in homogenous deposits, (2) failures mediated by permeability contrasts in heterogeneous slopes, and (3) slope instabilities caused by bedrock springs within homogeneous soil profiles. Each scenario is evaluated by comparing the computed susceptibility maps and the temporal evolution of unstable areas against the landslide inventory. It is shown that stratigraphy effects can capture successfully the observed distribution of landslide source areas. Finally, the advantages and limitations of each scenario are discussed, and recommendations for future analyses are proposed.

In recent years, it has been frequently reported that with the advent of extreme rainfall events, sudden ground subsidence or even cave-in of roadway occurred in densely populated urban areas of China, in particular in sand-gravel strata. This had caused massive damage to pre-existing structures or even fatalities. The relevant investigations disclosed that many of these incidents largely arose from seepage erosion of the cohesionless strata surrounding defective sewer pipeline under intense infiltration of rainwater. Ground cavity formed in the strata and ultimately collapsed with washout of fine particles into the defective pipeline over time. To explore the relevant erosion mechanism, a coupled two-dimensional discrete element model was adopted for analysis. The granular material was modeled in Particle Flow Code (PFC); the flow field was computed in Fast Lagrangian Analysis of Continua (FLAC) and was then imported into PFC for seepage force simulation. Because the morphology of gravel affects its mechanical behaviors in the erosion n process, a random generation algorithm of polygonal gravel shape was implemented into PFC. Parametric studies were conducted to investigate influences of major factors, including initial ground water level, size of pipe defect, gravel content, and gravel shape (angularity). Simulation results suggest that high-ground water level after intense rainfall infiltration accelerates erosion process significantly. Moreover, the gravel content and gravel shape both have important roles in the formation of internal cavity and the timing of sudden collapse. The average rate of cavity expansion and ground surface settlement decreased as gravel content or angularity increased. In the end, conclusions were drawn based on these results, which could serve as references for practitioners to cope with relevant geohazards in the future.

For unsaturated soils, the reduction of matric suction caused by climate change (global warming) or extreme events (rainfall and wildfire) can potentially trigger landslides. It is well known that temperature can impact the strength of unsaturated soils by changing capillary pressure. A key parameter to determine capillary pressure in unsaturated soils is the contact angle between clay and water (e.g., through the Young-Laplace equation). In this paper, we conduct a series of molecular dynamics simulations of a clay-water model to investigate the impact of temperature on the clay-water contact angle. The spatial location of individual water molecular is derived from the coordinate of the molecule’s center-of-mass. A numerical approach is adopted to determine the contact angle by treating the water droplet as a three-dimensional point cloud. For each molecule, we fit a tangent plane to the surface spanned by its k-nearest neighbors using the covariance matrix method. The normal vector of the planar surface is used to derive the contact angle. For implementation, we enhance the covariance matrix method by a deep learning algorithm based on the Hough transform, a shape feature extraction technique by a voting procedure. Through the Hough transform, all the normal vectors are mapped into a spherical accumulator space (Hough space) divided by many bins of nearly the same area. Each bin corresponds to a small range of normal. The voted sphere accumulator is then transformed to a square image-accumulator, as the input of a convolutional neural network. The output of the convolutional neural network is two angles representing the normal direction in spherical coordinate system. The numerical results have demonstrated that the deep learning enhanced method is robust in characterizing variations of the contact angle between clay water at elevated temperature. It is expected the results can shed some light on interpreting unsaturated soil slope failures triggered by environmental temperature increase during extreme events.

In response to Hurricane Katrina, a multi-year study was performed for the US Department of Homeland Security to improve disaster recovery from flooding by way of emergency paving materials. This work was followed by several years of field aging research funded by the Mississippi Department of Transportation and supported by private industry. Chemical warm mix technologies were incorporated in all of this work, and this paper assesses data collected over several years to assess the resiliency of paving materials containing chemical additives when they are initially used in challenging conditions such as emergency paving requiring very long haul times. This paper showed asphalt is a resilient material that should be part of conversations on how to respond to extreme events such as hurricanes or other natural disasters. Chemical warm mix technologies can simultaneously facilitate longer haul distances and keep the residual material less crack prone for service over time.

