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Infrastructure service disruptions impact households in an affected community disproportionally. To enable integrating social equity considerations in infrastructure resilience assessments, this study created a new computational multi‐agent simulation model, which enables integrated assessment of hazard, infrastructure system, and household elements and their interactions. With a focus on hurricane‐induced power outages, the model consists of three elements: (1) the hazard component simulates exposure of the community to a hurricane with varying intensity levels; (2) the physical infrastructure component simulates the power network and its probabilistic failures and restoration under different hazard scenarios; and (3) the households component captures the dynamic processes related to preparation, information‐seeking, and response actions of households facing hurricane‐induced power outages. We used empirical data from household surveys from three hurricanes (Harvey, Florence, and Michael) in conjunction with theoretical decision‐making models to abstract and simulate the underlying mechanisms affecting the experienced hardship of households when facing power outages. The multi‐agent simulation model was then tested in the context of Harris County, Texas, and verified and validated using empirical results from Hurricane Harvey in 2017. Then, the model was used to examine the effects of different factors—such as forewarning durations, social network types, and restoration and resource allocation strategies—on reducing the societal impacts of service disruptions in an equitable manner. The results show that improving the restoration prioritization strategy to focus on vulnerable populations is an effective approach, especially during high‐intensity events, to enhance equitable resilience. The results show the capability of the proposed computational model for capturing the dynamic and complex interactions in the nexus of households, hazards, and infrastructure systems to better integrate human‐centric aspects in resilience planning and assessment of infrastructure systems in disasters. Hence, the proposed model and its results could provide a new tool for infrastructure managers and operators, as well as for disaster managers, in devising hazard mitigation and response strategies to reduce the societal impacts of power outages in an equitable manner. [ABSTRACT FROM AUTHOR]

Civil infrastructure systems transform over the long-term due to complex interactions among socio-environmental stressors, degradation of physical networks, and decision-making processes of infrastructure agencies. To incorporate such complex transformation mechanisms into infrastructure cost management, this study integrates simulation modeling and life-cycle cost analysis (LCCA) to create a framework for dynamic network-level LCCA in infrastructure systems. The proposed framework was implemented using a multi-agent simulation model in a numerical case study related to a road network affected by sea-level rise, population growth, and uncertain maintenance funding. The results include identification of several state transition, and tipping point behaviors at which small changes in service demand, maintenance budget, and adaptation investment may result in significant improvement in network cost and performance conditions. The results show the capability of the proposed framework in modeling the long-term trajectories of life-cycle costs at the network level under different stressors and decision scenarios. [ABSTRACT FROM AUTHOR]

The objective of this article is to systematically assess and identify factors affecting risk disparity due to infrastructure service disruptions in extreme weather events. We propose a household service gap model that characterizes societal risks at the household level by examining service disruptions as threats, level of tolerance of households to disruptions as susceptibility, and experienced hardship as an indicator for the realized impacts of risk. The concept of "zone of tolerance" for the service disruptions was encapsulated to account for different capabilities of the households to endure the adverse impacts. The model was tested and validated in the context of power outages through survey data from the residents of Harris County in the aftermath of Hurricane Harvey in 2017. The results show that households' need for utility service, preparedness level, the existence of substitutes, possession of social capital, previous experience with disasters, and risk communication affect the zone of tolerance within which households cope with service outages. In addition, sociodemographic characteristics, such as race and residence type, are shown to influence the zone of tolerance, and hence the level of hardship experienced by the affected households. The results reveal that population subgroups show variations in the tolerance level of service disruptions. The findings highlight the importance of integrating social dimensions into the resilience planning of infrastructure systems. The proposed model and results enable human‐centric hazards mitigation and resilience planning to effectively reduce the risk disparity of vulnerable populations to service disruptions in disasters. [ABSTRACT FROM AUTHOR]

Street view imagery has emerged as a valuable resource for urban analytics research. Recent studies have explored its potential for estimating lowest floor elevation (LFE), offering a scalable alternative to traditional on‐site measurements, crucial for assessing properties' flood risk and damage extent. While existing methods rely on object detection, the introduction of image segmentation has expanded the utility of street view images for LFE estimation, although challenges still remain in segmentation quality and capability to distinguish front doors from other doors. To address these challenges in LFE estimation, this study integrates the Segment Anything model, a segmentation foundation model, with vision language models (VLMs) to conduct text‐prompt image segmentation on street view images for LFE estimation. By evaluating various VLMs, integration methods, and text prompts, the most suitable model was identified for street view image analytics and LFE estimation tasks, thereby improving the coverage of the current LFE estimation model based on image segmentation from 33% to 56% of properties. Remarkably, our proposed method, ELEV‐VISION‐SAM, significantly enhances the availability of LFE estimation to almost all properties in which the front door is visible in the street view image. In addition, the findings present the first baseline and quantified comparison of various vision models for street view image‐based LFE estimation. The model and findings not only contribute to advancing street view image segmentation for urban analytics but also provide a novel approach for image segmentation tasks for other civil engineering and infrastructure analytics tasks. [ABSTRACT FROM AUTHOR]

