By: Paolo Gardoni, Ana Pinheiro Privette, and Ana Barros
Executive Summary
Structures and infrastructure are vital for economic growth, quality of life, and social connectivity, providing essential services like water, transportation, healthcare, energy, and communications. However, they face significant threats from both natural and human-made hazards, risks that in many cases are worsened by climate variability, including more frequent and severe extreme weather events. These challenges are further amplified by the interconnectedness and mutual dependencies of community-level engineering systems, which can accelerate deterioration, reduce performance, and ultimately lead to system failure. To minimize these risks, both robustness (the ability to endure disruptions) and resilience (the ability to recover and adapt) are essential. Understanding and modeling hazard impacts, evaluating the residual functionality of structures and risks, and developing mitigation and recovery strategies require a holistic analytical framework that recognizes infrastructure as an interconnected system of systems. A regional risk assessment that integrates both the physiographic setting and the social fabric of communities is essential for understanding community-level vulnerabilities and estimating the likelihood of damage and expected performance levels when hazards occur. It integrates data-driven and physical models, historical data, and predictive tools, accounting for uncertainties. The process involves six main stages: 1) understand and quantifying the threats and associated hazards, 2) defining the physical inventory of structures and systems at risk, and identify relevant users and stakeholders; 3) generating intensity measures (IMs) of hazards, such as earthquake shaking values, probable maximum precipitation and probable maximum floods and compound hazards from extreme events in general; 4) estimating damage probability considering the effects of aging and maintenance and operations on the vulnerability of the infrastructure components; 5) determining how physical damage translates into functionality reduction or complete loss while considering infrastructure dependencies; and 6) determining how impaired or collapsed infrastructure systems impact the communities’ social fabric. Factors such as insurance, availability of resources, emergency response, management capacity, and individual adaptability play key roles in recovery. In this context, sustainability considerations aim at minimizing risk while protecting future generations and the environment by making careful use of limited resources.
Building Resilience in the Physical Space
Structures and infrastructure (physical systems) are the background upon which quality of life, economic activity, and social interactions take place. They provide vital services like water, energy, communication, transportation, healthcare, and education, all of which are crucial for human development and well-being. Natural and human-made hazards cause billions of dollars in damages annually to these systems, as well as thousands of fatalities, injuries, and other societal consequences (NCEI, 2025; Gardoni et al., 2016).
Climate variability and change can exacerbate this problem for some hazards and regions by modulating and modifying the likelihood, magnitude, and consequences of some natural hazards such as heat waves, droughts, floods, and hurricanes, thus introducing uncertainty in the characterization of potential hazards and consequently increasing uncertainty in model inputs used to predict physical damage to and loss or reduction of functionality of structures and infrastructure, and societal impacts (Chang, 2014).
To minimize risks, both robustness and resilience of physical systems must be considered. Robustness refers to the initial safety as the ability of a physical system to endure stress, shocks, or disruptions without significant damage or decline in performance. It is related to the system’s residual functionality immediately following a hazard. A robust system continues to function effectively under a range of conditions, ensuring minimal disruption and sustained performance. Resilience, on the other hand, is generally defined as the ability of a system to rapidly recover to the original or a new level of functionality (Murphy and Gardoni, 2018). The duration of recovery is crucial for risk evaluation and is influenced by factors like the extent of damage, available resources, environmental conditions, and potential additional disruptions during the recovery process (Murphy and Gardoni, 2008).
Hurricane Helene struck the southeastern United States in the fall of 2024, causing widespread disruptions to water systems in Western North Carolina. In the Asheville area, 80% of the population was left without access to potable water for 53 days. The storm brought record-breaking rainfall, resulting in severe runoff that triggered extensive and destructive flooding. This flooding not only damaged critical infrastructure, including water treatment plants and pipelines, but also contaminated water sources with debris, sediments, and pollutants. This devastating event underscores the vulnerability of infrastructure to natural disasters and highlights the urgent need to build greater resilience in water systems to withstand such extreme events.
