A Climate Adaptation Asset Risk Management Approach for Resilient Roadway Infrastructure

Abstract

As climate change intensifies, roadway infrastructure is increasingly at risk from extreme weather events including floods, hurricanes, and wildfires. This paper presents a system-of-systems performance-based asset risk management approach, designed to integrate various elements for effective investment prioritization and infrastructure resilience. Central to this approach are an Asset Inventory Database and a Risk Registry Database, supported by a Common Reference Location System (GIS). These components are the foundation for analytical modules to assess vulnerability and resilience based on exposure, sensitivity, and adaptive capacity. The approach includes an actionable framework to support a proactive data-driven performance-based management process for prioritizing investments. The project prioritization process consists of four steps: identifying risk factors, integrating climate data, conducting advanced risk assessments, and project prioritization. The goal is to prioritize resource allocation and develop climate-adaptive risk mitigation management strategies. Key performance indicators (KPIs) are recommended for setting goals, monitoring the outcomes of these strategies, and measuring their benefits. A Climate Impact Vulnerability Score (CIVS) is proposed to assess the susceptibility of infrastructure assets to environmental conditions. The approach also leverages artificial intelligence (AI) tools to analyze roadway infrastructure vulnerabilities and climate risk exposure. A case study applied to bridges using k-means clustering and multi-criteria decision analysis (MCDA) demonstrates the potential of advanced analytical methods in improving decision-making. This research concludes that the approach will contribute to enhancing resource allocation, supporting strategic decisions, aligning goals with budgets prioritizing investments, and strengthening the resilience and sustainability of roadway infrastructure.

1. Introduction

Roadway infrastructure is fundamental to sustainable development, driving economic and social growth towards healthy communities. Effective management is crucial for preserving this infrastructure. Climate change has increased the frequency and severity of extreme weather events including floods, drought, hurricanes, tornadoes, tsunamis, and wildfires. This poses a significant threat to the condition of roadway infrastructure due to the risk of damage, affecting project lifecycles and thus underscoring the urgency for immediate, adaptive measures.

Risk management emerged as a distinct practice around 2005, differentiating itself from traditional management that considered risk but lacked formal risk management practices [1]. At present, there is a growing interest in using risk assessment to support transportation asset management. Initial efforts have concentrated on enterprise and program risks related to specific asset groups, evolving into systematic methods for assessing uncertainty and variability of risk events. The focus has since shifted towards managing risks to evaluate their impact on asset conditions and establish performance-based objectives for entire roadway infrastructure systems, moving beyond earlier efforts targeting individual asset groups (e.g., pavements and bridges).

Aligned with these efforts at the international level, ISO 31000 (2021) offers a comprehensive global standard for risk management applicable across various sectors [2]. It emphasizes a dynamic and inclusive approach prioritizing the identification, assessment, and control of risks, fostering a culture of risk awareness. Additionally, there are several documented efforts worldwide aiming to manage risks effectively, incorporating diverse strategies tailored to the specific challenges faced by different regions and industries. For instance, the Netherlands employs the RISMAN method to assess and mitigate climate-related risks to their flood-prone infrastructure. The RISMAN method is a cyclical risk management process akin to Deming’s quality circle. It starts with an initial risk analysis, identifying major risks and establishing control measures to mitigate them. The project manager then implements these measures, along with regular evaluations to ensure they remain effective. After each evaluation, risks and controls are updated to address the most pressing current threats, ensuring continuous, adaptive risk management throughout the project. In France, the GERICI framework is used for roadway infrastructure to manage risks posed by extreme weather events. It stands for Gestion des Risques Climatiques sur les Infrastructures (Management of Climate Risks on Infrastructure) and is designed to manage climate risks to roadway infrastructure by assessing vulnerabilities, developing adaptation strategies, and continuously monitoring effectiveness. Through risk assessment, agencies identify parts of the transportation network susceptible to extreme weather, such as floods or heatwaves, and use adaptation planning to implement strategies that enhance resilience. Finally, ongoing monitoring and evaluation allow agencies to track infrastructure performance and adjust measures as needed, ensuring both public safety and cost-effective maintenance. Japan has long integrated seismic risk management into its transportation networks, ensuring resilience against frequent earthquakes [3]. New Zealand’s Ministry of Transport, along with agencies like Waka Kotahi (New Zealand Transport Agency), has been actively investing in research that supports climate adaptation and mitigation efforts. New Zealand is investing on sustainable materials and practices that reduce the carbon footprint of infrastructure projects, as well as strategies to increase the resilience of pavements and other assets under extreme weather conditions [4]. These global initiatives underscore the shared international commitment to enhancing infrastructure resilience.

In the United States, there are risk management guidelines prepared by the AASHTO and the FHWA. The AASHTO Guide for Enterprise Risk Management (2016) includes a range of insightful studies by the FHWA [5]. The AASHTO guide (2016) sets the foundation with its focus on risk identification, analysis, prioritization, and a multi-tactic approach for managing risks, emphasizing the need for adaptability and continuous monitoring [5]. It details five specific tactics for managing risks: tolerating (accepting the risk and its impacts), treating (mitigating or reducing the risk), transferring (shifting the risk to another party, typically through insurance or contracts), terminating (eliminating the risk or its causes), and taking advantage of (leveraging the risk for opportunities). The FHWA’s Vulnerability Assessment and Adaptation Framework (VAAF) was first published in 2012 [6]. The VAAF was updated in 2018, providing in-depth guidance for agencies and their partners to assess the vulnerability of transportation infrastructure and systems. Additionally, a scalable, customizable GIS-based web tool implementing this methodology was developed. The FHWA’s 2017 study furthers this by detailing the integration of risk management into transportation asset management plans, highlighting the importance of a systematic approach in considering various challenges [6]. It includes examples of how different U.S. and international transportation departments incorporate risk management into their asset management strategies. Building on these previous efforts, FHWA developed a study in 2022 with a comprehensive framework for risk-based asset management under both normal and extreme conditions, integrating quantitative assessment and economic impact analysis [7].

While the principles of sustainability and resilience are widely recognized and management practices are in place, they remain insufficient for addressing challenges posed by the evolving threats of climate change on roadway infrastructure systems. The existing management practices often lack the integration of adaptive strategies necessary to respond effectively to an increasing frequency, intensity, and complexity of climate-related impacts.

1.1. Research Problem

The research problem centers on the increasing vulnerability of roadway infrastructure to the escalating impacts of climate change. Previous research studies have documented the inadequacies of traditional infrastructure management approaches in responding effectively to the increasing frequency and severity of climate change events [6]. Existing infrastructure management practices are largely reactive and often limited to individual asset groups, making them inadequate for addressing the complex, interconnected risks posed by evolving climate conditions [8,9]. Moreover, risk assessment and management should be interconnected at the network and project levels [10,11]. Therefore, the lack of a cohesive, system-wide approach to integrating risk assessments and asset management results in insufficient preparation for cascading climate impacts across roadway infrastructure assets. There is a need to address this problem and develop a comprehensive, proactive approach that integrates climate adaptation strategies and risk assessments into asset management practices throughout the asset lifecycle [12].

1.2. Research Objective

The main objective of this research is to develop a comprehensive asset risk management framework for roadway infrastructure that incorporates resilience standards for assessing and enhancing functional capacity after extreme weather events. This involves evaluating the impact of climate change on infrastructure durability, establishing and validating specific resiliency standards, integrating advanced technological tools for accurate risk assessment, and proposing climate adaptation strategies for transportation planning and management. This research also aims to propose climate adaptation strategies for planning and management, demonstrating their effectiveness in improving infrastructure resilience, functionality, and sustainability.

1.3. Research Significance and Relevance

Technological advancements have significantly improved data collection and post-event condition assessments of roadway infrastructure, with sophisticated monitoring techniques and diagnostic tools providing high-fidelity data to evaluate structural integrity and functional capacity. Despite the wealth of data available, integrating climate data and risk assessment into the asset management process remains a substantial challenge.

The significance and relevance of this paper lie in its potential to address this integration challenge by proposing a comprehensive asset risk management approach with an actionable framework to address climate change. This framework will provide transportation agencies with a robust tool to better manage the impacts of extreme weather events. Furthermore, it will contribute to the development of more resilient and adaptive roadway infrastructure systems, which are essential for maintaining economic stability, environmental sustainability, and public safety.

1.4. Organization of This Paper

This paper presents a climate adaptation asset risk management approach that considers roadway infrastructure resilience as a central component, integrating systematic risk assessment into asset management practices to enhance the durability and adaptability of roadway infrastructure systems in the face of increasing extreme weather events.

This paper is organized into seven sections: (1) this introduction; (2) the research methodology, (3) a literature review of risk assessment research efforts, the state of practice, and tools, identifying research gaps, (4) a description of the asset risk management approach for resilient roadway infrastructure, (5) a case study, (6) a discussion, and (7) the conclusion.