Pipelines are the most common way to transport essential fluids, such as water, oil, and natural gas. A pipeline usually traverses a large geographical area, and it is likely to be exposed to geohazards, which might result in permanent ground deformations. These large deformations can occur due to landslide, lateral spreading, or fault movement. This experimental research focuses on the influence of boundary conditions on the performance of a pipeline in a reverse fault-crossing zone. Pipeline length in a fault area is generally far longer than the length of experimental models; therefore, it is challenging to properly simulate the far-field effect in laboratory studies. Because interactions between the pipe and surrounding soil in the hogging zone (bearing resistance) and sagging zone (uplift resistance) are not similar, a reverse fault-crossing area results in an asymmetric movement. This study presents the results of a series of a laboratory-scaled pipeline buried in a sandy layer with four different end conditions subjected to reverse fault offset: free (moving freely in three directions), axial-free (axially free but laterally fixed), springs (lateral and axial springs), and axial-fixed (axially fixed and laterally constrained by springs). The tests were carried out in a split-box apparatus modeling the reverse faulting under 1g conditions. The results show that the end-joint conditions of the pipe have a significant effect on failure mode of the pipeline. For the spring end joint simulating the bearing, uplift, and axial resistances of surrounding soil, local buckling occurred near the fault plane. Euler’s beam buckling was observed for the pipe with the axial-fixed end joint, and no significant deformation for the pipes with free and axial-free end joints was recorded. Finally, practical implications of the study are discussed, and recommendations are made for applying the results in experimental modeling of buried pipelines in reverse fault area are made.

The reversible shrink-swell behavior of the expansive soil imposes a serious challenge that threatens the safety and durability of the overlying structural systems, such as pavements, buildings, etc. Mineralogy of the expansive soil has been briefly discussed. In this paper, a treatment solution to reduce the swelling potential of the expansive soil was proposed. Different weight ratios of the polymeric stabilizing agents were studied and added to the bentonite samples. The proposed solution was examined in the free-swell soil test, and the efficiency of the hydrophobic closed-cell polyurethane foam in reducing the swelling potential of the high-swelling ability bentonite clay has been assessed. Results of those experimental tests showed promising enhancement in the behavior of the treated expansive soil.

Rain-fed agriculture areas in Africa are experiencing heavy pressure from climatic changes and food insecurity risk. This paper aims to provide an integrated modeling framework to identify food-insecurity hotspots in African agricultural systems and potential effective adaptation strategies at a local scale. We use the integrated modeling framework to drive rain-fed maize simulation models with baseline and generated climatic data. Maize yields simulated with the current and doubling CO₂ concentration are then compared to analyze the effect of CO₂ fertilization. Incorporating two local adaptive strategies into crop modeling provided some insights on preferred types of adaptation in different areas. The results showed that rain-fed maize production is highly vulnerable to simulated global warming. Such impacts differed with different weather conditions. Climate change has greater impacts on the relatively arid area. The results of the effectiveness of adaptive strategies imply that adaptive strategies need to be tailored to the local and community level to buffer the yield reduction impacts of climate change effectively.

The impacts of extreme weather cause billions of dollars of damage each year to buildings and structures, and these costs continue to increase as climate patterns change and buildings are exposed to new and different hazards than what they were designed to withstand. For organizations with large building portfolios, such as the US Department of Defense (DoD), it is important to have an awareness of the risks that extreme weather presents when conducting strategic planning on the long-range sustainment, restoration, modernization, and eventual recapitalization of their building stock. The primary objective of this research is to develop a model to quantify the estimated condition loss and effective service life reduction to the components of a building due to extreme weather hazards, and use the results to communicate system level and facility level risks. To do this, a damage association matrix is defined, which links characteristic damage modes for a given climate hazard to building component types. The resulting condition loss as measured by a component condition index is associated to each damage mode, along with the likelihood that the damage mode materializes given the hazard occurrence. This is applied to an actual portfolio of buildings in a particular geographic location and with a pre-defined component inventory that comprises the building. A weather forecasting model is used to estimate hazard occurrence likelihood along with building location, component type, and age to adjust the expected service life. The approach uses findings at a component level to support building sustainment decisions related to individual component repair/replacement, as well as identifying potential mitigation activities to reduce damage extent or likelihood. These findings are also aggregated to a system or building level to support recapitalization decisions related to restoration/modernization or total building replacement.