Together, robustness and resilience contribute to the long-term reliability of infrastructure, institutions, and communities. Enhancing both ensures that the systems we rely on are not only safe from short-term disruptions but are also capable of evolving and thriving as environmental conditions change. As we design physical systems for a rapidly changing world, innovative engineering solutions are becoming crucial to mitigate the impacts of disruptions and accelerate recovery. By incorporating advanced materials, smart and adaptive designs, and data-driven models, engineers can ensure that systems are not only resilient but also sustainable in the long term. Ultimately, these innovations play a key role in building communities that are both prepared for disruptions and able to recover quickly, should a disruption occur, ensuring continued well-being, development, and prosperity. One important element of integrated risk assessment is recognizing that reliability is essentially nonstationary in the sense that it evolves with changes in physical and socioeconomic systems (e.g., growing urbanization, expansion of roads, technological disruptions, etc.). Therefore, community resilience is a process that requires continuous monitoring of risks and vulnerabilities.
Evaluating the Reliability and Functionality of Physical Systems
Evaluating the reliability of physical systems, assessing hazard impacts, and planning effective recovery and mitigation strategies require careful consideration of many interrelated factors. Risk analysis provides a structured framework to support optimal system design, maintenance, and resource allocation by balancing robustness and resilience. The process typically begins with a regional risk assessment, which estimates both the likelihood of damage and the ability of systems to maintain performance during natural or human-made disasters. Effective risk assessments integrate data-driven and physical models, historical records, and predictive tools to generate a comprehensive view of potential threats. Given the inherent uncertainties—stemming from both the unpredictable nature of hazards and the limitations of available data or models—the framework must be probabilistic. This allows it to account for aleatory uncertainty (randomness in natural hazards) and epistemic uncertainty (knowledge gaps or modeling assumptions (Gardoni et al., 2002; Murphy et al., 2011).
There are six primary stages in regional risk analysis. The first stage of regional risk analysis involves understanding and quantifying threats and associated hazards, including the hydroclimatological processes that drive extreme rainfall, drought, and broader hydrometeorological variability. Hydroclimatology examines how water cycle dynamics—such as moisture transport, precipitation formation, and the timing and intensity of rainfall events—are influenced by atmospheric and land–surface interactions, and how these in turn stress water and infrastructure systems when they occur outside historical norms. Several studies (Barros et al., 2017; NASEM, 2016; IPCC, 2022) emphasize the importance of accurately characterizing precipitation dynamics in complex terrain, including the spatial variability and multi-scale organization of extreme rainfall events that lead to flood hazards and infrastructure stress, and the development of high-resolution precipitation estimates linked to hydrologic responses in headwater basins. Such insights into precipitation variability and drought occurrence are critical for quantifying the frequency and magnitude of hydroclimatic hazards and forming the basis for basin-scale modeling of water availability, flood risk, and infrastructure vulnerability under a changing climate, which traditional historical records alone may not capture.
Second, we define a physical inventory, which describes the portfolio of physical systems within the region of interest. This step is crucial for understanding the assets that are at risk and assessing their vulnerability to hazards. The physical inventory typically includes the classification and characterization of buildings by their construction materials, age, design, and condition, and other relevant parameters (Steelman et al., 2007). Similarly, the transportation network, electric power grid, and water and wastewater systems must be fully defined. These networks are made up of both linear elements (like roads, power lines, and pipelines) and nodal elements (such as bridges, substations, and pumping stations).
The third stage involves estimating how strong the hazard will be at different locations across the region. These estimates are called intensity measures (IMs). For example, during an earthquake, IMs like peak ground shaking or spectral acceleration are calculated for each point using models that consider things like the distance from the earthquake’s center, its size, the local soil, and the type of fault (Murphy and Gardoni, 2008).
