The central insight of the study is deceptively simple but potentially transformative for disaster planning: the tools used to assess flood risk inequity are themselves biased. When the researchers compared their modeled estimates against observed data from the 2016 flood, they discovered that the models captured only 35.8 percent of the actual risk inequity between low-income and high-income communities. In other words, nearly two-thirds of the real disparity in emergency service disruption was invisible to the standard estimation approaches. A city planner using these models would conclude that flooding affects rich and poor neighborhoods in roughly similar ways, and would allocate resources accordingly, unaware that the true burden on disadvantaged areas was far heavier than the numbers suggested.

Why do these models fail so badly, and why do they fail disproportionately for the poor? The Georgia Tech team traced the bias to two mechanisms that existing approaches simply do not account for. The first is traffic congestion. During a flood, residents evacuate, roads clog, and vehicles cluster on the few elevated or unblocked routes that remain passable. Lower-income communities, the study found, experienced greater traffic congestion during the disaster, which slowed emergency vehicles far more than the models predicted. The second mechanism is the failure of the emergency infrastructure itself. More ambulance stations serving lower-income communities were closed or ran out of capacity during the flood, forcing vehicles to travel from farther away and adding delays that no static distance-based model could anticipate.

These findings arrive at a moment when cities around the world are investing heavily in digital twins, flood simulators, and equity dashboards intended to guide climate adaptation. The implicit promise of such tools is that if we can model the hazard precisely enough, we can protect everyone fairly. The Virginia Beach case study punctures that promise. A model can be technically accurate about water depth, road network topology, and travel distances, and still be profoundly wrong about who suffers, because it omits the dynamic, human, and infrastructural realities of a disaster as it unfolds. Congestion is a behavioral phenomenon; station closures are an operational one. Neither appears in the static network calculations that underpin most accessibility analyses.

The methodological approach of the study deserves attention because it offers a template other cities can follow. Rather than building a new simulation from scratch, the researchers performed a validation exercise: they took modeled travel time estimates for the Hurricane Matthew flood and compared them, neighborhood by neighborhood, with the actual ambulance response times recorded during the event. This comparison between modeled and observed data is the gold standard for assessing whether a risk estimation tool reflects reality, yet it is rarely done, particularly with an equity lens. By stratifying the comparison by community income level, Pan and colleagues were able to reveal not just that the models were biased, but that the bias itself was unequal, larger for the communities least able to absorb delayed medical care.

The implications for health outcomes are stark. Ambulance response time is one of the most tightly studied variables in emergency medicine, and the difference between a response within a few minutes and one delayed by five or more can determine whether a patient survives cardiac arrest, stroke, severe trauma, or childbirth complications. During a flood, when call volumes spike and normal hospital capacity is degraded, every additional minute of travel time compounds the strain on the system. If planners believe low-income areas face a two-minute underestimation when the real figure approaches six, they will size their ambulance fleets, pre-position their units, and design their evacuation protocols for a disaster that is milder than the one that actually arrives in those neighborhoods.

There is also a deeper equity dimension. The study’s authors frame their work around the question of whether socially vulnerable neighborhoods face disproportionate travel delays during urban floods, and their answer is an emphatic yes, compounded by the fact that the disproportion is systematically hidden. Risk inequity assessments are supposed to be the corrective lens that lets policymakers see and remedy these disparities. When the lens itself distorts, the harm is doubled: vulnerable communities suffer worse outcomes during the flood, and then suffer again in the planning process, because the data used to justify investments understates their need. Resources flow to where the models say the risk is, and the models say the risk is lower in the places where it is actually highest.

What would better estimation look like? The study points toward models that incorporate real-time traffic dynamics during flood events, rather than assuming free-flow conditions on the surviving road network. It points toward models that track the operational status of emergency facilities, including closures and capacity exhaustion, as part of the disruption itself rather than as an afterthought. And it points toward a culture of validation, in which cities routinely compare their modeled disaster scenarios against observed response data from past events, disaggregated by demographic characteristics, to detect and correct bias before it shapes policy. None of these requirements is conceptually exotic; the novelty of the study lies in demonstrating how much inequity goes undetected without them.

The research was supported by the Georgia Partnership for Innovation Community Research grant program and the National Science Foundation, and it arrives with an urgency amplified by climate change. Urban flooding is intensifying in frequency and severity across coastal and inland cities alike, and the populations most exposed, often lower-income residents in flood-prone districts, are precisely those with the fewest private resources to compensate for public failures. An ambulance that arrives six minutes late in a wealthy suburb with a private car in the driveway is a different catastrophe than one that arrives six minutes late in a neighborhood where that ambulance was the only realistic route to emergency care. The bias in the models, in this sense, is not merely a technical flaw but a distributional one, determining whose emergencies the system is calibrated to meet.