2. Research Methodology

The research methodology involved an extensive literature review to identify existing risk assessment practices and gaps in risk asset management as relates to climate adaptation strategies. The review covered existing guidelines, standards, methods, and tools for risk assessment and asset management of roadway infrastructure. Documents were sourced from organizations such as the NCHRP, FHWA, and U.S. Departments of Transportation (DOTs), and relevant international reports and manuscripts were sourced from peer-reviewed technical journals. A comparative analysis of the findings in the literature was conducted to assess the capabilities and limitations of current practices, identifying gaps in the state of practice based on expert judgment. Based on these findings, a tailored asset risk management approach was prepared, incorporating insights from expert feedback. The applicability of the approach and specific framework was demonstrated through a case study which provided recommendations for implementation. The case study was followed by a discussion of the challenges and proposed climate adaptation strategies.

The research methodology can be broken down into the following seven systematic steps, each designed to build upon the previous phase to create a robust framework for climate adaptation and the integration of risk assessments into asset management.

Step 1: Literature Review: The first step involved a comprehensive literature review to achieve a solid foundation on the current knowledge and practices related to risk assessment and asset management, especially as they relate to climate adaptation. Key sources included guidelines, standards, methodologies, and tools from reputable organizations such as NCHRP, FHWA, U.S. DOTs, and various international agencies, along with peer-reviewed articles from technical journals. This phase aimed to identify what practices are currently in place, the extent of climate adaptation integration, and any recognized gaps in these methodologies.

Step 2: Identification and Comparative Analysis: In this phase, findings from the literature were systematically organized and analyzed through a comparative approach. The goal was to assess the strengths, weaknesses, and capabilities of existing risk assessment practices and asset management approaches. Expert judgment was applied to evaluate where current practices fall short, particularly regarding resilience and climate adaptation, thereby identifying critical gaps in the state of practice.

Step 3: Development of a Tailored Performance-Based Asset Risk Management Approach: Using insights gathered from the comparative analysis, a customized asset risk management approach was designed. This approach incorporated specific elements that address identified gaps in resilience and climate adaptation, aligning with feedback gathered from industry experts. This step ensured that the approach was not only comprehensive and theoretically sound but also adaptable for practical use by infrastructure agencies.

Step 4: Expert Feedback and Refinement: To enhance the relevance and applicability of the tailored approach, targeted feedback was solicited from domain experts. Engaging these stakeholders allowed for iterative adjustments to the framework, ensuring it met real-world needs and constraints. This feedback loop refined the framework to be feasible, actionable, and adaptable, thereby increasing the likelihood of successful implementation.

Step 5: Case Study Application: A real-world case study was then conducted to demonstrate the practical utility and flexibility of the proposed framework. This involved applying the risk-based approach to a specific context, simulating various climate-related scenarios to test how the framework would guide decision-making and prioritize resilience investments. The case study provided concrete insights and validated the approach by demonstrating its effectiveness in an applied setting.

Step 6: Development of Recommendations and Adaptation Strategies: Based on the findings of the case study, specific recommendations were developed for implementing the framework in diverse contexts. This included a discussion of the challenges encountered during the case study, such as data limitations and resource constraints, as well as strategic solutions to address these issues. The adaptation strategies were designed to provide actionable steps for integrating climate resilience into asset management practices across agencies.

Step 7: Final Discussion and Future Directions: The final step involved a comprehensive discussion of the research findings, challenges, and implications for future work in this field. Key points included strategies for overcoming implementation barriers, such as enhancing data collection methods, fostering inter-agency collaboration, and developing standardized resilience metrics. This phase highlighted the broader significance of this research and suggested pathways for further innovation and refinement in climate adaptation for asset management.

By following these structured steps, the research methodology ensured a rigorous, thorough exploration of climate adaptation within asset management, culminating in a well-defined framework and actionable strategies for enhancing the resilience of transportation infrastructure.

3. Literature Review

The literature review is organized into four key sections to provide a comprehensive overview of the current landscape in risk assessment for asset management. These sections include the following: (1) research efforts on risk assessment, offering insights into foundational studies and theoretical frameworks; (2) the state of practice in risk assessments, detailing how various organizations implement risk assessment in asset management; (3) an evaluation of existing risk assessment tools, examining their applicability, strengths, and limitations; and (4) an identification of research gaps, highlighting areas that require further investigation to address climate adaptation in infrastructure asset management.

3.1. Research Efforts on Risk Assessment

Risk assessment guidelines emphasize the importance of integrating risk management as a core competency within transportation agencies, akin to financial management and public safety protocols [5]. Effective risk management processes involve engaging stakeholders, providing appropriate training, and ensuring continuous support from experts to foster a culture of risk awareness [13]. Furthermore, the incorporation of risk management into transportation asset management plans allows for a structured approach to identifying risk mitigation strategies, thus enhancing the resilience of roadway systems. By utilizing best practices and case studies, these guidelines aim to improve decision-making and resource allocation, ultimately leading to safer and more efficient transportation networks.

Over the years, NCHRP projects have had a significant contribution in the development of risk assessment frameworks. NCHRP Report 706 (2011), which lays the groundwork by outlining methods for integrating risk and data management in transportation agencies [14]. This early work emphasizes establishing risk tolerance and developing mitigation strategies, setting the stage for performance-based resource allocation in roadway networks. Progressing to 2016, NCHRP 08-36 Task 126 presents a novel approach to risk assessment, introducing a spreadsheet-based tool designed for transportation management [15]. This report focuses on the practical application of risk management, highlighting the preference for accessible tools and the need for dedicated risk departments within transportation organizations.

NCHRP Report 859 (2017), led by Chang et al., adds a new dimension by quantifying the consequences of delayed maintenance of highway assets. Providing a structured framework, this report aids agencies in making informed decisions for highway system preservation, crucial for maintaining the integrity and safety of roadway networks [16]. In 2021, NCHRP 20-123(04) emphasized the critical importance of integrating risk management into transportation maintenance practices, specifically highlighting financial risk assessments and the need for improved risk communication [17]. This report reflects on the multifaceted nature of risk management in roadway networks, addressing diverse challenges and stakeholder priorities. Moreover, NCHRP Report 985 (2022) offers guidance to transportation agencies on integrating performance, risk, and asset management, focusing on enhancing decision-making and resource allocation [18]. This report’s emphasis on data-driven strategies and policy underscores the complexity of managing roadway networks.

In 2022, NCHRP Research Report 986, authored by Varma and Proctor, explored the practical implementation of the AASHTO Guide for Enterprise Risk Management within state Departments of Transportation [19]. This study provides insights into various risk management initiatives, highlighting the integration of Enterprise Risk Management into the operational framework of transportation agencies. NCHRP Report 366, again by Varma et al., provides a comprehensive framework for risk assessment, focusing on balancing risk, cost, and performance [1]. The inclusion of real-world case studies in this report demonstrates the application of risk management principles in the context of roadway networks. Following this, NCHRP Report 1014 (2023) by Pena et al. discusses the development of an All-Hazards Risk and Resilience Analysis framework, advocating for the adoption of a unified glossary and structured RR assessment processes [20]. This report’s focus on the transportation industry, particularly on roadway networks, aligns with the earlier studies’ emphasis on comprehensive data collection and effective communication. Finally, NCHRP Research Report 1066 (2023), also led by Varma et al., offers extensive research on risk management techniques specifically tailored for transportation asset management [9]. Covering a range of studies and pilot tests, this report underscores the application of various strategies for evaluating and mitigating risks associated with transportation assets, including roadways.

The methods employed across the NCHRP projects highlight the diversity of approaches used to address risk management over the years. NCHRP Project 8-60 (2010) uses the OpenVC dataset for model training and improvement, along with Range Estimating and Applied Contingency Analysis to manage uncertainty and enhance project performance [21]. A year later, NCHRP Report 706 (2011) leveraged GIS, business intelligence tools, and knowledge management systems to enhance data sharing and resource allocation within transportation agencies [14]. NCHRP 20-24 (74) (2011) uses Monte Carlo analysis to quantify and analyze risks, supporting DOT executives in advancing risk management strategies through leadership [10].

In 2016, NCHRP Project 8-36 (126) employed surveys and interviews with industry leaders to develop a risk register tool that aids ERM but requires leadership for full adoption [21]. In the same year, NCHRP 20-24 (105) utilized a decision tree tool for managing complex risk decisions, integrating costs and probabilities to strengthen Enterprise Risk Management frameworks [22]. Three years later, NCHRP 20-07 (Task 378) (2019) implemented its methodology through a demonstration project using transportation agency data and NOAA climate records to assess risk management for bridges [23].