In 2022, New Mexico’s largest wildfire, a result of out-of-control prescribed burns, destroyed hundreds of homes and displaced thousands of people across a fire zone larger than the city of Los Angeles. The McBride Fire burned through areas of forest and vegetation that are part of the watershed feeding the city of Las Vegas, NM drinking water supply. Heavy rains that followed the fire washed ash, debris, and harmful substances into local rivers and reservoirs making the water unsafe for consumption. Source: New York Times
The fourth stage is estimating the probability of damage, i.e., the chance that each part of the system will be damaged or lose functionality when impacted. For nodal elements (like buildings or stations), this is done using fragility curves, which show the likelihood that an element will reach or exceed a certain level of damage based on the hazard intensity (Gardoni, 2017. For linear elements (like roads or pipelines), repair rate curves are used to estimate how many repairs will be needed per unit length, depending on the intensity of the hazard (IMs) (ALA, 2002; O’Rourke and Deyoe, 2004).
The fifth stage involves translating the physical damage sustained by infrastructure components into a quantified reduction or complete loss of functionality over time. This step captures how damage to physical assets—such as broken pipelines, collapsed power lines, or compromised structural elements—impairs the system’s ability to deliver essential services. Once the functionality degradation is assessed, its effects are propagated, or cascaded, throughout all interdependent infrastructure networks, thereby providing a comprehensive understanding of systemic vulnerability and recovery challenges across interconnected infrastructure systems (Ellingwood et al., 2016; Gardoni et al., 2016).
The sixth and final stage requires determining how impaired or collapsed infrastructure systems impact the social fabric of the community, which requires an integrated analytical framework that captures both physical system performance and the broader human consequences of disruption.
Assessing the societal impact builds on analyses from Civil Engineering (Stages 1-5) and also needs a multi-disciplinary approach, where Civil Engineers play a central role (Gardoni et al., 2016; Murphy et al., 2018). A capability approach has been proposed to assess the societal impact of hazards by looking at the impact on individuals’ well-being (Murphy and Gardoni, 2016). A capability is the genuine opportunity an individual has to do and become something of value. Examples include the ability to be healthy, mobile, educated, and enjoy affiliations with others. The genuine opportunities open to an individual depend on what an individual has (e.g., resources, skills, knowledge) and what the individual can do with them (e.g., given the structure of legal, economic, and social institutions, as well as the characteristics of the built and natural environments). Therefore, we can quantify the societal impact of hazards in terms of changes in individuals’ capabilities since they depend on the state and functionality of structures and infrastructure (Gardoni and Murphy, 2010; Gardoni and Murphy, 2020). For example, the impact of an earthquake can be quantified in a comprehensive and meaningful way as changes in the opportunities individuals have immediately after the earthquake and during the recovery (Boakye et al., 2022).
Key Considerations for Infrastructure Risk Management
While assessing and managing risk, three considerations must be incorporated: dependencies and interdependencies among physical systems, the aging and deterioration of physical systems, and the possible presence of multiple hazards (Gardoni and Murphy, 2018).
Dependencies and Interdependencies
Extensive research has assessed the performance and reliability of individual components, such as bridges, buildings, and electrical substations, as well as systems like water, power, and transportation networks. (Kang et al.; 2008, Guikema and Gardoni, 2009; Lee et al., 2011, Frangopol and Bocchini, 2012; Gardoni et al., 2003; Choe et al., 2007; Ramamoorthy et al., 2006). However, the functionality of these systems often hinges on the interconnections between multiple systems. Quantifying, monitoring, and mitigating risks is a complex process requiring a deep understanding of how systems interact with one another. Dependencies and interdependencies among infrastructure networks—such as energy, transportation, water, telecommunications, and healthcare—are critical, both for assessing robustness and resilience. Failures in one system can trigger cascading effects across others. For instance, a power outage can disrupt communication and transportation systems, while a water supply failure can compromise building functionality and public health (Chang, 2014; Gardoni, 2017). Different methods have been developed to model the effects of such (inter)dependencies (Sharma et al., 2020a; Sharma et al., 2020b; Vespignani, 2010; Sharma and Gardoni, 2022).