An increase of extreme weather events, such as high precipitation, hurricanes, and floods, leads to frequent water-induced damage to pavement infrastructure. Rapid pavement infrastructure assessments after disaster are regularly based on a qualitative visual inspection and do not provide high-resolution information. Impacts to pavement serviceability can be assessed quantitatively using the International Roughness Index (IRI). The purpose of this study was to evaluate five phone-based applications to rapidly approximate IRI for a pavement by measuring vehicle response in conjunction with evaluating factors that may affect data collection such as phone model, mount, and mount placement in the vehicle. Nine secondary and tertiary roadway sections encompassing a variety of roughness and surface condition levels (good, fair, and poor) were identified and rapid visual assessment of their condition was performed. Their roughness was evaluated using optimum commercially available phone applications and four phone models to assess the consistency of the roughness measurement across multiple hardware and software combinations. It was concluded that a rigid phone mount placed on the windshield was superior. Total Pave IRI demonstrated the most consistency and reliability when recording data across different phone types.

The success of wells is heavily reliant on accurate identification of the formation being drilled. Lithology prediction allows us to foresee the target interval ahead of schedule to ensure maximum contact with the producing zone. By viewing the drilling process as a multivariate time series, algorithms such as Toeplitz Inverse Covariance-Based Clustering (TICC) can be deployed to generate a refined dataset for supervised deep learning models such as Long Short-Term Memory (LSTM). The present study combines labeled dataset generation through the TICC algorithm and predictive time-series classification via deep learning models to determine the formation ahead of the sensors. The well trajectory can be re-evaluated in real time to foresee what the drill bit will encounter in advance, allowing directional drillers to optimize well placement and ultimately production. This methodology can be implemented across all disciplines where real-time data analysis is necessary for immediate decision making such as anomaly detection, predictive modeling, and risk assessment.

Many researchers have examined compound flooding by either statistically characterizing the joint occurrence of river flows and storm tides, or physically modeling selected storm scenarios within high-fidelity computational models. However, there has been less work on developing methods that quantify risk from multiple sources of flooding in terms of return period flood heights, which are crucial components of coastal design. In this study, we compare statistically driven versus physically driven approaches for compound flood height assessment in order to understand if computationally efficient statistical methods can accurately represent joint flood hazard. We utilize storm tides and river flows from 941 synthetic tropical cyclone (TC) events passing near Town Creek, NC. We first apply a widely used statistically driven approach that estimates the joint peak flow (Q) and storm tide (S) distribution using a bivariate copula and develops a limited set of scenarios for flood mapping. We compare this computationally efficient approach to a physically driven approach that simulates the full set of events within a hydrodynamic model and then estimates return period flood heights based on the modeled maximum depths. By comparing the two methods, we find that the statistically driven approach can capture the different flood zones present along the catchment and can estimate maximum water levels well compared to the physics-driven approach for low-return periods. However, for high-return periods the statistics-driven approach significantly underestimates water levels in the midstream portion of the catchment.

The objective of this study is to develop preliminary predictive models for seismic energy dissipation of rocking foundations using machine learning algorithms and supervised learning technique. Data from a rocking foundation database consisting of dynamic base shaking experiments conducted on centrifuges and shaking tables have been used for the development of multivariate linear regression (MLR) model using stochastic gradient descent optimization and weighted k-nearest neighbors (k-NN) model. Normalized seismic energy dissipation in soil during rocking is considered as the performance parameter and the input features include critical contact area ratio and rocking coefficient of soil-foundation system, and peak ground acceleration and Arias intensity of earthquake. It is found that though both MLR and weighted k-NN models perform satisfactorily in capturing the complex relationships between seismic energy dissipation and input features of rocking systems, weighted k-NN model consistently outperforms MLR model for the problem considered.