In 2021, NCHRP 20-123 (04) applied the AASHTO ERM Portal to guide risk management strategies, conducting a gap assessment to evaluate the state of practice in DOTs [17]. NCHRP Document 366 (2023) relies on historical cost data for materials such as steel and concrete, analyzing lowest historic trends, average past trends, and highest growth rates to support risk assessments in asset management [1]. Similarly, NCHRP Research Report 1014 (2023) utilizes a risk assessment methodology that included asset characterization, threat assessment, and vulnerability assessment, utilizing publicly available spatial data from agencies like the USGS, NOAA, and FEMA to enhance risk and resilience assessments [20]. The most recent report, NCHRP Report 1066 (2023), uses a combination of CAGR, ETS, and Monte Carlo simulations to forecast revenue and funding risks for transportation assets [9].

3.2. State of Practice on Risk Assessment

Risk assessment practices across countries have similar approaches, emphasizing the identification of climate change impacts on roadway infrastructure, evaluating the severity of these impacts, and formulating mitigation strategies for high-risk factors. The Netherlands, France, Japan, and New Zealand, among other countries, have developed frameworks that integrate climate risk assessments into asset management processes. As described in the introduction, these international approaches involve systematic evaluations of potential risks, using methodologies that prioritize adaptive strategies to enhance resilience. The focus is on understanding vulnerabilities, assessing the likelihood and consequences of various climate-related events, and applying performance-based objectives to infrastructure management. This approach aims not only to protect individual assets but also to ensure the overall functionality and safety of interconnected transportation systems.

In line with international practices, U.S. efforts foster resilience by incorporating both short-term responses and long-term adaptation measures within comprehensive risk management frameworks. Most Departments of Transportation (DOTs) perform risk assessments by using simple risk registers relying on collective judgment. Some DOTs are expanding their initiatives and incorporating quantitative methodologies for risk and resilience assessments. There is not a standard methodology for performing quantitative risk and resilience assessments for transportation agencies. DOTs assess risks by accessing data, developing performance measures, and performing cost–benefit analyses. This assessment helps in understanding the potential impacts of risks and supports decision-making processes. More generally, these indices can be viewed as measures of utility and are beginning to be incorporated into existing bridge management systems and processes.

Figure 1 illustrates the extent of risk management practices across nine U.S. Departments of Transportation leading the implementation effort: the California Department of Transportation (Caltrans), Florida Department of Transportation (FDOT), Texas Department of Transportation (TxDOT), Minnesota Department of Transportation (MnDOT), Nevada Department of Transportation (NDOT), New Jersey Department of Transportation (NJDOT), New York State Department of Transportation (NYSDOT), Virginia Department of Transportation (VDOT), and Washington State Department of Transportation (WSDOT).

The risk management practices shown in Figure 1—including policies, guides, qualitative and quantitative analysis methods, and tools—were derived from a comprehensive review of documents from various DOTs [1,24,25,26,27,28]. All nine DOTs perform qualitative analysis as part of their risk management, while 89% implement risk management guides and 78% conduct quantitative analysis. Additionally, 56% maintain a risk list, suggesting that more than half actively track identified risks. In contrast, only 33% adopt formal policies and tools for quantitative analysis, indicating a gap in policy frameworks and toolsets for quantitative methods.

3.3. Risk Assessment Tools

This subsection describes examples of risk assessment tools developed by NCHRP projects, FHWA, and those in practice at state DOTs. NCHRP projects have introduced tools such as risk registers, Monte Carlo simulations, and geospatial databases to support comprehensive risk assessment and resource allocation. In practice, state DOTs have implemented spreadsheet-based models, prioritization frameworks, and vulnerability assessments to address risks to pavements, bridges, and road users. This review highlights how these tools contribute to managing risks and enhancing the resilience of transportation infrastructure.

NCHRP Project 8-60 (Guidebook on Risk Analysis Tools and Management Practices to Control Transportation Project Costs), published in 2010, addresses the increasing complexity and challenges in transportation projects, offering strategic tools for risk analysis and fostering change in management practices [21]. This report emphasizes the importance of addressing uncertainty and providing tools to support project performance. In 2011, NCHRP Report 706 (Uses of Risk Management and Data Management to Support Target-Setting for Performance-Based Resource Allocation by Transportation Agencies) enhanced highway research by promoting the use of IT tools for data sharing and implementing geodatabases to support resource allocation [14]. Additionally, NCHRP 20-24 (74) (Executive Strategies for Risk Management by State DOTs) outlines national guidance on risk management for DOTs, particularly through leadership and Monte Carlo analysis, providing key insights for advancing risk management strategies [10].

In 2016, NCHRP Project 8-36 (126) (Development of a Risk Register Spreadsheet Tool) emphasized the need for formal Enterprise Risk Management (ERM) processes, developing a risk register tool that supports effective risk management while noting the need for executive leadership to ensure full effectiveness [15]. Similarly, NCHRP 20-24 (105) (Launching U.S. Transportation Enterprise Risk Management Programs), published in the same year, focused on enhancing health, safety, and environmental performance through robust risk assessment frameworks designed to meet organizational objectives [22].

NCHRP 20-07 (Task 378) (Assessing Risk for Bridge Management), published in 2019, provides practical guidelines for state DOTs to implement risk-based asset management, using agency data, bridge inventories, and NOAA climate data to help manage risks to bridge infrastructure [23]. NCHRP 20-123 (04) (Development of a Risk Management Strategic Plan and Research Roadmap), released in 2021, reinforces the incremental approach necessary for risk management, emphasizing organizational alignment and using the AASHTO ERM portal for strategy implementation [17].

The most recent reports, NCHRP Document 366 (Risk Assessment Techniques for Transportation Asset Management: Appendices, 2023), NCHRP Research Report 1014 (Developing a Highway Framework to Conduct an All-Hazards Risk and Resilience Analysis, 2023), and NCHRP Report 1066 (Risk Assessment Techniques for Transportation Asset Management: Conduct of Research, 2023), focus on current and future transportation risks [1,9,20]. NCHRP Document 366 emphasizes on systematic research for solving complex highway-related problems, relying on historic cost data for materials such as steel and concrete [1]. NCHRP Research Report 1014 identifies gaps in state DOT risk and resilience assessments, offering methodologies for vulnerability and threat assessment using spatial and public data from the USGS, NOAA, DHS, and FEMA [20].

The Federal Highway Administration (FHWA) has developed various tools and resources to assist transportation agencies in assessing climate change vulnerabilities and risks. The Transportation Climate Change Sensitivity Matrix summarizes research on how different transportation assets respond to specific climate and extreme weather stressors [29]. The U.S. DOT CMIP Climate Data Processing Tool processes downscaled climate change data at the local level, providing relevant statistics for transportation planning [10]. The Vulnerability Assessment Scoring Tool (VAST) guides agencies through a quantitative, indicator-based vulnerability screening based on exposure, sensitivity, and adaptive capacity [6]. Additionally, FEMA’s HAZUS-MH, a GIS-based tool integrated with ESRI’s ArcGIS, assesses earthquake and flood risks, generating restoration curves and damage estimates for roads and bridges [30].

At the state level, several DOTs have implemented specific risk analysis tools. The Colorado DOT developed a spreadsheet-based risk and resilience tool with a deterministic model to evaluate risks to pavements, bridges, and culverts from floods, rockfall, and scour [1,24,25]. The Florida DOT created bridge risk models that assess risks from hurricanes, tornadoes, wildfires, floods, and other threats, using a monetary-benefits approach [31]. The Minnesota DOT (MnDOT) uses a risk-based prioritization tool called BRIM, which employs the Bridge Performance Index (BPI) to measure utility and prioritize bridge maintenance [27]. The MnDOT also uses risk checklists for cost estimation and project management, addressing technical risks like incomplete designs, right-of-way errors, and geotechnical issues [32].

The Ohio DOT’s Ohio Bridge Condition Index (OBCI) evaluates bridge performance by considering the direct and indirect consequences of maintenance strategies, guiding prioritization based on physical integrity and maintenance costs [25]. The Pennsylvania DOT’s spreadsheet tool, part of its Extreme Weather Vulnerability Study, factors in AADT and functional class for each roadway segment to assess vulnerability to extreme weather [25]. Vermont’s Transportation Resilience Planning Tool (TRPT) uses web-based maps to visualize flood risks to embankments, culverts, and bridges [33]. The New Mexico DOT (NMDOT) employs an equity-based approach to assess road safety for vulnerable users, integrating demographic factors such as race, income, and air quality into crash data analysis [34]. The Washington State DOT (WSDOT) uses probabilistic risk management tools like Monte Carlo simulations and decision trees to evaluate cost and schedule risks through multiple project scenarios [28].