Aging and Deterioration
Another key factor to consider is that physical systems deteriorate over time due to multiple interacting mechanisms, making them increasingly vulnerable to hazards (Choe et al., 2008; Choe et al., 2009; Eck-Olave et al., 2015; Gardoni and Rosowsky, 2011). As physical systems age, their fragility estimates, repair rates, failure probabilities, and reduced functionality tend to increase. In regional risk analysis, different networks deteriorate at varying rates. For example, a bridge may experience corrosion faster than a highway overpass due to environmental conditions. Additionally, the dependency between infrastructure elements, like water networks relying on power systems, can change over time due to aging and deterioration. Recent research has improved models that predict the impact of aging and deterioration on infrastructure reliability, showing that traditional models, which ignore the interaction between different deterioration mechanisms, tend to underestimate vulnerability and damage following extreme events (Iannacone et al., 2016).
Multiple Hazards
The assessment of a system’s vulnerability becomes more complex when it faces multiple hazards, as the interactions between different threats can amplify the risks. Multiple hazards can be classified as concurrent (e.g., wind and surge); cascading (e.g., fire following earthquake); or independent and likely to occur at different times (e.g., wind and earthquake). While some regions around the world might be at risk for a single hazard type, many parts of the world are likely to face multiple hazard types.
When evaluating the probability of failure of structures and infrastructure, or the likelihood of reaching a specified damage state, it is essential to account for the most relevant hazards and their interconnections, rather than treating each as an isolated event (Gardoni and LaFave, 2016). However, such assessments are often challenged by the rarity and complexity of compound events, which limit the availability of historical data to estimate their joint probabilities with confidence.
In the absence of sufficient local data, researchers often rely on analog data from similar events in other regions to infer possible magnitudes, sequences, and impacts. While such comparative or surrogate analyses can offer valuable insights—helping to constrain models, validate conceptual understanding, and identify plausible ranges of outcomes—they come with important limitations. Regional differences in climate, topography, soil properties, geology, infrastructure design, land-use patterns, and governance capacity can significantly alter how similar physical processes unfold and interact. As a result, transferring data or models from one context to another can introduce systematic biases if not adjusted for local conditions.
Earthquakes often have severe impacts on water distribution systems. One notable example is Napa, California, which was struck by a 6.0 magnitude earthquake in 2014. The earthquake resulted in infrastructure damage, with the water system being the most impacted, experiencing approximately 160 water main breaks. One water main break impaired firefighting effort at the largest of six fires that occurred due to the earthquake. Source: https://peer.berkeley.edu/sites/default/files/spa-2014-napa-report.pdf
Risk Positioning and Monitoring
Large-scale extreme events such as hurricanes produce large amounts of precipitation over short durations of time causing flashfloods, landslides and debris flows that in turn drive cascading failures across communications, transportation, energy and water infrastructure. Although concepts such as Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) have guided the design of major, isolated civil works—particularly dams—for more than a century, their adoption reflects strong societal risk aversion following catastrophic failures in the first half of the 20th century that caused widespread downstream destruction and thousands of fatalities. The underlying engineering risk benchmark associated with PMP and PMF design is on the order of 1:1,000,000 (e.g., Douglas and Barros, 1993).
Because this standard implies extremely high construction costs, PMP/PMF criteria have rarely been applied to urban infrastructure systems. This reluctance is reinforced by the perception that such extreme events are exceedingly rare and therefore pose negligible practical risk. However, when weather systems and topographic controls are considered in defining regional risk, such as in “flash flood alley” in Texas or along the Appalachian Mountains, the probability of extreme precipitation occurring somewhere within a well-defined region becomes much higher. For example, Liao and Barros (2025) show that across the state of Texas, and more generally along the Gulf and Atlantic coasts, the so-called thousand-year rainfall event is frequently associated with landfalling hurricanes exhibiting decadal recurrence intervals (approximately 10–30 years).