The effects of disaster events on gas emissions from landfills was evaluated using earthquakes as an example event. Cracking constituted the main damage mechanism and available data from previous studies was adapted to establish crack characteristics. In particular, methane flux was estimated using STOMP numerical analysis software. A landfill with a 1-m-thick intermediate cover was analyzed. Overall, three levels of damage (low, moderate, and high) were evaluated, where normalized crack depths ranged from 0.25 (surficial) to 1.0 (full depth) and the extent of cracking ranged from 0.01% to 3% of the landfill area. Flux and emissions increased due to earthquake damage with relatively low increases for partial crack penetration and significant increase due to full cover cracking. The emissions increased from 389 to 678 t CO₂-Eq./year from baseline, intact conditions to high damage conditions for a landfill area of 100 ha. Disaster events provide a pathway for increasing atmospheric abundance of methane, which is a greenhouse gas with significant climate impacts.

The importance of the geological model in a geotechnical engineering project has long been recognized. However, the uncertainty associated with the geological model has rarely been quantified and explicitly considered in the geotechnical design. This paper explores the role of the geological model uncertainty and the benefit of reducing such uncertainty in a geotechnical design. To this end, the landslide that occurred on April 25, 2010, at 3.3K of Freeway No. 3 in Northern Taiwan, referred to herein as NH-3 Slope, is reanalyzed with various assumed geological models. Traditionally, the design (i.e., a choice of design parameters such as slope height, slope angle, and supporting anchors) of a weathered rock slope seeks to satisfy a target safety factor (FS). In the case of NH-3 slope, the apparent dip angle (i.e., inclination) of the slip surface along the dip direction of slope constitutes the geological model of concern. Different survey techniques produce data with varying degrees of accuracy and precision (or uncertainty) in the measured apparent dip angle. For example, data from a Regional Geological Map (RGM) typically yield a low-precision estimate (with a high level of uncertainty) of the bedding plane attitudes; in contrast, LiDAR can significantly reduce the uncertainty of the derived bedding plane attitudes. By lowering this uncertainty, the variation (or uncertainty) in the resulting FS may be reduced, which tends to yield a lower failure probability (P_f), given other conditions being the same. From a design perspective (to achieve a target P_f), the use of LiDAR data (with lower uncertainty), as opposed to the use of RGM data (with higher uncertainty), may result in an acceptable design at a lower cost. The benefit of reducing the geological model uncertainty in a geotechnical design is demonstrated through a retrospective analysis of the NH-3 slope, under both the “as-design” condition and the “failure” state.

The US Department of State, Bureau of Overseas Buildings Operations (OBO) manages a portfolio of over 25,400 properties spread across 290 foreign missions, for which it is charged with a broad range of responsibilities including acquisition, design, construction, and operations and management. Central to OBO’s mission is providing safe, secure, functional, and resilient facilities that represent American values. At all of OBO’s global missions, there is an emphasis on keeping properties secure from natural hazard-related risks including geohazard threats. Ground subsidence, karst, and landslide hazards were managed during projects at high-risk posts in Bangkok, Merida, and Caracas through early geotechnical involvement, design team coordination, and design and construction modifications to mitigate risk potential. Future forecasts anticipate climate change to increase exposure to natural hazards at these and other missions intensifying the need to modernize current risk management techniques. OBO’s evolving Climate Security & Resilience (CS&R) Program will facilitate integration of improved data-informed strategies into risk management processes to better ensure OBO facilities are secure from and resilient to a changing climate and risks posed by natural hazards. The CS&R program is developing a dashboard to display exposure information of multiple natural hazards (flood, earthquake, tsunami, extreme wind, extreme heat, and water stress) and an overlay with vulnerabilities to capture risks to overseas missions. This information will then be utilized to develop more efficient planning strategies and processes to evaluate and mitigate risk. Future goals for the CS&R program include seeking out and incorporating geohazard exposure and vulnerability information into the dashboard to assist OBO in managing cost-effective methods for characterizing and reducing geohazard risk alongside other hazards. Geohazard predictive modeling would allow OBO to improve current practices of assessing risk based on past events and more accurately map effects of climate change.