For the field of urban sustainability and disaster resilience, the Virginia Beach study is likely to become a canonical example of what researchers call estimation bias in risk assessment, and a reminder that equity analysis is only as good as the models on which it rests. The authors’ finding that conventional approaches captured barely a third of the true risk inequity should prompt every city that maintains a flood response plan to ask an uncomfortable question: if our models missed 64 percent of the disparity in Virginia Beach, how much are they missing here? The answer will not be found in more sophisticated water simulations or finer-grained road networks alone, but in grounding those models in the messy, observed reality of how floods actually paralyze traffic, close stations, and stretch emergency capacity, and in checking, neighborhood by neighborhood, whether the numbers we plan with match the minutes patients actually wait.

Subject of Research: Biased estimation of flood-related emergency service disruptions and ambulance response inequity in urban areas

Article Title: Exposing biased risk estimation of emergency service disruptions during urban floods

Article References: Pan, X., Mohammadi, N., & Taylor, J. E. (2026). Exposing biased risk estimation of emergency service disruptions during urban floods. npj Urban Sustainability. https://doi.org/10.1038/s42949-026-00473-3

Image Credits: AI Generated

DOI: 10.1038/s42949-026-00473-3

Keywords: urban flooding, ambulance response time, risk estimation bias, emergency services, social vulnerability, Hurricane Matthew, Virginia Beach, traffic congestion, health equity, disaster modeling, infrastructure resilience, npj Urban Sustainability

The award, part of NSF’s Fire Science Innovations through Research and Education program, runs from September 2026 through August 2029 and brings together a multidisciplinary team from the University at Buffalo, the University of California, Berkeley, Northeastern University and the U.S. Department of Energy’s Pacific Northwest National Laboratory. The study, titled Collaborative Research: FIRE-WUI: REKINDLE: Resilient Engineering through Knowledge Integration for NetworkeD Lifelines and Ecosystem, will focus on communities in the wildland-urban interface, the zone where housing, power lines and other human development meet or intermingle with forests, grasslands and other natural vegetation. These are precisely the places where the destructive potential of wildfire and its aftermath is growing fastest.

As wildfires become more frequent and severe across the American West and beyond, wildland-urban interface communities face escalating risks that extend well beyond the immediate destruction of the fire itself. Burned landscapes lose the vegetation and soil structure that once absorbed rainfall, making them dramatically more prone to debris flows and flash floods during subsequent storms. At the same time, fires and their secondary hazards can inflict significant disruptions on lifeline infrastructure, including power grids, water systems and transportation networks. Current approaches to wildfire resilience planning, however, typically consider these interconnected risks in isolation, which makes it difficult to see how they interact with one another and how people actually respond when multiple hazards strike in sequence.

REKINDLE is designed to close that gap by combining artificial intelligence, infrastructure modeling and social science to assess wildfire-related risks together and to identify concrete ways to bolster public safety and community resilience. Sayanti Mukherjee, PhD, the project’s lead principal investigator and an associate professor in UB’s Department of Industrial and Systems Engineering, explained the central problem the team hopes to solve. People are familiar with wildfire risk, she noted, but what is often overlooked are the additional hazards and cascading effects that can follow. A region affected by wildfire can become more vulnerable to subsequent hazards like debris flows and flash floods, and when those events cut off roads or interrupt power or water service, the impacts can quickly compound for communities already struggling to recover.

Mukherjee, who is also an affiliate faculty member with UB’s Department of Electrical and Computer Engineering and the Institute for Artificial Intelligence and Data Science, is joined by co-principal investigator Susan Spierre Clark, PhD, of UB’s Department of Environment and Sustainability. The broader investigative team includes Auroop R. Ganguly, PhD, of Northeastern University; Marta González, PhD, and Anna Serra-Llobet, PhD, of UC Berkeley; and Andre Coleman, PhD, and Sam Chatterjee, PhD, of Pacific Northwest National Laboratory. This mix of expertise in engineering, environmental science, data science and social systems reflects the project’s core premise: that understanding cascading wildfire disasters requires simultaneously modeling the physical hazards, the engineered networks they damage and the human decisions those damage cascades trigger.

Through REKINDLE, researchers will investigate how wildfires and the hazards they set in motion can compromise key infrastructure, producing disruptions that ripple through the essential services people depend on for daily life and well-being. Clark emphasized that the team wants to understand not only how infrastructure systems are affected, but how those disruptions shape household decisions and community response. Bringing those pieces together, she said, can help communities build resilience, restore critical infrastructure and recover faster from future disasters. The framing matters because infrastructure failures are rarely just technical problems; a closed road can prevent evacuation, a lost power connection can disable water pumping, and a prolonged outage can determine whether a family can remain in or return to a damaged neighborhood.