Closing the gap between local and regional risk understanding is essential for vulnerability assessment and to achieve affordable resilience which may require convergence of built and social solutions. Nonstationarity emerges at regional scale from climate variability (e.g. the lull in landfallen hurricanes 1950-1990 in North Carolina,; Barros and Evans, 1997) and locally due to the dynamic nature of infrastructure systems that in turn impact landscape and system-scale response (Barros et al. 2014). Ongoing positioning and monitoring risk are key therefore to resilience that is smart, agile, and adaptive (Olson et al. 2015; Barros and Evans, 1997).
Given these constraints—limited observational records, regional nonstationarity, and evolving infrastructure dynamics—the most defensible approach integrates process-based physical understanding with carefully contextualized cross-regional analogs, supported by numerical modeling and synthetic data generation. This hybrid framework allows scientists and engineers to systematically explore the behavior of low-probability, high-consequence compound events, even where empirical records are sparse or incomplete. By doing so, it enhances the robustness, transferability, and credibility of vulnerability and risk assessments in complex, multi-hazard environments.
Thinking Beyond the Physical Aspects
Several additional factors must be more explicitly considered to enhance the understanding of risk management of structures and infrastructure. First, the role of insurance should be carefully examined, as it is a vital tool for redistributing and transferring risks, offering financial support for recovery efforts.[31] Insurance can help communities and businesses mitigate the financial impacts of disasters, enabling quicker recovery and reducing the long-term economic burden. Second, the availability of government resources at various levels—local, regional, and national—needs to be better integrated into recovery strategies. Governments play a crucial role in facilitating and coordinating recovery efforts, but their capacity to respond effectively can vary significantly depending on the available resources, political will, and organizational efficiency. Third, individuals’ ability to adapt and recover independently of the built infrastructure should be considered, as personal resilience can significantly influence overall community recovery. The ability of a community or society to resist, absorb, accommodate, and recover from a hazard in a timely and efficient manner is closely linked to both the repair and rebuilding of physical infrastructure (often with external support) and the capacity of individuals and communities to adapt independently and creatively, outside of the recovery of built infrastructure (Olshansky, 2018). A more holistic approach that includes these factors can lead to more comprehensive and effective resilience-building strategies.
Lastly, sustainability plays a key role in the design of physical systems and the recovery of communities affected by hazards, focusing not only on restoration but also on maintaining a desired quality of life over time. It emphasizes both the intrinsic value of a healthy ecosystem and its importance to human well-being. Sustainable recovery involves protecting the environment’s ability to renew itself and rehabilitate damaged ecosystems, while considering recovery in the broader context of both the natural environment and physical systems
About the Center for Secure Water
The Center for Secure Water (C4SW) is based in the Department of Civil and Environmental Engineering at the University of Illinois Urbana-Champaign. The Center is dedicated to developing and advancing innovative, inclusive, and sustainable solutions for global water security through research, technology development, and collaboration with partners across academia, government, industry, and civil society.
ASSESSING THE IMPACT ON CRITICAL INFRASTRUCTURE FOLLOWING DAMAGING EVENTS
Our work (Guidotti et al, 2019; Iannacone et al., 2022) explores a probabilistic flow-based modeling framework to assess the capacity and demand of critical infrastructure following damaging events. The approach captures how such events impact infrastructure functionality and predicts resulting service disruptions. It accounts for both physical damage to infrastructure and shifts in human behavior (e.g., evacuation), which alter post-event demand. By integrating physical systems with social dynamics, the model provides more accurate predictions of system performance and recovery. An application to Seaside, Oregon’s potable water network under a seismic scenario illustrates that ignoring physical-social interdependencies can lead to overestimating demand, underestimating societal impacts, and misjudging recovery timelines.