With population growth, US coastal communities have been growing in recent years. More people tend to live along the coasts because of the unique recreational and economic opportunities offered by these regions. The value of coastal properties along the Atlantic and Gulf coasts has experienced a compound annual growth rate of about 4% over the past decade. People and assets in the coastal regions, however, are exposed to a rising risk from weather and climate-related hazards such as hurricanes, storm surge, and flooding. In the same vein, with rising sea levels and a possible increase in the frequency of wet storms like Harvey and Florence, insurance companies and decision-making organizations need resilient strategies for coastal risk management more than ever before. As a leading natural hazards catastrophe modeling company, AIR Worldwide (AIR) developed the first US building inventory database in 1987 that provides a high-resolution view of properties including the physical characteristics of structures and their respective replacement values. This paper focuses on the importance of property databases as the foundation for catastrophe model loss estimates. It is described how AIR manages to realistically capture the estimation of properties at risk by leveraging available resources. AIR’s view of risk and insights on the US coastal properties and common construction practices are put in perspective.

Event trees are often used to assess the risks associated with geoextreme events caused by natural hazards and choose the appropriate risk-management strategy. However, because the frequencies of interest for these extreme events can be as small as once per 1,000–10,000 years, there is limited information available to assess them. This paper proposes a methodology for assessing the probabilities of rare events. The methodology utilizes the possibility of fractional occurrences in a random process to represent estimates of small frequencies in limited lengths of historical record and a decision-based approach to establish noninformative probabilities to be updated with the available information. A case study of a risk-based rehabilitation decision for major rockfill dam demonstrates that realistically representing the uncertainty in assessing small frequencies is important, both in deciding whether or not to rehabilitate the dam and in assessing the value of obtaining additional information about the risk.

The risk of personal injury due to extreme events cannot be completely eliminated by the government agencies responsible for maintenance of public property, including geotechnical infrastructure. Nevertheless, when extreme events occur, those who incur personal injury as a result of geosystem failure will often seek to hold those government agencies responsible. This paper examines the legal grounds upon which government agencies may be held liable, and the immunity theories under which government agencies may avoid liability for personal injury due to geosystem failure during extreme events. As a case study, the paper examines litigation involving the 2014 Oso landslide in the State of Washington, which killed forty-three people and injured many others, resulting in potential liability for federal, state, tribal, and local government agencies. Although the federal, tribal, and local government agencies avoided liability on a variety of immunity theories, the state government ultimately consented to a judgment of $50 million to resolve all personal injury claims against state agencies.

Extreme precipitation events can cause flooding leading to severe socioeconomic and environmental impacts. Drawing on General Climate Model (GCMs) projections, an assessment of the potential impacts of climate change on the characteristics of extreme precipitation events was undertaken. Statistical downscaling approaches, along with temporal disaggregation techniques, were implemented to estimate continuous precipitation series at fine spatiotemporal scales, allowing extreme precipitation events to be extracted. Quantile-based downscaling approaches have recently been applied to estimate future extreme precipitation quantiles through empirical relationships between GCM and observed precipitation quantiles. It remains unclear how these two approaches differ from one another in terms of their projections. In this study, two quantile- and one continuous-based approaches were employed to estimate the future extreme precipitation quantiles in Montréal, Canada. These downscaling approaches provided an acceptable performance during the historical period; however, their estimates of the sign and magnitude of future extreme precipitation quantile changes differed considerably.