Artificial intelligence will play a central role in the technical core of the project. The researchers plan to build a physics-informed, geospatial-AI model that fuses Earth observation data with wildfire expertise to generate maps showing where wildfires and related hazards such as debris flows and flash floods are most likely to occur. By anchoring machine learning in physical processes, the team aims to produce hazard predictions that are both data-driven and scientifically interpretable, an increasingly important consideration for tools intended to inform emergency management decisions. In parallel, the researchers will deploy machine learning models to trace how failures could propagate across interconnected power, water and transportation systems, capturing the dependency structures that turn a localized fire damage into a region-wide service disruption.

The human dimension of the cascade will receive equal attention. Using household surveys, focus groups and simulation games, the research team will examine how infrastructure failures affect families’ livelihoods and their ability to recover from cascading disasters. These social science methods are intended to reveal how households weigh risks, make evacuation and recovery decisions, and cope when multiple services fail at once. The team will then use these findings to help determine where investments in stronger infrastructure could deliver the greatest protective impact before a fire occurs, and how recovery efforts should be prioritized in the aftermath. Mukherjee noted that deciding where to invest resources, whether through hardening infrastructure or increasing community engagement and outreach, will be an important part of the project, and that AI and optimization can help assess different strategies and pinpoint where investments would have the most value. That kind of insight, she said, can support emergency managers, infrastructure operators and environmental planners in making more informed decisions for their communities.

To keep the work grounded in real conditions, the researchers will build and test REKINDLE through case studies in the Los Angeles and Montecito regions of California, both of which have experienced major wildfires and devastating post-fire debris flows in recent years. Montecito in particular has become a sobering reference point for cascading wildfire hazards, having seen destructive debris flows sweep through burned terrain after intense rainfall. The research team will collaborate with government agencies, utility providers and community stakeholders throughout the project to help ensure that the tools they develop reflect the practical needs of the people and institutions that will ultimately use them, from county emergency planners deciding where to pre-position resources to utilities weighing which assets to reinforce first.

The project also carries a substantial education and workforce development mission. Researchers will create wildfire-focused educational materials for students ranging from middle school through graduate school, introducing a new generation to the science of wildfire resilience and the related hazards that follow fires. Undergraduate and graduate students will have opportunities to gain hands-on experience through internships with industry partners and national laboratories, building career pathways in a field where demand for expertise continues to grow. The team will make its educational resources and research data publicly available so that other researchers, practitioners and communities can build on the work. If REKINDLE succeeds, its integrated view of fire, flood, infrastructure and human response could reshape how at-risk communities plan for the full arc of a wildfire disaster, not just the day the flames arrive.

Subject of Research: AI-based modeling of cascading wildfire hazards and infrastructure resilience in wildland-urban interface communities

Article Title: University at Buffalo to lead $2 million National Science Foundation project on wildfire resilience

Article References: University at Buffalo to lead $2 million National Science Foundation project on wildfire resilience. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: wildfire, cascading disasters, artificial intelligence, infrastructure resilience, wildland-urban interface, debris flows, flash floods, National Science Foundation, University at Buffalo, geospatial AI, community resilience, lifeline infrastructure

The team focused on a type of structure that is deceptively common: a bridge pier founded on a group of piles, embedded in a slope affected by a slow-moving landslide. Unlike catastrophic, fast-moving slope failures that destroy bridges in seconds, slow-moving landslides creep at rates of millimeters to centimeters per year, often going unnoticed until cumulative deformation begins to distort the superstructure or crack the foundations. Yet these quiescent masses of soil are not necessarily benign. When an earthquake arrives, the weakened, sheared, and sometimes saturated landslide material can be reactivated, subjecting the piles to a combination of inertial forces from the shaking structure and kinematic forces from the moving ground. The study’s central question was deceptively simple: does the state of the slope before the earthquake matter for the pier’s seismic performance?

To answer it, the researchers built a nonlinear finite element model of the coupled soil-foundation-structure system, using a reference case study chosen precisely because it is paradigmatic of the numerous Italian bridges located in areas exposed to both seismic and hydrogeological hazards. Bridge piers, after all, may sit on the crest of a slope, along its face, or downstream of it, and the relative position of the bridge and the slope strongly influences the type and severity of the actions the structure must resist. The numerical model captured the essential mechanics of the problem: the nonlinear, strain-dependent behavior of the soil under cyclic loading, the interaction between the piles and the surrounding ground, and the dynamic response of the pier itself.