3.4. Research Gaps

Despite significant advancements in risk assessment and management for roadway infrastructure, several research gaps remain, as identified through a comprehensive review of risk assessment and asset management practices. Key gaps include the need for rapid recovery strategies, enhanced communication and workforce support within transportation agencies, and better integration of risk management into daily operations.

Existing asset management practices falls short in offering an integrated asset risk management approach, especially regarding extreme weather risks and socio-economic impacts. Additionally, there is a need for standardized methodologies for corridor selection, risk mitigation in large-scale infrastructure projects, and coordination between land use forecasting and infrastructure planning. Addressing these gaps is critical for developing more effective, climate-adaptive, and resilient roadway infrastructure.

Overall, the challenge lies in synthesizing diverse risk data and applying advanced methods to develop cohesive climate adaptation strategies. The goal is to maintain roadway infrastructure functionality and safety amid increasingly volatile climate environment. To achieve this, there is an urgent need for an asset risk management approach for addressing both the short-term effects and long-term consequences of evolving climate-related hazards.

4. Asset Risk Management Approach for Resilient Roadway Infrastructure

Risk is defined by three components: hazard, exposure, and vulnerability. Accurately characterizing risk requires hazard models that capture the spatio-temporal variations in hazard intensity. A risk management approach for resilient roadway infrastructure in the context of climate change involves identifying and assessing risks by evaluating the likelihood and severity of climate-related damage to assets. Treating the exposure of individual roadway assets—such as pavements, bridges, or culverts—as isolated events can result in inflated or inaccurate risk assessments. A strategic, integrated approach involves spatially aggregating risks across asset components using segmented management units, such as transportation corridors or routes to assess their vulnerability and resilience.

The interdependence of transportation asset components leads to the implementation of a system-of-systems management approach. This approach provides a framework with methods and tools for prioritizing investments and formulating climate adaptation strategies to protect infrastructure, mitigate risk, and ensure the sustainability of services provided by transportation systems in the face of uncertainties. Risk assessments of potential vulnerabilities in transportation assets due to climate change impacts and other threats need to be blended into an integrated system-of-systems decision-making climate adaptation asset management process, as shown in Figure 2.

A system-of-systems performance-based asset risk management approach integrates various elements to support investment prioritization for infrastructure resilience. At the core of this approach are the Asset Database, which contains asset characteristics by corridor, and the Risk Database, which registers hazards and damages. These databases are supported by an Asset Inventory and a Common Reference Location System (GIS), forming the foundation for the analysis modules. Inputs of Climate Information and Lessons Learned from Experience contribute to assessing vulnerability and resilience, accounting for exposure, sensitivity, and adaptive capacity of roadway assets.

This decision-support framework is driven by policies and objectives that set target objectives and formulate budgets for investments, guiding the analysis modules for needs, scenario vulnerability, and multi-objective investment analysis. The results of these analyses feed into report modules including condition and risk assessments, gap–needs analysis, vulnerability impact, climate adaptation scenario analyses, and multi-objective investment reports, all of which provide critical information for decision-making. Ultimately, this integrated performance-based “system-of-systems” approach seeks the prioritization of investments, in alignment with the goal of achieving sustainable and resilient roadway infrastructure at the local, state, regional, and national levels.

Key performance indicators (KPIs) could be adopted to measure the benefits of adopting the proposed system-of-systems performance-based asset risk management approach. The Risk Reduction Index (RRI) measures the percentage reduction in identified risks to critical assets, indicating how effectively the methodology mitigates vulnerabilities to climate-related risks. Complementing this is the Infrastructure Resilience Score (IRS), a composite metric assessing resilience by factoring in exposure, sensitivity, and adaptive capacity, which quantifies improvements in resilience after the framework’s implementation. Resource Allocation Efficiency (RAE) gauges the proportion of the budget directed to high-risk assets, ensuring that resources are prioritized effectively toward critical areas. Additionally, the Condition Improvement Rate (CIR) captures the rate of improvement in asset conditions post-intervention, demonstrating the impact of prioritized actions on infrastructure health. The Climate Impact Vulnerability Score (CIVS) measures the susceptibility of infrastructure assets to environmental conditions that increase the risk of damage. The Climate Adaptation Readiness Index (CARI) highlights the percentage of assets equipped with climate adaptation measures, providing an overview of how prepared the infrastructure is to handle climate risks. Further, Response Time for Risk-Based Prioritization (RTRP) tracks the average time needed to assess, prioritize, and act on high-risk assets, evaluating the framework’s responsiveness. In cases where infrastructure is impacted by climate events, the Asset Recovery Time Post-Disruption (ARTPD) measures the time required for assets to return to functional operation, indicating the resilience and efficiency of pre-planned adaptive strategies.

These KPIs, as suggested by the authors, offer a multi-dimensional evaluation of the framework’s effectiveness in mitigating climate risks, improving resilience, and ensuring efficient resource allocation. They also enable ongoing monitoring and adjustment, facilitating a dynamic and data-driven approach to infrastructure asset management.

The following sections provide insights into the key components of this approach, including climate information; risk assessment, vulnerability, and resilience; risk assessment in the asset management process; and project prioritization considering climate risk factors.

4.1. Climate Information

Reliable climate data are essential for evaluating vulnerabilities and risks, enabling transportation agencies to develop robust adaptation strategies. Various datasets provide critical insights, helping to assess the potential effects of climate change on roadway infrastructure.

NASA Earthdata offers a comprehensive view of climate indicators in the United States, as provided by the U.S. Environmental Protection Agency (EPA). It combines data from natural sources, like ice cores and tree rings, with information from modern tools such as satellites, illustrating how climate changes influence infrastructure risks [35]. Similarly, the United Nations’ Climate Action platform presents reports on recent climate challenges, global mitigation strategies, sustainable development goals, and initiatives under the Paris Agreement of 2015, supporting informed decision-making in sustainable infrastructure planning [36].

The National Oceanic and Atmospheric Administration’s (NOAA) Climate Data Online (CDO) provides access to historical weather and climate data, which are useful for understanding past climate trends and impacts. CDO includes measurements of temperature, precipitation, wind, and 30-year climate normals, offering a detailed overview of climate patterns that have influenced infrastructure over time [37]. The Climate Change Knowledge Portal (CCKP), developed by the World Bank, offers global data on both historical and projected climate impacts, vulnerabilities, and adaptation actions. It features country-specific and watershed-based data, making it a valuable tool for localized risk assessment [38].

The U.S. Climate Resilience Toolkit’s Climate Explorer presents climate projections through 2100 for U.S. counties, showing variables like temperature and precipitation under both lower- and higher-emission scenarios. This tool helps transportation agencies visualize potential climate impacts, aiding long-term risk management and resilience planning [39]. By integrating these diverse datasets, agencies can better prioritize resources, refine maintenance strategies, and develop risk management plans that enhance infrastructure sustainability and resilience.

4.2. Risk Assessment, Vulnerability, and Resilience

Risk assessment in transportation infrastructure projects involves evaluating identified risks based on their potential impact and likelihood of occurrence. This process aims to understand the severity and probability of each risk, facilitating prioritization and determining the appropriate level of attention each risk requires. Various methods are employed in the risk identification phase to ensure a comprehensive understanding of potential threats. For example, the Crawford Slip Method is a group-based approach that allows participants to list potential risks independently, promoting unbiased categorization. Expert Interviews leverage the knowledge and experience of subject matter experts to anticipate project risks. The Red Flag List, developed by cross-functional teams, identifies critical elements affecting cost and schedule, revisited throughout the project lifecycle to enhance management. The Risk Breakdown Structure systematically categorizes risks by type, such as environmental or design-related factors, while the Risk Comparison Table facilitates qualitative comparisons, aiding in prioritization.

Once risks are identified, assessment methods are applied to evaluate their impact and likelihood, using both qualitative and quantitative tools. Risk assessment largely rely on vulnerability assessment. Vulnerability is defined as the degree to which a system is susceptible to and unable to cope with adverse effects of climate change, including variability and extremes. This concept is characterized by three main components—exposure, sensitivity, and adaptive capacity—which together determine how a system responds to climatic stressors. Techniques like Monte Carlo simulations estimate the probability of different outcomes through repeated random sampling, providing insights into uncertainties. The Probability and Impact Matrix categorizes risks by likelihood and impact on cost, schedule, and scope, aiding visualization and prioritization. Risk Priority Ranking further refines the process by ranking risks based on their qualitative or quantitative analysis, considering their potential effects on project objectives. Risk Workshops encourage collaboration among estimators, project team members, and experts to ensure comprehensive qualitative and quantitative analysis, while three-point estimates offer a range of estimates to provide a better understanding of uncertainties associated with specific risks.