Natural hazards have been a serious problem in society due to their risks to human lives and infrastructure. An extreme storm in Hong Kong can trigger multiple types of hazards due to the hilly terrain. Hence, assessment of the multihazard risk is needed. This study aims to assess the risks posed by multiple hazards on Hong Kong Island under two storm scenarios of 29% and 85% of the 24-h Probable Maximum Precipitation (PMP). The hazards considered in this study are landslides, debris flows, and flooding. The consequences on different kinds of buildings and exposed population are evaluated quantitatively and recorded by combining both the multihazard layers and the building database layer. The results are presented on a geographic information system (GIS) showing the spatial distributions of the hazards and the potentially affected population from the impact of the three integrated multihazards. The outcome of the study indicates high vulnerability if Hong Kong Island is impacted by high-intensity rainstorms. The multirisk analysis information in this study is useful for making decision on suitable risk management measures.

Flexible pavements in cold regions are subjected to severe environmental conditions. Adding an insulating layer into a regular flexible pavement structure can reduce the frost penetration depth and mitigate uneven frost heave. This study used a thermo-hydro-mechanical coupled model to evaluate the pavement responses for pavements insulated with extruded polystyrene (XPS) boards. Model input parameters were calibrated with experimental and simulation data obtained from a large-scale insulated pavement box and Layered Elastic Analysis. Moreover, the potential application of the simulation results for the structure design of insulated pavement was demonstrated. Numerical simulation results validate that the presence of the insulation layer can efficiently reduce the frost penetration depth, whereas might induce loading capacity and drainage issues.

Seasonal Freeze-Thaw cycles (FT) is a key to environmental processes and socioeconomic activities across northern latitudes. The large-scale dynamics of FT are mainly governed by air temperature and snow depth. We argue that this physical control can be empirically characterized, represented, and simulated by the trivariate dependence structure between FT, temperature, and snow depth. To showcase this, we consider the gridded data of these variables over Québec, Canada, and use canonical vine copulas to formulate the trivariate interdependence between FT, temperature, and snow depth in different grids and ecozones. Our results reveal different dependence structures across Québec ecozones, pointing at the role of landscape in regulating the impacts of snow depth and temperature on FT. Having the trivariate dependence, we use a bottom-up impact assessment approach to address the alterations in FT characteristics under single and compound changes in the temperature and snow depth.

The quantity and intensity of wildfires have increased while permafrost in the interior of Alaska has become more vulnerable to thaw with warming climate conditions. The Trans-Alaska Pipeline System (TAPS) transports oil through a 1.2-m-diameter pipeline from Prudhoe Bay to Valdez and passes over stretches of warm, discontinuous permafrost. The pipeline is typically supported aboveground where it traverses thaw-unstable permafrost. In 2015, a lightning strike caused a wildfire in boreal forest underlain by ice-rich permafrost, northwest of Fairbanks, Alaska. Nearly 13 km of the pipeline were within or along the edge of the wildfire perimeter. Alyeska Pipeline Service Company selected a site to evaluate wildfire impact on warm, ice-rich permafrost, and long-term effects on aboveground pipeline supports. Findings show warming and degrading permafrost in the vicinity of the pipeline following the fire, indicating that wildfire control practices near TAPS could be re-evaluated where TAPS traverses warm permafrost.

Engineered water repellency has the potential to mitigate frost heave in geotechnical systems such as roads and foundations. Models can be used to inform design approaches and predict performance; the literature is replete with general models of frost action. There are comparatively fewer reports on how to practically incorporate the effect of mineral surfaces, pore fluid composition, and engineered water repellency into thermo-hydro-mechanical-chemical (THMC) models. An aspect of such models involves describing the state of pore fluid in the frozen fringe and unfrozen soil beneath an impinging freezing front and growing ice lens. Capillary and osmotic gradients are created as ice formation reduces moisture content while increasing the concentration of ions in the remaining unfrozen pore fluid. The work reported here is part of a larger effort to quantify the relative role of osmotic and matric potential on frost heaving, while exploring the use of organosilanes to mitigate ice lens formation and growth. As a precursor to incorporating the broader array of physicochemical processes in frost action, this paper reviews the sensitivity of unsaturated flow to changes in contact angle. Unsaturated flow gradients are created in response to simulated matric, osmotic, and cryogenic suction within a two-dimensional model. Results indicate that an increased contact angle results in a reduction in unsaturated flow, regardless of induced gradient.