The analysis compared three distinct scenarios. In the first, the pier stands in a stable slope, representing the conventional design assumption in which the ground is competent and the only seismic demand comes from the shaking itself. In the second, the slope hosts a quiescent landslide that the earthquake reactivates, so the ground begins to move as the shaking progresses. In the third, the landslide is already active before the earthquake, meaning the piles have already accumulated deformation and stress from the creeping soil. This progression allowed the researchers to isolate the influence of pre-seismic slope conditions on the dynamic response of the pier, something that standard seismic assessment procedures, which typically assume stable ground, simply do not address.

The results, expressed in terms of maximum and residual displacements and rotations of the pier, show that the starting condition of the slope is far from a secondary detail. When the landslide is active before the earthquake, the piles enter the seismic event already stressed and deformed, and the ground movement during shaking adds kinematic demands on top of the inertial ones. The interaction between the two loading mechanisms can amplify displacements and, critically, leave the pier with larger residual deformations after the shaking stops. Residual displacement matters enormously for bridges, because permanent tilting or lateral shifting of a pier changes the geometry of the deck, alters load paths, and can compromise serviceability or trigger collapse even when the structure survives the earthquake itself.

Underpinning these findings is the physics of soil-structure interaction, a phenomenon that simplified design methods often reduce to a set of springs or, worse, ignore altogether. In reality, the piles, the surrounding soil and the pier form a single dynamic system whose response depends on the stiffness and damping of every component. Decades of research, from classical work on dynamic pile stiffness to modern studies of kinematic pile bending in layered soils, have shown that the ground is not a passive support but an active participant in the seismic response. The new study extends this understanding to the multi-hazard setting, demonstrating that the dynamic properties of a slope containing a landslide mass differ from those of a stable slope, and that these differences propagate directly into the demands on the foundation.

The Italian context gives the work particular urgency. The national landslide inventory compiled by the Institute for Environmental Protection and Research documents hundreds of thousands of landslide phenomena across the country, and recent research has catalogued extensive interactions between landslides and bridge foundations in Italian case studies. At the same time, Italy has endured repeated bridge failures, some linked to the degradation of structures subjected to unanticipated environmental actions. The national guidelines for classifying and managing the risk of existing bridges, introduced in 2020 and operationalized in subsequent years, pushed owners to assess their assets systematically, and recent technical guidance has begun to address landslide interaction explicitly. The new study provides a quantitative basis for that effort, showing what is at stake when a bridge pier’s foundation crosses a slowly moving mass of soil.

What makes the study especially valuable for practitioners is its methodological clarity. By using a nonlinear finite element approach within a single consistent model, the researchers could track the full path from static slope condition to dynamic seismic response, capturing the state of stress and strain in the soil at the moment the earthquake begins. This matters because the initial stress state governs how the soil stiffens or softens under cyclic loading, how pore pressures evolve, and how much permanent deformation accumulates. The approach also reflects the reality of earthquake-triggered landslide reactivation, a phenomenon documented in devastating detail after events such as the 2008 Wenchuan earthquake in China and the 2016 Kumamoto earthquakes in Japan, where seismically induced slope failures destroyed bridges that had performed adequately under shaking alone.

The broader message is a call for multi-hazard thinking in the assessment and retrofit of existing infrastructure. A bridge evaluated only for seismic loads, assuming stable ground, may be far more vulnerable than its safety factor suggests if it stands in the path of a slow-moving landslide. Conversely, retrofit strategies designed for landslide stabilization alone may leave the structure exposed to seismic demands that the modified soil-foundation system now transmits differently. The authors argue that accounting for soil-foundation-structure interaction is essential when assessing the seismic response of bridge piers in the presence of slow-moving landslides, and that a multi-hazard approach is needed to enhance both the assessment of existing bridges and the sustainability of retrofit strategies. As climate change alters rainfall patterns and slope hydrology, and as seismic hazard remains a permanent feature of the Mediterranean landscape, the quiet creep of a hillside may prove to be the hidden variable that determines whether the next earthquake is survivable for the bridges that carry our lifelines.

Subject of Research: Seismic response of pile-supported bridge piers founded in slopes affected by slow-moving landslides

Article Title: Seismic response of a pile-supported bridge pier under multi-hazard conditions in slow-moving landslides: a numerical investigation

Article References: Ambrosino, A., Servodio, M., Sica, S., Losanno, D., Parisi, F., & Santolo, A. S. D. (2026). Seismic response of a pile-supported bridge pier under multi-hazard conditions in slow-moving landslides: a numerical investigation. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02678-4

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02678-4

Keywords: soil-structure interaction, landslides, earthquake engineering, multi-hazard assessment, bridge piers, pile foundations, finite element analysis, slope stability, seismic retrofit, geotechnical engineering, infrastructure resilience, residual displacement

The research, conducted by Tiku T. Tanyimboh of the University of the Witwatersrand and North Lanarkshire in the United Kingdom, together with Nokuthula R. Nyawo of the University of the Witwatersrand and the Johannesburg Roads Agency, tackles a deceptively simple question: when a developer must choose between conventional piped drainage, rainwater harvesting systems and detention basins, how should that choice be made? The authors argue that previous studies have typically addressed fragments of the problem. Some evaluated hydrologic performance without cost. Others considered water supply reliability for rainwater tanks in isolation, without examining how those tanks alter the behavior of the entire drainage network. Very few combined probabilistic rainfall modelling, full hydrologic-hydraulic simulation of the complete drainage infrastructure, and whole-life cost analysis within a single decision framework.