The outcome of the risk assessment is a prioritized list of risks—often categorized based on their significance—which contributes to the resilience of the infrastructure. Resilience is defined as the ability to prepare for, adapt to, and recover rapidly from disruptions and changing conditions. To manage these risks effectively, decision makers use tools like the risk register, a dynamic document that tracks, addresses, and adapts to changes in identified risks. SWOT Analysis serves as an analytical tool to evaluate strengths, weaknesses, opportunities, and threats, supporting the development of a comprehensive risk management plan. This plan includes strategies for managing risks, involving development, implementation, and continuous monitoring to mitigate potential impacts. Risk maps further aid decision-making by graphically representing prioritized risks based on likelihood and impact, highlighting areas where mitigation can reduce consequences.

Overall, the identification and assessment of risk assist transportation agencies in developing informed, proactive strategies to address risks, ultimately enhancing project resilience and sustainability.

4.3. Risk Assessment in the Asset Management Process

The integration of risk assessment within the asset management process is central to ensuring that every stage is informed by potential threats and vulnerabilities. This comprehensive approach spans from the initial setting of goals to continuous adaptation, making the integration of risk assessment a foundational element of the decision-making asset risk management framework shown in Figure 3. The framework is composed of seven steps: (1) Goals and policies, (2) Asset inventory, (3) Condition assessment, (4) Risk assessment, (5) Needs (gap) analysis, (6) Scenario analysis, and (7) Continue monitoring, adaptive management, and policy integration.

The asset management process begins with defining goals and policies that provide a strategic direction for managing infrastructure assets. Even at this initial step, risk assessment plays a vital role by aligning these goals with potential risks, especially those posed by climate change and other external threats. Incorporating a risk-focused perspective early in the management process ensures that the goals are not only focused on asset performance but also on resilience to evolving hazards. Once the goals are established, the process moves to the Asset Inventory. This risk-informed assessment management approach emphasizes resilience by registering critical vulnerable assets in the Asset Inventory. The data in the Asset Inventory are not static and should be interpreted through a lens of risk, making evaluations more forward-looking.

In the condition assessment step, the current state of assets is evaluated alongside their remaining service life. Condition assessment is an integral part of the framework, as it helps prioritize assets based on their vulnerability and importance. Identifying which assets are most susceptible to risks—whether due to location, design, or existing condition—guides decision makers in focusing their efforts where they are needed most. This step ensures that, in condition assessments, the information in the databases is up to date.

Risk assessment becomes a dedicated step in the process, where potential threats are analyzed in greater detail. This step includes formulating climate scenarios and conducting risk quantification to assess gaps and identify needs. Risk assessment continues to play a key role in the needs analysis step, which identifies discrepancies between current asset conditions and the desired performance levels. By integrating risk data into this analysis, decision makers can understand not just performance gaps but also the risks associated with these gaps. The needs analysis allows agencies determine plan a strategy and estimate a budget in order to carry out a program for addressing gaps and meeting goals. It is followed by a scenario analysis step that builds on risk assessment data to explore various future outcomes and budget allocation strategies. Risk scenarios simulate different conditions, providing insights into potential impacts on infrastructure performance. Tools such as probability and impact matrices, Monte Carlo simulations, and AI-driven predictive models can be used to estimate risk severity and prioritize interventions. For instance, machine learning-based models like neural networks and random forests are commonly used to predict asset conditions by analyzing historical data on maintenance, environmental factors, and performance patterns, allowing agencies to forecast when maintenance or replacement will be needed. Similarly, models such as support vector machines and gradient boosting can be applied to predict the likelihood of asset failure based on factors like age, material, and environmental exposure, enabling prioritized interventions.

Climate impact forecasting models, such as recurrent neural networks (RNNs) and long short-term memory (LSTM) networks, help predict the effects of climate patterns on infrastructure resilience, using historical climate data to forecast potential future impacts. Asset deterioration models, including time series forecasting and Markov chains, assess gradual wear and deterioration, assisting in planning optimal intervention times based on past conditions and usage data. Natural language processing (NLP) models, such as BERT and GPT, analyze inspection reports and sensor data to identify emerging risks in real time, enabling more effective risk monitoring. Spatial risk models, using convolutional neural networks (CNNs) integrated with geographic information system (GIS) data, analyze satellite imagery and geospatial patterns to pinpoint areas susceptible to climate-related events like flooding and erosion, supporting localized risk mitigation. Additionally, reinforcement learning can enhance Monte Carlo simulations by adjusting simulation parameters based on evolving data trends, offering refined predictions for complex and uncertain risk scenarios, such as the effects of climate change on asset performance. Together, these AI-driven models provide agencies with powerful tools for proactive decision-making and long-term resilience planning.

The quantitative and scenario-based analysis ensures that risk considerations are fully integrated into the broader asset management strategy. It helps align resource allocation with the most significant risks, ensuring that funds are directed toward assets and strategies that address critical vulnerabilities.

In the final step, continuous monitoring and adaptive management ensure that risk assessment remains an ongoing component of the asset management process. Collected data can be analyzed using AI tools to update deterioration models, refine risk projections, and recommend adaptive strategies based on real-time data. This adaptive approach allows for immediate adjustments as new information emerges, ensuring that risk management evolves with changing conditions. For example, as updated risk data become available, agencies can modify their strategies to enhance resilience, aligning them with the latest insights and organizational goals.

Throughout this cyclical process, it is observed that risk assessment is not limited to a single step; it permeates every phase of the asset management process. By integrating risk considerations from start to finish, the process becomes more proactive, aligning infrastructure management with dynamic external conditions and supporting both short- and long-term performance. This holistic approach ensures that roadway infrastructure assets are managed to withstand both anticipated and unforeseen challenges, making resilience a central objective of asset management practices.

4.4. Project Prioritization Considering Climate Risk Factors

The integration of risk assessment into asset management at the strategic level is not sufficient for the implementation of climate-adaptative strategies. A project-level analysis is essential for prioritization to address climate change threats. Risk-based prioritization assesses the likelihood and consequences of potential threats, such as extreme weather events, and prioritizes roadway transportation routes with the highest exposure. Criticality assessment evaluates the importance of a route to the overall roadway system, giving priority to those essential for economic, social, or emergency services. The prioritization process at the project level involves four main steps, as illustrated in Figure 4.

The prioritization process begins with the identification of risk factors in Step 1, emphasizing the need to determine key risks, including those related to climate change. In Step 2, data integration and quality assessment are carried out by merging diverse data sources, evaluating data quality, and addressing challenges related to data consistency. The process advances to Step 3, where predictive analytics are employed to analyze risks, providing insights into potential impacts. The final step, Step 4, involves risk-based prioritization of investments and the implementation of climate-adaptive approaches in asset management practices.

The outcomes of the prioritization process include risk-based and criticality assessments, focusing on addressing the most vulnerable and essential routes, and cost–benefit comparison analyses on implementing resilience measures and the benefits of avoiding disruptions and damage, while performance-based prioritization tracks resilience metrics to prioritize routes that perform poorly. The outcomes also incorporate scenario planning, allowing agencies to assess multiple future climate conditions and enabling adaptive prioritization based on evolving risks. Equity and environmental justice prioritization ensure that vulnerable communities are protected, emphasizing fair access to resources and services amid climate disruptions. The following sections elaborate more on the steps and are followed by a case study to illustrate their application.

• Step 1: Identification of Risk Factors

The key objective of this step is to identify and categorize key risk factors impacting roadway infrastructure, including those exacerbated by climate change. Risk identification is mostly performed using a risk register and content analysis tools to extract risk factors from project reports. By analyzing project claim data, such as equipment breakdowns, delays, material shortages, and management issues [40], agencies can identify recurring patterns and root causes of risks, enabling them to proactively mitigate potential threats. Moreover, integrated risk assessment frameworks offer a structured approach to assessing risks by considering various dimensions like economic, operational, social, and environmental factors [41]. The outcome of these efforts will be a detailed, categorized list of risk factors, emphasizing vulnerabilities directly associated with climate change, which will serve as a foundational input for subsequent risk assessment and management processes.

Figure 5 provides a comprehensive overview of the various types of risks that can affect infrastructure projects, emphasizing the importance of a strategic risk management approach to ensure project success.

Figure 5 provides a visual aid for agencies and stakeholders, helping them understand the diverse risk landscape in infrastructure projects. It underscores the need for a systematic approach to risk management, where each category represents a critical aspect of infrastructure development. Risk is categorized into five primary groups: project management and operations risks, design risks, environmental risks, safety risks, and traffic system risks. Each category is distinctly represented by icons and colors, reflecting the diverse range of challenges encountered in infrastructure development.