Environmental regulations can greatly affect the operation of new and existing infrastructure projects. In fact, the economic performance of infrastructure projects is closely related to environmental regulations whose landscape is extremely dynamic. To assist infrastructure project sectors in better coping with environmental regulatory changes, this study proposes guidelines for the robust adaptation to environmental regulations through information collected from an exploratory research process. As part of these guidelines, a process map model was developed that details the different stages in the environmental regulation development process, including prerulemaking, proposed rule development and publication, commenting period, final rule development and publication, and post-promulgation. Five adaptation subprocesses (i.e., regulatory horizon scanning, stakeholder engagement, risk and opportunity assessment, response strategy development, and compliance implementation) are examined to help regulated entities better adapt to regulatory changes across different regulatory stages. This information has the potential to provide companies with a structured stage-by-stage approach for dealing with uncertainties arising from environmental regulation changes. [ABSTRACT FROM AUTHOR]

Damages in critical infrastructure occur abruptly, and disruptions evolve with time dynamically. Understanding the situation of critical infrastructure disruptions is essential to effective disaster response and recovery of communities. Although the potential of social media data for situation awareness during disasters has been investigated in recent studies, the application of social sensing in detecting disruptions and analyzing evolutions of the situation about critical infrastructure is limited. To address this limitation, this study developed a graph‐based method for detecting credible situation information related to infrastructure disruptions in disasters. The proposed method was composed of data filtering, burst time‐frame detection, content similarity calculation, graph analysis, and situation evolution analysis. The application of the proposed method was demonstrated in a case study of Hurricane Harvey in 2017 in Houston. The findings highlighted the capability of the proposed method in detecting credible situational information and capturing the temporal and spatial patterns of critical infrastructure events that occurred in Harvey, including disruptive events and their adverse impacts on communities. The proposed methodology can improve the ability of community members, volunteer responders, and decision makers to detect and respond to infrastructure disruptions in disasters. [ABSTRACT FROM AUTHOR]

Climate change has various chronic and acute impacts on civil infrastructure systems (CIS). A long-term assessment of resilience in CIS requires understanding the transformation of CIS caused by climate change stressors and adaptation decision-making behaviors of institutional agencies. In addition, resilience assessment for CIS includes significant uncertainty regarding future climate change scenarios and subsequent impacts. Thus, resilience analysis in CIS under climate change impacts need to capture complex adaptive behaviors and uncertainty in order to enable robust planning and decision-making. This paper presents a system-of-systems (SoS) framework for abstraction and integrated modeling of climate change stressors, physical infrastructure performance, and institutional actors’ decision-making processes. The application of the proposed SoS framework is shown in an illustrative example related to the impacts of sea level rise and subsequent saltwater intrusion on a water system. Through the use of the proposed SoS framework, various attributes, processes, and interactions related to physical infrastructure and actor’s decision-making were abstracted and used in the creation of a computational simulation model. Then, the computational model was used to simulate various scenarios composed of sea level rise and adaptation approaches. Through an exploratory analysis approach, the simulated scenario landscape was used to identify robust adaptation pathways that lead to a greater system resilience under future uncertain sea level rise. The results of the illustrative example highlight the various novel capabilities of the SoS framework: (i) abstraction of various attributes and processes that affect the long-term resilience of infrastructure under climate change; (ii) integrated modeling of CIS transformation based on simulating the adaptive decision-making processes, physical infrastructure performance, and climate change impacts; and (iii) exploratory analysis and identification of robust pathways for adaptation to climate change impacts. [ABSTRACT FROM AUTHOR]

The objective of this study is to establish a framework for analyzing infrastructure dynamics affecting the long-term steady state, and hence resilience in civil infrastructure systems. To this end, a multi-agent simulation model was created to capture important phenomena affecting the dynamics of coupled human-infrastructure systems and model the long-term performance regimes of infrastructure. The proposed framework captures the following three factors that shape the dynamics of coupled human-infrastructure systems: (i) engineered physical infrastructure; (ii) human actors; and (iii) chronic and acute stressors. A complex system approach was adopted to examine the long-term resilience of infrastructure based on the understanding of performance regimes, as well as tipping points at which shifts in the performance regime of infrastructure occur under the impact of external stressors and/or change in internal dynamics. The application of the proposed framework is demonstrated in a case of urban water distribution infrastructure using the data from a numerical case study network. The developed multi-agent simulation model was then used in examining the system resilience over a 100-year horizon under stressors such as population change and funding constraints. The results identified the effects of internal dynamics and external stressors on the resilience landscape of infrastructure systems. Furthermore, the results also showed the capability of the framework in capturing and simulating the underlying mechanisms affecting human-infrastructure dynamics, as well as long-term regime shifts and tipping point behaviors. Therefore, the integrated framework proposed in this paper enables building complex system-based theories for a more advanced understanding of civil infrastructure resilience. [ABSTRACT FROM AUTHOR]