To close that gap, the team developed a generic, parsimonious and computationally efficient method for sizing rainwater harvesting tanks based on the modal storm, the representative storm event that captures the dominant rainfall characteristics of a site. This SuDS-oriented tank-sizing approach deliberately sidesteps the elaborate continuous simulations and extensive historical data requirements that have traditionally made probabilistic tank design burdensome. By anchoring the sizing calculation to the modal storm, the method delivers tank capacities consistent with the statistical structure of local rainfall while remaining fast enough to embed in iterative design and optimization workflows. The tank sizing also moves beyond the conventional focus on water supply reliability alone, instead treating the tank as a component of the drainage system whose storage volume attenuates runoff and reshapes the hydrographs that downstream pipes and channels must convey.

With tank sizing resolved, the researchers built a multicriteria decision-making framework around the analytical hierarchy process, a structured technique for weighting and combining multiple, often conflicting, objectives. The framework compares alternative SuDS-based integrated drainage designs across infrastructure performance, whole-life costs and a broader set of sustainability factors. The analytical hierarchy process, originally formulated by Thomas Saaty, asks decision makers to perform pairwise comparisons among criteria and alternatives, translating qualitative judgments into quantitative priorities. Here those priorities were complemented by entropy-based criteria weighting, a technique that derives objective weights from the spread and discriminating power of the performance data itself, reducing the subjectivity that can undermine purely judgment-based weighting schemes.

The test bed for the framework was a large, real-world urban residential development on a greenfield site in Johannesburg, South Africa, a city whose climate and regulatory context make it a compelling laboratory for drainage research. The authors modelled the hydrologic and hydraulic response of the complete drainage infrastructure under each design alternative, routing runoff generated by probabilistically derived design storms through the network to assess whether pipes and channels retained adequate freeboard and conveyance capacity. Greenfield sites are particularly instructive because they allow designers to compare conventional and sustainable approaches from first principles, without the legacy constraints that complicate retrofit studies.

One of the study’s most striking results concerns hidden redundancy in existing design standards. The analysis revealed excess flow capacity in the drainage network, which translated directly into high marginal construction costs. The authors attribute this oversizing to implicit constraint duplication within the drainage design standards, meaning that multiple clauses in the governing guidelines effectively impose the same hydraulic requirement, each adding its own margin of safety. When these overlapping constraints stack, the resulting infrastructure is significantly larger, and therefore significantly more expensive, than a technically defensible design requires. In an era when municipal budgets are stretched thin, the finding suggests that simply harmonizing design standards could free substantial resources without sacrificing flood protection.

The cost analysis carried a second sobering message. Operation and maintenance expenditure accounted for approximately 35 to 47 percent of the net present value of the drainage schemes examined. Whole-life costing, which discounts construction, operation, maintenance, rehabilitation and end-of-life costs over the asset’s service horizon, is frequently championed in principle but neglected in practice, with decisions dominated by lowest capital cost. The study’s numbers make clear that ignoring the operational tail of a drainage project’s life distorts the comparison between options. Detention basins, for example, may appear inexpensive at construction yet accrue maintenance liabilities associated with sediment removal, vegetation management and outlet structure upkeep, while rainwater harvesting systems require pump maintenance, tank cleaning and eventual component replacement.

Rainwater harvesting carries an additional dividend that conventional drainage cannot match: it converts a nuisance into a resource. In a water-stressed country such as South Africa, where national planning documents have repeatedly warned of supply deficits, harvested rainwater can displace potable demand for non-potable uses such as toilet flushing, irrigation and laundry. The study’s framework captures this dual function, crediting harvesting designs both for runoff attenuation and for water conservation, while detention basins earn their place through flood peak reduction and conventional systems through their simplicity and familiarity. By scoring every alternative on the same weighted criteria, the framework exposes trade-offs that single-metric evaluations conceal, such as how much additional capital a rainwater harvesting scheme must absorb to deliver a given improvement in network hydraulic performance.