The following is a description of the risk types and examples:

• Project Management and Operations Risks: This category includes risks related to the administrative and operational processes of a project, which can significantly affect timelines, budgets, and resource management:
  • Access/ROW/Easement: involves risks related to access, property acquisition, relocation, and compensation;
  • Contract: concerns about contract terms, payment conditions, dispute resolution, and termination clauses;
  • Permit: covers issues with the permitting process, such as delays or non-compliance with environmental and building permits;
  • Procurement: involves risks related to bid evaluation, contractor selection, and contract management;
  • Schedule: risks associated with project delays, schedule conflicts, and timing of activities;
  • TCP (Construction Phase): focuses on risks linked to temporary traffic control, including detours, diversions, and work zone safety;
  • Survey: relates to inaccuracies in survey data acquisition and processing.
• Design Risks: These risks address potential issues related to the planning and technical design of infrastructure elements:
  • Corridor/Traffic Design: risks tied to land use, access, and connectivity within a transportation corridor;
  • Roadway Alignment: concerns about the horizontal and vertical alignment of a roadway, which may impact safety and performance;
  • Roadway Design: encompasses specific elements, such as guardrails and other design features;
  • Structural: involves design, material selection, and construction challenges for bridges and other structures;
  • Pavement: covers design, material selection, drainage, and skid resistance;
  • Finishing and Interior Work: relates to the completion of interior and finishing touches on buildings or structures within the project.
• Environmental Risks: These risks pertain to environmental compliance and mitigation:
  • Environmental: includes risks related to air and water quality, noise pollution, hazardous material disposal, and mitigation of environmental impacts;
  • Geotechnical: concerns about unstable soil or rock conditions, foundation design, and earthworks;
  • Hydraulics: focuses on drainage system failures, inadequate flood control measures, and stormwater management issues;
  • Natural Disaster: involves risks from natural events such as floods, landslides, and earthquakes;
  • Ferry: relates to risks associated with the design, construction, and operation of ferry systems.
• Safety Risks: Safety risks emphasize the protection of both workers and the public:
  • Safety: involves risks to worker safety during construction and hazards to public safety due to inadequate traffic control or compliance failures, leading to potential injuries or fatalities;
  • Security/Protection: includes physical security, cybersecurity, and the protection of critical infrastructure.
• Traffic System Risks: This category includes risks associated with traffic management systems and their components:
  • Traffic Control Systems (Except Pavement Markings): covers the design, installation, and maintenance of signals, signs, and lighting;
  • Traffic Control Systems (Pavement Markings): focuses specifically on pavement markings as traffic control devices;
  • Railroad: involves track design, construction, and maintenance risks.
• Utilities Risks include potential disruptions to utility services (e.g., gas, water, electricity, and telecommunications), improper installation or relocation of utility lines, and coordination challenges with utility companies.
• Other Risks: This category encompasses risks that do not fit into any specific category but may still impact the project.

By systematically identifying, classifying, and prioritizing risk factors, agencies can ensure informed decision-making throughout the project lifecycle. Additionally, aligning resources and responses to the unique characteristics of each risk type promotes proactive planning, effective mitigation measures, and successful project outcomes.

• Step 2: Data Integration and Quality Assessment

The prime objective of this step is to integrate diverse data sources and assess the quality and reliability of the data used for risk assessment. Risk assessment demands a wide array of data sources based on the expected anticipated outcomes. The data sources can be broadly classified into temporal and spatial data.

Spatial data, such as access point densities and traffic volumes, can be analyzed to identify corridor segments with higher safety needs, aiding in risk assessment and management in transportation planning [10]. Additionally, real-time data integration poses challenges due to varying data formats and collection cycles between state and local entities, necessitating the development of conversion programs for seamless integration. This comprehensive approach ensures that temporal and spatial data are leveraged to enhance risk management practices in transportation and infrastructure development. Integrating data into risk management poses several challenges, including difficulties in sharing and integrating real-time and local road data due to format disparities and varying collection cycles [13].

Furthermore, the need for continuous updates, processing big data, and preparing for emerging risks are methodological challenges in ideal risk assessment practices [13].

• Data Quality: ensuring the accuracy, consistency, and relevance of data collected by DOTs;
• Integration Tools: artificial intelligence, use of GIS-based tools, risk registers, and climate data processing tools (e.g., U.S. DOT CMIP);
• Challenges: data integration across various sources with different formats and collection cycles.

Quality assessment in risk management involves multiple methods to ensure data accuracy and reliability, crucial for effective decision-making. First, data undergo validation and verification to align with standards, ensuring consistency and identifying discrepancies. Completeness checks address gaps that could undermine assessments, while data quality metrics, such as completeness, timeliness, precision, and accuracy, provide a standardized scoring system for evaluating and prioritizing data sources. Additionally, metadata documentation captures essential contextual details, such as collection methods and data limitations, highlighting potential biases. Sampling techniques and statistical controls help spot-check large datasets, and advanced analytics or AI machine learning algorithms are applied to detect anomalies, particularly in complex data that resist manual review. Audit trails and comparisons with historical data further reinforce reliability by identifying unexpected changes or trends. This multifaceted approach builds a robust quality assessment framework that enhances confidence in risk assessments, ultimately supporting informed decision-making in transportation and infrastructure management.

• Step 3: Risk Assessment Using Advanced Analytical Techniques

The objective of this step is to assess the likelihood and impact of identified risks using advanced analytical techniques and models. A major challenge of this step is to quantify risk. A comprehensive risk analysis model typically includes a likelihood model to assess the probability of hazards, a consequence model to evaluate direct effects and costs, and an impact model to understand indirect effects on the public and environment. The risk metrics are probability of occurrence, consequence of failure, and impact on infrastructure and economic losses.

Predictive analyses such as multi-criteria decision analysis (MCDA), Monte Carlo simulations, decision trees, and scenario analysis provide sophisticated methods for analyzing program and project risks, enabling agencies to quantify the likelihood and impact of potential hazards [42]. The Monte Carlo simulation allows for the evaluation of risks based on factors like costs, time, quality, and road distress.

Integrating machine learning techniques like deep neural networks (DNNs) can enhance the accuracy and flexibility of risk assessment models, particularly in dynamic environments where unexpected events can occur [43]. The k-NN algorithm can be utilized to predict risk data and classify risks impartially, although it requires historical data for accuracy [44].

Quality control in projects remains a significant concern due to a lack of experience and established standards. To address these risks, methods like risk checklist techniques, fault tree analysis, FMECA (Failure Mode, Effects, and Criticality Analysis), and cause/effect diagrams are utilized.

The outcome of Step 3 is a more accurate quantification of risks and their potential impacts on infrastructure, providing a data-driven foundation for decision-making. This process leads to more informed, resilient infrastructure planning and ensures that resources are allocated effectively to address the most critical risks.

• Step 4: Risk-Based Prioritization

This is centered on the integration of comprehensive risk-based prioritization into transportation asset management (TAM). The primary objective is to prioritize resource allocation and develop climate-adaptive risk mitigation strategies in asset management plans, ensuring that roadway infrastructure can withstand both current and future challenges. Key factors in this process include the establishment of a review cycle with annual updates based on new data and evolving conditions. Adaptive management practices are also critical, enabling DOTs to refine and enhance their strategies in response to emerging challenges.

Risk assessment results can be used to prioritize resource allocation using different models. Analytical methods vary from ranking approaches to optimization techniques for prioritizing resource allocation. Ranking approaches are used in several management systems to prioritize funding allocation among management sections in need of treatment. Ranking approaches are usually based on performance indexes based on the agencies’ criterion for establishing priorities. Based on the ranking criterion, a list of candidate sections ranked from the highest to the lowest priority is prepared. Another alternative is to follow a multi-year prioritization approach. In a multi-year prioritization approach, the condition of non-funded pavement sections, as well as funded pavement sections, is projected to the next year of analysis, and a new list of candidate sections is produced each year. On the other hand, the funding allocation problem can be solved using optimization approaches. The two general approaches involve the use of mathematical methods or metaheuristic techniques. Among the mathematical approaches, the techniques that can be used to solve the funding allocation problem are linear programming, integer programming, and dynamic programming, among others.

The other general prioritization optimal approach involves the use of metaheuristic methods which are either local search approaches or population-based approaches. In cases where the solution space is large, there may be more than one combination of projects that provides the optimal or near-optimal solution. In such cases, the use of optimization techniques that involve decision-making tools becomes useful. When there are multiple objectives to be addressed, goal programming is an alternative to model this type of problem. Goal programming is a technique that provides the ability to attempt meeting several objectives simultaneously. This technique is a form of linear programming that provides recommendations for actions regarding either minimizing cost or maximizing benefits. For difficult decisions, goal programming can be combined with fair division methods. Fair division methodologies study the problem of allocating a set of indivisible goods to a set of people, called participants, from an envy-free perspective. Envy is generated when a participant believes that the share received is unfair when compared to the share received by the other participants. Chang et al. developed a fair division transportation allocation model (FDTAM) to allocate transportation funds according to individual preferences to prioritize the projects requested for funding, using the criteria of participants to set priorities. Fair division methods make allocations based on proportionality and envy-freeness. Envy-free methods strive to distribute resources based on participants’ preferences; the aim is to assign resources to the participant who shows more desire for that resource.