RESILIENCE-INFORMEDINFRASTRUCTURE RECOVERY
We modeled the electric power and potable water infrastructure systems of Shelby County, TN to demonstrate how infrastructure interdependencies can be effectively handled. A rigorous mathematical framework was developed to simulate post-disaster recovery, quantify system resilience, and optimize resilience strategies for large-scale infrastructure networks. This approach significantly reduces computational complexity while producing practical, easily implementable recovery schedules. By leveraging multi-objective optimization, the method enhances regional resilience based on proposed metrics, all while minimizing recovery costs.
The Role of Civil Engineers in Building Resilient Communities
Civil engineers play a vital role in creating safe, functional, and resilient communities. Their expertise in designing, building, and maintaining infrastructure—such as transportation, water, energy, and waste systems—allows them to develop integrated solutions that can withstand hazards like earthquakes, floods, and sea-level rise. They promote sustainability through efficient designs, renewable energy, and the use of sustainable materials, while also adapting infrastructure to evolving climate conditions through measures like flood-resistant construction and system retrofits.
Trained in risk and vulnerability assessment, civil engineers identify system weaknesses and design strategies to minimize disruptions and support rapid recovery. They also lead interdisciplinary collaboration, working with other engineers, urban planners, social scientists, ethicists, and policymakers to develop solutions that address technical, social, and ethical challenges. Through this holistic approach, civil engineers help ensure that infrastructure meets the needs of communities now and in the future.
ALA. (2001, April). “Seismic fragility formulations for water systems: Part 1 – guideline, American lifelines alliance”, ASCE. Retrieved June 26, 2017 from http://www.americanlifelinesalliance.org/
Barros, A.P., Hodes, J., and Arulraj, M. (2017), Decadal Climate Variability and the Spatial Organization of Deep Hydrological Drought. Env.Res.Letters, DoI:10.1088/1748- 9326/aa81de.
Barros, A.P., Duan, Y., Brun, J., Medina, M.A., 2014: Flood Nonstationarity in the SE and Mid
Atlantic Regions of the United States. J. Hydrologic Engineering, DOI:(ASCE) HE.1943
5584.0000955.
Barros, A.P., and Evans, J.L., 1997: Designing for Climate Variability. J. of Professional Issues in Engineering Education and Practice, ASCE, 123, No.2, 62-65.
Boakye, J., Guidotti, R., Gardoni, P., and Murphy, C. (2022). “The role of transportation infrastructure on the impact of natural hazards on communities,” Reliability Engineering & System Safety, 219, 108184.
Chang, S. E. (2014). “Infrastructure resilience to disasters”. The Bridge, 44, 36–41
Choe, D., Gardoni, P., & Rosowsky, D. (2007). “Closed-form fragility estimates, parameter sensitivity, and Bayesian updating for RC columns,” ASCE Journal of Engineering Mechanics, 133(7), 833–843.
Choe, D., Gardoni, P., Rosowsky, D., & Haukaas, T. (2008). “Probabilistic capacity models and seismic fragility estimates for RC columns subject to corrosion.” Reliability Engineering and System Safety, 93(3), 383–393.
Choe, D., Gardoni, P., Rosowsky, D., and Haukaas, T., (2009). “Seismic fragility estimates for reinforced concrete bridges subject to corrosion”, Structural Safety, 31, 275-283.
Contento, A., Chhabra, T., Gardoni, P., & Dierer, S. (2017, August 6–10). “Analysis of the effects of climate change on insurance against hurricanes”, Proceedings of the 12th International Conference on Structural Safety & Reliability (ICOSSAR 2017), Vienna, Austria.)
Douglas, E. and Barros, A.P., 2003: Probable Maximum Precipitation Estimation Using Multifractals: Application in the Eastern United States. J. Hydromet., 4(6), 1012-1024.