The broader significance of the work lies in its methodological generality. The authors describe their approach as generic, meaning it can be transplanted to other catchments, climates and regulatory regimes by substituting local rainfall statistics, unit costs and design standards. The parsimonious tank-sizing method lowers the computational barrier that has kept smaller municipalities and design consultancies out of probabilistic SuDS analysis, and the multicriteria structure accommodates additional criteria, from water quality improvement to amenity and biodiversity value, as local priorities demand. As climate change allowances force designers to plan for intensified rainfall, frameworks of this kind provide a defensible, transparent and reproducible basis for spending scarce infrastructure funds where they deliver the most value.

For engineers, planners and municipal officials, the practical takeaways are concrete. Audit drainage design standards for duplicated constraints before committing to oversized pipes. Bring operation and maintenance costs to the table at project inception, where they can shape the choice of technology rather than merely haunt the asset register. Treat rainwater tanks not just as water supply devices but as distributed storage that reshapes catchment response. And above all, evaluate drainage alternatives as integrated systems, using structured multicriteria methods that make every assumption explicit. The study suggests that the path to affordable, resilient urban drainage runs not through any single technology, but through smarter, more honest comparisons among them.

Subject of Research: Multicriteria evaluation of sustainable urban drainage design options combining hydrologic-hydraulic performance, rainwater harvesting tank sizing and whole-life cost analysis.

Article Title: Multicriteria Evaluation of Sustainable Drainage Options: Comparison of Conventional Drainage, Rainwater Harvesting and Detention Basins

Article References: Tanyimboh, T. T., & Nyawo, N. R. (2026). Multicriteria Evaluation of Sustainable Drainage Options: Comparison of Conventional Drainage, Rainwater Harvesting and Detention Basins. Water Resources Management, 40(12), Article 527. https://doi.org/10.1007/s11269-026-04878-8

Image Credits: AI Generated

DOI: 10.1007/s11269-026-04878-8

Keywords: sustainable drainage systems, stormwater management, rainwater harvesting, detention basins, multicriteria decision making, analytical hierarchy process, whole-life costing, urban flooding, runoff attenuation, water resources management, Multicriteria, Evaluation

The concept of sustained resilience is especially relevant because the built environment is not a collection of independent objects. Buildings, bridges, roads, drainage networks, power systems, water supplies, hospitals and communication infrastructure operate as interconnected networks. When one component fails, the consequences can spread well beyond the original point of damage. A flooded transport tunnel can delay emergency response; a power outage can disable water pumps; a damaged hospital can overwhelm facilities in neighboring districts. Engineers often describe these relationships using concepts such as interdependency, redundancy and cascading failure. Interdependency means that one system relies on another, while redundancy refers to the presence of alternative components or routes that can maintain service when part of a network fails. Sustained resilience brings these technical ideas together with social and institutional capacity, asking whether the entire urban system can continue operating over long periods of stress rather than merely withstand a single event.

This approach also challenges the idea that resilience has a fixed endpoint. In a conventional disaster-recovery model, the goal is often represented as a return to a previous condition, sometimes called the baseline state. That model can be useful for measuring physical damage, but it may conceal important weaknesses. Returning quickly to a vulnerable arrangement can simply restore the conditions that produced the original risk. A city rebuilt in the same floodplain, with the same drainage limitations and the same social inequalities, may appear recovered while remaining exposed. Sustained resilience instead treats recovery as a dynamic process. The desired outcome may be different from the pre-disaster state: safer buildings, more flexible public services, lower energy demand, improved accessibility and stronger community networks. In technical terms, resilience becomes a capacity for maintaining critical functions and adjusting system performance as environmental, social and economic conditions change.

One of the study’s most significant implications is that resilience cannot be measured only by the strength of individual structures. A building may be designed to resist a specified earthquake intensity or wind speed, yet the surrounding community may still become inaccessible if roads, utilities or emergency services fail. Similarly, a flood-resistant structure may offer limited protection if residents cannot evacuate, afford repairs or return to work. This means that structural engineering must be connected to urban planning, environmental science, public health and social policy. Performance-based design can help engineers estimate how a building will behave under different hazard scenarios, including whether it remains safe, repairable or operational. At the city scale, however, performance also depends on recovery time, service continuity and the distribution of risk. Sustained resilience therefore encourages a multi-scale assessment, from materials and structural components to neighborhoods, infrastructure corridors and metropolitan regions.

The framework is particularly powerful in the context of climate change, which is altering the frequency, intensity and geographic distribution of hazards. Heatwaves can degrade road surfaces, increase electricity demand and threaten human health at the same time. Heavy rainfall can overwhelm drainage systems that were designed using historical climate records no longer representative of present conditions. Coastal cities face the combined effects of sea-level rise, storm surges, saltwater intrusion and land subsidence. These pressures are not necessarily temporary. They can create chronic stress that gradually reduces the performance of buildings and infrastructure even in the absence of a headline-making disaster. Sustained resilience asks whether urban systems can operate under this continuous load, whether maintenance and adaptation are funded before failure occurs, and whether design standards can evolve as new evidence emerges. The emphasis is therefore not only on emergency response, but also on monitoring, preventive investment and long-term adaptation.