As the asset risks are identified, evaluated, and prioritized, a summary of the agency’s strategies to minimize or capitalize on them will be provided by a risk-based TAM program. Furthermore, an agency’s uncertainties will be recognized via a risk-based TAM program. These could include ambiguities over unit costs, long-term material performance, future revenues, shifting agency priorities, or other pertinent issues. External hazards that could hinder performance, whether coming from economics, geology, the climate, or the political environment, will be identified via a risk-based TAM program.

The outcomes of the prioritization process include risk-based and criticality assessments, focusing on addressing the most vulnerable and essential routes, and cost–benefit comparison analysis on implementing resilience measures and the benefits of avoiding disruptions and damage, while performance-based prioritization tracks resilience metrics to prioritize routes that perform poorly. The outcomes also incorporate scenario planning, allowing agencies to assess multiple future climate conditions and enabling adaptive prioritization based on evolving risks. Equity and environmental justice prioritization ensure that vulnerable communities are protected, emphasizing fair access to resources and services amid climate disruptions.

5. Case Study

A case study utilizing the general approach described in Section 4.4 is presented in this section. The purpose of this case study is to explore how environmental factors affect bridge deterioration and to provide a systematic way to rank bridges that require adaptation and maintenance based on their exposure to environmental conditions.

5.1. Identification of Risk Factors

The first step identifies climate-related risk factors impacting Florida’s bridges. In this study, average relative humidity and total precipitation were selected as primary environmental risk factors due to their direct impact on corrosion and material degradation.

This case study illustrates the application of k-means clustering with multi-criteria decision analysis (MCDA) to classify bridges into clusters based on their attributes and rank the clusters according to environmental criteria. Instead of employing traditional risk calculation methods, which typically quantify risks based on probabilities and consequences, this approach introduces performance vulnerability scores based on specific environmental criteria: average relative humidity and total precipitation through an Analytical Hierarchy Process (AHP). The advantage of using performance vulnerability scores is that it allows for a more flexible prioritization framework that can be adjusted based on the decision makers’ focus.

5.2. Data Source Integration and Preprocessing

The dataset for this case study was extracted from the National Bridge Inventory (NBI) database, which adheres to the National Bridge Inspection Standards (NBIS). All states adhere to the National Bridge Inspection Standards (NBIS), which established a scale of nine to zero for rating bridge condition with four as follows: Poor Condition—advanced section loss, deterioration, spalling, or scour.

The dataset includes 30,120 bridges from the state of Florida with a variety of attributes. For this study, only bridges rated “Poor Condition” were considered. This selection was based on the NBIS scale, where a rating of 4 indicates “Poor Condition” with advanced section loss, deterioration, spalling, or scour.

As shown in

Table 1. Data variables and clustering levels.

Variable	Description/Levels
mainspanmaterial	Aluminum, concrete, concrete continuous, masonry, other, prestressed concrete, prestressed continuous, steel, steel continuous, timber
lanes	One lane, two lanes, three lanes, More than three
Bridge_Age (Years)	0 to 25, 26 to 50, more than 50
Deck_Area (sft)	0 to 1000, 1000 to 2000, 2000 to 3000, 4000 to 5000, 5000 to 6000, more than 6000
ADT	0 to 50 k, 50 k to 100 k, 100 k–200 k, more than 200 k
structure_length	0 to 200, 200 to 500, 500 to 1000, 1000 to 3000, 3000 to 5000, more than 5000
type_of_work	Replace—load carrying capacity; replace—relocation; widen; widen—deck replace; rehab—deterioration; rehab—widen; deck replace; structural work
ADTT_Percent	1–2%, 2–3%, 3–5%, greater than 5%
avg_relative_humidity	0–50%, 50–75%, more than 75%
total_precipitation (mm/year)	0–500, 500–1000, 1000–2000, 2000–3000, more than 3000

, the variables selected were average daily traffic (ADT), average daily truck traffic (ADTT), bridge age, construction material, deck area, number of lanes, structural length, total precipitation, work done, and average relative humidity.

All variables in the case study were converted into categorical values, or “levels”, to facilitate the analysis. For example, average relative humidity was categorized into levels where 0–50% is Level 1, 50–75% is Level 2, and more than 75% is Level 3.

5.3. Risk Assessment Using Advanced Analytical Techniques

Clustering analysis combined with multi-criteria decision analysis (MCDA) using the Analytical Hierarchy Process (AHP) was applied to process data and calculate the Climate Impact Vulnerability Score (CIVS). The Climate Impact Vulnerability Score (CIVS) is a quantitative measure used to assess the susceptibility of infrastructure assets, such as bridges, to environmental conditions that increase the risk of damage. CIVS leverages data processed through these methods to evaluate exposure to climate-related factors such as humidity, rainfall, temperature, and other environmental stressors. Higher CIVS values indicate greater vulnerability, thereby aiding in the prioritization of assets needing resilience and maintenance strategies. For example, a CIVS of 4.5 might represent bridges located in areas with very high humidity and heavy rainfall, making them more susceptible to corrosion and erosion.

5.3.1. Clustering Analysis

The first step in the analysis was to perform k-means clustering to group the bridges into clusters based on their attributes. Five clusters were selected based on the Elbow Method, which identifies the optimal number of clusters by finding the point where adding more clusters does not significantly improve the model. These clusters grouped bridges with similar vulnerabilities, allowing for a prioritized approach in identifying which bridges are most exposed to environmental risks as an outcome in Step 3. By examining these clusters, patterns based on bridge attributes emerged. For instance, bridges in areas with high precipitation and humidity may belong to the same cluster, indicating a shared vulnerability to environmental degradation.

5.3.2. Multi-Criteria Decision Analysis (MCDA) Using AHP

After clustering the bridges, the Analytical Hierarchy Process (AHP) was used to rank the clusters based on environmental factors: specifically, average relative humidity and total precipitation. These two factors are crucial as they significantly affect the durability of bridges, particularly in corrosion-prone environments where adaptive measures are needed.

5.3.3. Criteria and Pairwise Comparisons

Average relative humidity and total precipitation are the two environmental factors were selected as criteria for the AHP. These factors were given equal weights in the analysis. A pairwise comparison was used to evaluate the relative importance of each factor. In this case, both factors were treated as equally important.

The eigenvector method was applied to calculate the priority vector, which determines the weight of each criterion. In this case, the weights remained equal, reflecting the equal importance of both environmental factors in bridge vulnerability.

5.3.4. Climate Impact Vulnerability Scores and Cluster Ranking

The Climate Impact Vulnerability Score (CIVS) was calculated using the Analytical Hierarchy Process (AHP). After assigning equal weights to these factors, a pairwise comparison matrix was created to evaluate their significance in relation to each other. The matrix was used to generate a priority vector through the eigenvector method, which provided the weights for each criterion. These weights were then applied to the values of each factor within the clusters, and the weighted sum of these factors for each cluster resulted in the final Climate Impact Vulnerability Score, as shown in Figure 6.

The clusters with higher scores indicated greater vulnerability to environmental risks and thus higher priority for maintenance.

5.4. Risk-Based Prioritization

Bridges in CIVS clusters with higher scores are considered more vulnerable to environmental degradation due to higher humidity and precipitation levels. Bridges in these clusters should be prioritized for maintenance and adaptation strategies. Cluster 0 had the highest performance score, indicating that the bridges in this cluster experience the highest levels of relative humidity and precipitation. These environmental conditions contribute significantly to accelerated deterioration, particularly through the process of corrosion.

The performance scores derived from the AHP offered a flexible framework for TAM, allowing transportation agencies to allocate resources effectively based on each cluster’s specific climate vulnerabilities. Bridges in the highest-scoring clusters, which indicate the greatest vulnerability to environmental degradation, were ranked as higher priorities for adaptation interventions as an outcome of Step 4.

5.5. Visualizing Environmental Vulnerability in Clusters

Heatmaps were generated to visualize the relative humidity and precipitation levels for the cluster with the highest CVIS (Cluster 0). These heatmaps provide a clear visual representation of how bridges in this cluster are distributed according to the environmental criteria.

In Figure 7, the heatmap illustrates a wide range of relative humidity values across bridges within Cluster 0. Dark blue shades indicate bridges experiencing higher levels of humidity, while lighter blue shades indicate lower humidity (in Table 1, avg. relative humidity has the following levels: 0–50% = 1, 50–75% = 2, more than 75% = 3). This variation indicates that some bridges in the cluster are more exposed to moisture (Level 3, meaning a relative humidity of more than 75%, indicated in dark blue) potentially leading to a higher risk of deterioration due to humidity-related factors, such as corrosion or material degradation.