Eck-Olave, M. K., Bracci, J. M., Gardoni, P., & Trejo, D. (2015b). “Performance of RC columns affected by ASR. I: Accelerated exposure and damage”, ASCE Journal of Bridge Engineering, 20(3), 04014069.
Ellingwood, B.R., Cutler, H., Gardoni, P., Peacock, W.G., van de Lindt, J.W., and Wang, N. (2016). “The Centerville virtual community: A fully integrated decision model of interacting physical and social infrastructure systems”, Sustainable and Resilient Infrastructure, 1(3–4), 95–107.
Frangopol, D.M., and Bocchini, P., (2012). “Bridge network performance, maintenance and optimization under uncertainty: Accomplishments and challenges”, Structure and Infrastructure Engineering, 8(4), 341–356.
Gardoni, P. (Ed.). (2017). Risk and Reliability Analysis: Theory and Applications. Springer.
Gardoni, P., and LaFave, J., (2016). “Multi-hazard approaches to civil infrastructure engineering: mitigating risks and promoting resilience,” in Paolo Gardoni and James LaFave (Eds.), Multi-hazard Approaches to Civil Infrastructure Engineering, Springer.
Gardoni, P., and Murphy, C., (2009). “Capabilities-based Approach to measuring the societal impacts of natural and man-made hazards in risk analysis,” ASCE Natural Hazards Review, 10 (2), 29-37.
Gardoni, P., and Murphy, C., (2010). “Gauging the societal impacts of natural disasters using a Capabilities-based Approach,” Disasters, 34 (3), 619-636.
Gardoni, P., and Murphy, C., (2018). “Society-based Design: Developing Sustainable and Resilient Communities”, Sustainable and Resilient Infrastructure, DOI: 10.1080/23789689.2018.1448667.
Gardoni, P., and Murphy, C., (2020). “Society-based design: Promoting societal well-being by designing sustainable and resilient infrastructure”, Sustainable and Resilient Infrastructure, 5(1-2), 4-19.
Gardoni, P., and Rosowsky, D., (2011). “Seismic fragility increment functions for deteriorating reinforced concrete bridges”, Structure and Infrastructure Engineering, 7 (11), 869-879.
Gardoni, P., Der Kiureghian, A., & Mosalam, K. M. (2002). “Probabilistic capacity models and fragility estimates for RC columns based on experimental observations”, ASCE Journal of Engineering Mechanics, 128(10), 1024–1038.
Gardoni, P., Mosalam, K. M., & Der Kiureghian, A. (2003). “Probabilistic seismic demand models and fragility estimates for RC bridges”, Journal of Earthquake Engineering, 7(1), 79–106.
Gardoni, P., Murphy, C., and Rowell, A., (Eds.) (2016). Societal Risk Management of Natural Hazards, Springer.
Guidotti, R., Chmielewski, H., Unnikrishnan, V., Gardoni, P., McAllister, T., and van de Lindt, J. (2016). “Modeling the resilience of critical infrastructure: the role of network dependencies”, Sustainable and Resilient Infrastructure, 1(3-4), 153-168.
Guidottia, R., Gardoni, P., and Rosenheim, N. (2019), “Integration of physical infrastructure and social systems in communities’ reliability and resilience analysis”, Reliability Engineering and System Safety, 185, 476–492.
Guikema, S., and Gardoni, P. (2009). “Reliability estimation for networks of reinforced concrete bridges“, ASCE Journal of Infrastructure Systems, 15, 61–9.
Iannacone, L., Sharma, N., Tabandeh, A., and Gardoni, P. (2022), “Modeling Time-varying Reliability and Resilience of Deteriorating Infrastructure”, Reliability Engineering and System Safety, 217, 108074.
IPCC (2022), Climate Change 2022: Impacts, Adaptation and Vulnerability. Intergovernmental Panel on Climate Change. Cambridge University Press.
Kang, W.H., Song, J., and Gardoni, P. (2008). “Matrix-based system reliability method and applications to bridge networks”, Reliability Engineering and System Safety, 93, 1584–93.