The researchers’ unifying perspective also highlights the role of time. Materials deteriorate, populations move, technologies change and land-use patterns evolve. A resilient design that performs well today may become inadequate decades later if it cannot be modified or maintained. This is why adaptability is a central technical feature of sustained resilience. Adaptable buildings can be reconfigured for new uses, strengthened without complete demolition or equipped with systems that respond to changing conditions. Infrastructure can be designed with modular components, distributed energy generation, multiple water sources or flexible capacity. Digital tools, including remote sensing, structural-health monitoring and urban digital twins, can help authorities detect damage and model possible interventions. Yet technology alone cannot guarantee resilience. Data must be interpreted, maintenance must be carried out and decisions must be coordinated across agencies. The framework places equal importance on physical design and the governance systems that keep urban assets functional throughout their life cycle.

That governance dimension may be crucial because resilience is unevenly distributed. Wealthier districts often have stronger buildings, better drainage, more reliable utilities and greater access to insurance and emergency assistance. Vulnerable communities may live in areas exposed to flooding, landslides, extreme heat or industrial hazards while having fewer resources to recover. If resilience investments focus only on protecting valuable infrastructure or central business districts, they may reduce measurable economic losses without reducing human suffering. A sustained approach requires asking who benefits from adaptation, who bears the cost and which services are essential for people with limited mobility, insecure housing or restricted access to information. Social resilience can include trusted local organizations, inclusive emergency planning and the ability of residents to participate in decisions about rebuilding. These factors are not secondary to engineering performance. They influence whether technical systems are used, maintained and accessible when a crisis arrives.

The study’s message arrives as urban authorities worldwide search for ways to manage risk without replacing entire cities. The most effective strategies may combine gradual interventions rather than rely on one large protective project. Updating building codes, restoring wetlands, improving stormwater storage, reinforcing critical structures, expanding shaded public space and decentralizing energy systems can work together to reduce exposure and preserve essential functions. Nature-based solutions can complement conventional infrastructure by slowing runoff, cooling neighborhoods and protecting coastal edges, while distributed systems can prevent a single failure from disabling an entire network. Such measures require coordination across planning periods that rarely align with political cycles. They also require metrics that capture more than construction output. Authorities may need to track service continuity, recovery time, maintenance quality, adaptive capacity and the ability of different population groups to withstand disruption. Sustained resilience turns these long-term considerations into a central measure of urban performance.

The idea may also change how future cities define success. Instead of asking whether a structure was built to a particular standard or whether a damaged district has been restored, planners could ask whether the urban system is becoming less fragile over time. That means identifying critical functions, mapping dependencies, testing failure scenarios and designing pathways for safe transformation. Scenario analysis can reveal how simultaneous hazards might interact, while network models can estimate how disruption in one sector affects others. Life-cycle assessment can compare the environmental and economic consequences of repair, replacement and adaptation. Importantly, the approach recognizes that resilience is not a permanent label. It must be renewed through inspection, investment, learning and public participation. Cities that maintain resilience over decades will likely be those capable of treating every disruption as information: evidence of where systems are strong, where hidden dependencies exist and where future design must improve. The study presents sustained resilience not as a single technology or policy, but as a shared property emerging from the continuous interaction of buildings, infrastructure, ecosystems and communities.

As climate and urban risks accelerate, this broader definition could become one of the most consequential ideas in city design. A seawall, a stronger bridge or a heat-resistant building may protect people from a specific threat, but no isolated intervention can secure an interconnected metropolis facing multiple forms of stress. Sustained resilience offers a way to evaluate whether cities can preserve essential services, adapt without deepening inequality and recover without reproducing the vulnerabilities that caused damage in the first place. The concept links structural reliability with social capacity, environmental performance and institutional learning. It suggests that the future of urban safety will depend less on creating systems that never fail than on creating systems that fail safely, recover intelligently and become better prepared after each shock. In that sense, resilience is not a destination reached when construction ends. It is an ongoing urban capability—one that must be designed into the built environment, supported by governance and continuously tested by reality.