There is an urgent need for policies to close the existing financing gap for civil infrastructure. However, identification of the desired scenarios for the closure of the financing gap is complex because there are many factors that affect investment in infrastructure. Thus, comprehensive models are required to (1) simulate the impacts of policies, and (2) identify the highly likely scenarios for desired policy outcomes. The objective of this paper is to create a simulation model for ex-ante analysis of financing policies in highway transportation infrastructure in the United States. Using the agent-based technique, this model simulates the microbehaviors of state Departments of Transportation, private institutional investors, and the public. Using the output of the simulation model, financing landscapes of the U.S. transportation infrastructure are developed. The simulated landscape is shown to be helpful in identifying the highly likely scenarios leading to a high level of investment in highway transportation infrastructure under the existing infrastructure investment structures and budget constraints in the United States. The study presented in this paper is novel with respect to (1) the application of the system-of-systems modeling in the analysis of transportation infrastructure financing policies, (2) identification of the desired policy scenarios and their likelihoods for closure of the financing gap in the presence of uncertainties and adaptive behaviors, and (3) simulation and visualization of the impacts of financing policies at the state and national levels. The model promotes a data-driven policy analysis and provides policymakers with a tool to simultaneously account for the impacts of several factors and uncertainties. [ABSTRACT FROM AUTHOR]

While traditional financing approaches such as federal and state grants funded by taxation are insufficient to address the existing need, innovative financing such as novel public private partnership models, credit enhancement tools, and new bonding instruments has emerged to expand the fiscal space of public agencies for infrastructure development. Formulating innovative financing approaches is one of the challenges faced by policymakers to address the ever growing need for restoring the failing civil infrastructure in the U.S. Public support/opposition is one of the major drivers/disruptors of innovative financing. Assessment of public perceptions is a major component towards gaining public support and developing sustainable infrastructure financing policies. The objective of this study is to investigate the determinants of public perceptions of innovative financing. Data obtained from public survey of 616 individuals from 50 states in the U.S. is analyzed to investigate the effects of: (a) economic factors, (b) infrastructure conditions, and (c) personal characteristics of the public on the knowledge, awareness, perceptions, and attitudes of the public towards innovative financing. The findings reveal that the likelihood of public support of innovative financing is insensitive to economic conditions and is affected by the factors related to infrastructure condition such as the level of need for infrastructure renewal, the ability to protect the public against natural disasters, and the history of using innovative financing for infrastructure renewal. The findings also show that 57% of the sample population in the U.S. were not knowledgeable about different methods for financing infrastructure projects and 77% of the sample population in the U.S. were not aware of the activities of public organizations for finding innovative financing solutions for infrastructure projects. Based on the findings, two main strategies have been proposed for infrastructure agencies to enhance their efforts towards gaining public acceptance of innovative financing: (i) enhance strategies to facilitate learning for the public about innovative financing in infrastructure projects and (ii) improve marketing and citizen involvement strategies to get the public to buy-in to innovative financing methods. The findings can enhance the current efforts of public agencies related to educating the public and citizen involvement strategies by incorporating public preferences in policy development to enhance the likelihood of public support of innovative financing methods. Potential improvements can be made in changing the key messages in educating the public, using more effective strategies for communicating complicated financing concepts, and highlighting the success stories and benefits of innovative financing in other states and countries. [ABSTRACT FROM AUTHOR]

The objective of this study is to predict the near-future flooding status of road segments based on their own and adjacent road segments' current status through the use of deep learning framework on fine-grained traffic data. Predictive flood monitoring for situational awareness of road network status plays a critical role to support crisis response activities such as evaluation of the loss of access to hospitals and shelters. Existing studies related to near-future prediction of road network flooding status at road segment level are missing. Using fine-grained traffic speed data related to road sections, this study designed and implemented three spatio-temporal graph convolutional network (STGCN) models to predict road network status during flood events at the road segment level in the context of the 2017 hurricane Harvey in Harris County (Texas, USA). Model 1 consists of two spatio-temporal blocks considering the adjacency and distance between road segments, while model 2 contains an additional elevation block to account for elevation difference between road segments. Model 3 includes three blocks for considering the adjacency and the product of distance and elevation difference between road segments. The analysis tested the STGCN models and evaluated their prediction performance. Our results indicated that model 1 and model 2 have reliable and accurate performance for predicting road network flooding status in near future (e.g., 2–4 h) with model precision and recall values larger than 98% and 96%, respectively. With reliable road network status predictions in floods, the proposed model can benefit affected communities to avoid flooded roads and the emergency management agencies to implement evacuation and relief resource delivery plans • Using high-resolution traffic data to predict near-future road inundation status in urban floods • Adjusting spatio-temporal graph convolutional networks for road network inundation status predictions • Contributing near-future infrastructure failure prediction capability to the smart flood resilience [ABSTRACT FROM AUTHOR]