Lee, Y.-J., Song, J., and Gardoni, P., Lim, H.-W., (2011). “Post-hazard flow capacity of bridge transportation networks considering structural deterioration of bridges”, Structure and Infrastructure Engineering, 7 (7), 509-521.
Liao, M., and Barros, Ana, P., 2026: Estimating Probable Maximum Precipitation for the Continental United States. J. Hydrology-Regional Studies, 64, 103122. https://doi.org/10.1016/j.ejrh.2026.103122.
Murphy, C., and Gardoni, P. (2008). “The acceptability and the tolerability of societal risks: A capabilities-based approach”, Science and Engineering Ethics, 14(1), 77-92.
Murphy, C., and Gardoni, P., (2006). “The role of society in engineering risk analysis: a Capabilities-based Approach,” Risk Analysis, 26 (4), 1073-1083.
Murphy, C., Gardoni, P., and Harris, C.E. (2011). “Classification and moral evaluation of uncertainties in engineering modeling”, Science and Engineering Ethics, 17(3), 553-570.
Murphy, C., Gardoni, P., and McKim, R., (Eds.) (2018). Climate Change and Its Impact: Risks and Inequalities, Springer.
NASEM (2016), At the Intersection of Climate and Infrastructure: Nexus to the Built Environment. National Academies of Sciences, Engineering, and Medicine. The National Academies Press.
NCEI (2025 March) National Centers for Environmental Information Billion-dollar weather and climate disasters. National Oceanic and Atmospheric Administration. Retrieved March 2025 from https://www.ncei.noaa.gov/access/billions.
Olshansky, R.B. (2018). “Recovery after disasters: How adaptation to climate change will occur”, In C. Murphy, P. Gardoni, and R. McKim (Eds.). Climate Change and Its Impact: Risks and Inequalities. Springer.
Olsen, J. Rolf, Ayyub, B.M., Barros, Ana P., Lei, Wayne, Lombardo, F., Medina, Miguel, Samaras, Constantine , Smith, Orson, P., Stakhiv, Eugene Z., Tan, Jonathan, Vison, Walker, Dan, and Wright, Richard. (2015), Adapting Infrastructure and Civil Engineering Practice to a Changing Climate. Committee on Adaptation to a Changing Climate (CACC) of the ASCE, 89 pp. http://dx.DoI.org/10.1061/9780784479193.
O’Rourke, M. J., & Deyoe, E. (2004). “Seismic damage to segmented buried pipe”, Earthquake Spectra, 20, 1167–1183. doi:10.1193/1.1808143
Ramamoorthy, K. S., Gardoni, P., & Bracci, M. J. (2006). “Probabilistic demand models and fragility curves for reinforced concrete frames”, ASCE Journal of Structural Engineering, 132(10), 1563–1572.
Sharma N, Tabandeh A, Gardoni P. (2020) “Regional resilience analysis: A multiscale approach to optimize the resilience of interdependent infrastructure”, Computer-Aided Civil and Infrastructure Engineering;35(12):1315–30.
Sharma, N., Nocera, F., and Gardoni, P. (2020). “Classification and mathematical modeling of infrastructure interdependencies”, Sustainable and Resilient Infrastructure, DOI: 10.1080/23789689.2020.1753401
Sharma, N., Gardoni, P. (2022). “Mathematical modeling of interdependent infrastructure: An object-oriented approach for generalized network-system analysis,” Reliability Engineering & System Safety, 217, 108042.
Steelman, J., Song, J., & Hajjar, J. F. (2007, Jan 07). “Integrated data flow and risk aggregation for consequence-based risk management of seismic regional losses”, MAE Center Report. Retrieved June 26, 2017 from http://mae.cee.illinois.edu/
Vespignani, A. (2010). “Complex networks: The fragility of interdependency”. Nature, 464, 984–985.