Subject of Research: Sustained resilience in cities and the built environment

Article Title: Sustained resilience as a unifying property of cities and the built environment

Article References: Mitoulis, SA., Kopiika, N., Di Bari, R. et al. “Sustained resilience as a unifying property of cities and the built environment.” npj Urban Sustainability (2026). https://doi.org/10.1038/s42949-026-00457-3

Image Credits: AI Generated

DOI: 10.1038/s42949-026-00457-3

Keywords: Urban resilience, built environment, climate adaptation, infrastructure, disaster risk reduction, sustainable cities, resilient buildings, urban systems, cascading failures, sustainability

The Department of Energy’s Oak Ridge National Laboratory, in partnership with the National Laboratory of the Rockies, has launched a new national center to help strengthen the U.S. cement and concrete supply chain at a time of rising demand, increasing imports and mounting pressure on domestic infrastructure. The Center of Excellence for Advancement and Acceleration of Cement and Concrete Engineering Technologies, known as the ACCENT Center, will accelerate the development of advanced materials and manufacturing processes needed to support U.S. infrastructure, housing and industrial competitiveness.

Designed as a first-of-its-kind hub, ACCENT combines multiscale testing, AI-driven data collection and modeling, and pilot production to move cement and concrete innovations from discovery to early commercialization — a combination of capabilities not currently available in one place in the United States.

ACCENT will build the nation’s first comprehensive, publicly accessible database for concrete, housing millions of standardized, performance-based materials data and facts. By combining independent test results, long-term durability data and digital models, the database will help close critical gaps between laboratory research and field performance, enabling faster and more reliable qualification of new materials.

“The ACCENT Center represents a pivotal shift in how we address the challenges of maintaining and advancing our nation’s infrastructure,” said ORNL’s Denise Antunes da Silva, the center’s executive director. “By merging dedicated research with practical industry expertise, we’re shortening the innovation timeline and building a foundation for more resilient, cost-effective cement and concrete technologies.”

Securing America’s cement supply: key challenges and opportunities

Concrete is essential to the nation’s infrastructure, supporting roughly 80 percent of the U.S. construction industry. Although cement and concrete underpin a $25 trillion economy, the sector faces systemic bottlenecks. The United States imports approximately 25 percent of its cement — $2.6 billion annually — exposing infrastructure projects to supply and cost volatility.

These vulnerabilities are compounded by aging infrastructure and a nationwide housing shortage estimated to exceed 4 million units. At the same time, domestic cement production capacity is limited by aging manufacturing infrastructure, lengthy permitting processes and high capital costs to expand production, even as U.S. demand is projected to grow nearly 30 percent by 2032. This imbalance highlights the need for a more resilient, modernized supply chain.

“These challenges aren’t just about rising demand; they affect the nation’s infrastructure and economic stability,” said Robert Wagner, associate laboratory director for ORNL’s Energy Science and Technology Directorate. “ACCENT gives us a way to move research into the field faster. With standardized testing, better data and expanded training, the center will help reduce supply chain risks, manage costs and strengthen infrastructure safety.”

Accelerating breakthroughs in concrete innovation

To help address these challenges, the center will use its collaborative model to speed the development and deployment of advanced binders and construction materials, with the goal of cutting the typical 15- to 20-year innovation cycle by as much as half.

ACCENT will advance multiscale materials characterization by integrating unique imaging capabilities to reveal critical structure–property relationships and enable in situ analyses under a range of exposure conditions.

The center will also develop performance-based tests and accelerated durability frameworks to shorten evaluation times.

In parallel, ACCENT will monitor and predict material performance in real time through integrated sensors, predictive modeling and an AI-driven data platform designed to shorten testing cycles and improve prediction accuracy. Pilot-scale stations will speed up the transition of new materials from the lab to the field, while training of professionals will help transition new practices into widespread industry use.

“The ACCENT Center will unify R&D, testing and workforce development to close critical gaps in design, validation and skills,” Silva said. “This integrated approach will enable the rapid deployment of reliable, U.S.-made cement and concrete materials — and represents a major step toward strengthening the global competitiveness of American industry.”

Transforming cement innovation with an industry-leading framework

The center will work directly with industry through an open, secure framework that supports innovation, validation, prediction and scaling of novel binders for concrete materials. By providing a single environment for testing, data generation and collection, and performance verification, the framework reduces uncertainty and lowers barriers to adoption for new technologies. This approach builds on the demonstrated success of ORNL’s user facility models.

“The center will provide unprecedented industry access to unique testing capabilities and national laboratory expertise, fostering vital collaborations that span the entire cement and concrete value chain,” Silva said.

The ACCENT Center is a public-private partnership that brings together multiple national laboratories, universities, professional/trade associations and industry partners to accelerate the development of advanced materials and manufacturing processes. The center is funded by DOE’s Industrial Technologies Office. Learn more about the ACCENT Center.

UT-Battelle manages ORNL for the DOE’s Office of Science, the single largest supporter of basic research in the physical sciences in the United States. The Office of Science is working to address some of the most pressing challenges of our time. For more information, please visit energy.gov/science. — Tina M. Johnson