In Figure 8, the heatmap for total precipitation shows that most bridges in Cluster 0 fall within a narrow range (mostly between Levels 3 and 4), representing moderate-to-high precipitation exposure. Precipitation is categorized into the following levels: 0–500 = 1, 500–1000 = 2, 1000–2000 = 3, 2000–3000 = 4, more than 3000 = 5. The relatively uniform shading suggests that bridges in this cluster are more consistently exposed to moderate precipitation, increasing the potential risk of damage from factors such as flooding.

6. Discussion

The case study aligns closely with the steps outlined in Section 4.4 of this paper, particularly in demonstrating the application of advanced analytical methods for risk assessment. By integrating k-means clustering and multi-criteria decision analysis (MCDA), the case study exemplifies how data-driven tools can identify vulnerabilities and prioritize infrastructure based on environmental factors, echoing the third step of conducting advanced risk assessments. The use of vulnerability scores, rather than traditional risk metrics, complements the adaptive decision-making focus of the final step, providing a scalable approach to project prioritization that can be extended with additional variables in future studies.

While the case study provides valuable insights into bridge categorization and environmental vulnerability, it has several limitations. The analysis relies on the accuracy and completeness of the dataset, and any missing or misclassified data may affect the clustering and performance scores. The k-means algorithm assumes clusters of similar sizes and may not effectively capture complex, non-linear relationships between variables. The AHP method depends on subjective pairwise comparisons, which may introduce bias based on the decision-maker’s preferences. Additionally, the present study only considers two environmental factors—relative humidity and precipitation—and does not account for other potential risks. To overcome these limitations, future work can incorporate a more comprehensive dataset with additional variables. Moreover, using a combination of advanced analytical tools such as clustering, machine learning, etc., could better capture the complexity of relationships, while applying robust sensitivity analysis could mitigate biases in pairwise comparisons.

This prioritization enables decision makers to focus on the most vulnerable bridges for maintenance or adaptation strategies, aligning resources with areas where climate-related deterioration is the most significant. The framework must not only address the immediate consequences of extreme weather events but also incorporate the long-term effects of evolving climate patterns [22]. This highlights the importance of developing cohesive climate adaptation policies by synthesizing diverse risk data and leveraging technological advancements, ensuring the functionality and safety of infrastructure in increasingly volatile environments [27,45]. For instance, increased precipitation accelerates asset deterioration; for example, this occurred during the 1993 Mississippi River flooding, where adaptation strategies involved relocating infrastructure and employing nature-based solutions [46]. Additionally, regions facing rising sea levels must adopt context-specific measures, as the MaineDOT Pilot study has found that local conditions dictate replacing infrastructure in kind or with longer spans [47,48]. The dual threats of increased precipitation and wildfires also necessitate strategies like improved drainage systems and vegetation management to mitigate flooding and fire risks [17,49]. As climate events such as heat waves and hurricanes intensify, the engineering of assets to withstand higher temperatures and the building of protective barriers for storm surges become vital strategies [6,50]. This integrated approach between asset management and project-level strategies ensures that decision makers can address both immediate climate impacts and long-term vulnerabilities.

Risk-based prioritization enables decision makers to focus on the most vulnerable roadway infrastructure assets, implementing climate adaptation strategies where the risk of climate-related damage is most significant. In hurricane-prone regions, adaptation efforts include constructing protective barriers such as levees and using sacrificial designs for road sections expected to be damaged during severe storms. Additionally, elevating roadways and reinforcing bridge foundations help reduce the impacts of hurricane-induced flooding and wind damage. Similarly, increased precipitation exacerbates asset deterioration, particularly in areas already vulnerable to extreme weather. Heavy rainfall often leads to severe flooding, damaging transportation systems. To mitigate these effects, adaptation strategies involve relocating critical infrastructure from high-risk areas and employing nature-based solutions, like wetlands and retention basins, to manage water overflow and control flooding impacts. The relationship between increased precipitation and wildfire risks is complex. While more rainfall supports vegetation growth, it also creates additional fuel for wildfires during dry periods. To manage this dual threat, infrastructure systems are enhanced with improved drainage systems that handle heavy runoff, thus reducing potential fire risks. Controlled burns and vegetation management are also employed to lower fuel loads and mitigate wildfire risks, while simultaneously preparing for potential post-rainfall flooding. Higher temperatures present additional challenges, as more frequent heatwaves can weaken infrastructure materials. Adaptation measures include engineering assets with heat-resistant materials and implementing intensive maintenance schedules to ensure infrastructure resilience against heat-related damage. In coastal areas, rising sea levels increase the risk of inundation, prompting strategies such as hardening, raising, or relocating critical facilities to higher elevations. In some cases, sacrificial road sections are employed to absorb flood damage and protect more essential infrastructure elements. Similarly, storm surges, which are often intensified by rising sea levels, require measures like buried sheet piles to safeguard roadways. Other strategies, including elevated roadways and sacrificial designs, offer protection by diverting damage to less critical areas.

Overall, diverse adaptation measures can effectively address a wide range of climate-induced risks, ensuring the long-term resilience of transportation infrastructure. By focusing on proactive and flexible strategies, agencies can better manage the impacts of a changing climate.

7. Conclusions

This paper presents a comprehensive review of risk assessment and asset management practices for resilient roadway infrastructure. While significant advancements have been made, many transportation agencies continue to depend on simpler, qualitative tools, such as spreadsheet-based risk registers, due to resource constraints and implementation challenges. This study identifies persistent gaps, including data quality issues, limitations in climate change vulnerability assessments, and the lack of integration of advanced risk assessment methods into asset management practices.

The major contribution of this paper is the introduction of a system-of-systems performance-based asset risk management approach, designed to integrate various elements for effective investment prioritization and infrastructure resilience. At its core, this approach includes an Asset Database and a Risk Database, supported by an Asset Inventory and a Common Reference Location System (GIS). These components provide the foundation for analytical modules, enhanced by inputs from climate data and lessons learned, which contribute to vulnerability and resilience assessments based on exposure, sensitivity, and adaptive capacity.

A pivotal aspect of this approach is the integration of risk assessment throughout the asset management process. This process begins by defining goals and policies that establish the strategic direction. Incorporating a risk-focused perspective at this stage ensures that the goals are aligned not only with asset performance but also with resilience against evolving hazards. As the process moves to the Asset Inventory, risk assessment helps identify and register critical assets based on their vulnerability and importance, guiding decision makers toward the areas of greatest need. In the condition assessment step, risk assessment adds a crucial layer by contextualizing the condition data with potential threats.

The decision-support asset management framework that supports the integration of risk assessments is driven by policies and objectives that set targets and allocate budgets for investments, guiding analysis modules for needs assessment, scenario vulnerability assessment, and multi-objective investment analysis. Outputs from these analyses feed into a report module, which covers condition and risk assessments, gap analyses, vulnerability impacts, climate adaptation scenarios, and multi-objective investment reports, providing vital information for decision-making. Ultimately, this integrated, performance-based “system-of-systems” asset risk management approach supports the prioritization of investments, aligning with the goal of achieving sustainable and resilient transportation corridors at various levels.

In this context, key performance indicators (KPIs) are recommended for setting goals, monitoring the outcomes of climate-adaptive strategies, and measuring benefits. The recommended KPIs include the Risk Reduction Index (RRI), Infrastructure Resilience Score (IRS), Resource Allocation Efficiency (RAE), Condition Improvement Rate (CIR), Climate Impact Vulnerability Score (CIVS), Climate Adaptation Readiness Index (CARI), Response Time for Risk-Based Prioritization (RTRP), and Asset Recovery Time Post-Disruption (ARTPD). These KPIs collectively provide a comprehensive view of the framework’s effectiveness in mitigating risks and efficiently allocating resources for infrastructure assets.

In addition, the project prioritization process, supported by a case study demonstrating advanced methods such as k-means clustering and multi-criteria decision analysis (MCDA), effectively illustrates how to classify and prioritize vulnerable infrastructure based on environmental risks. A Climate Impact Vulnerability Score (CIVS) is proposed to measure the vulnerability of infrastructure assets to climate change conditions. Roadway infrastructure elements with high vulnerabilities are prioritized based on their risk profiles, helping to determine which gaps require immediate attention and resources. This practical application emphasizes the approach’s potential to inform real-world decision-making for resilient infrastructure.

Overall, the adoption of a climate adaptation asset risk management approach, supported by a decision-making framework and structured project prioritization process, is essential for developing more adaptive and resilient roadway infrastructure that can withstand evolving climate hazards. While considerable progress has been made in risk assessment for roadway infrastructure, challenges remain, such as the need for standardized risk performance measures, enhanced data integration, and a comprehensive approach to addressing socio-economic and environmental impacts.