Hybrid buildings occupy an awkward middle ground in structural engineering. They combine reinforced concrete frame elements, columns and beams, with load-bearing masonry walls in ways that do not fit the textbook definitions of either a pure concrete frame or a pure masonry structure. In these buildings, the stiff masonry walls and the more flexible concrete frames share the task of resisting lateral earthquake forces, producing complex load transfer paths and highly uneven stiffness distributions. Engineers have long known from laboratory studies that infill masonry walls can dramatically alter how a concrete frame responds to shaking, sometimes protecting it and sometimes triggering brittle failures. But when it comes to regional risk assessments, buildings like these are typically forced into one of the two standard categories, and their distinctive behavior is effectively erased from the statistics.

The February 2023 sequence made the cost of that simplification painfully visible. The magnitude 7.8 Pazarcık earthquake and the magnitude 7.6 Elbistan event struck hours apart, subjecting cities across Hatay, Gaziantep, and Kahramanmaraş provinces to some of the strongest ground motions ever recorded in Türkiye. In the aftermath, teams from the Ministry of Environment, Urbanization and Climate Change carried out rapid visual damage inspections across hundreds of thousands of structures, tagging each building with a damage class. That enormous dataset, normally used simply to prioritize demolitions and aid, became the raw material for the new analysis.

From the official field assessments, the researchers filtered out 169 low-rise hybrid buildings of one to three stories located in the Antakya and Nurdağı regions. For each building, they derived a coordinate-based estimate of short-period spectral acceleration, a standard measure of ground shaking intensity at the periods relevant to stiff, low-rise structures. The values came from the USGS ShakeMap rasters for both main shocks, and for every building the team adopted the larger of the two estimates as the maximum envelope demand, a conservative choice that reflects the cumulative assault of the two events on the same structure.

The damage picture in the sample was grim. Nearly 60 percent of the 169 buildings, 59.76 percent exactly, had been classified as heavily damaged, requiring urgent demolition, or had collapsed outright. The authors are careful to stress that this figure describes their filtered sample, not the region as a whole. Because the representativeness of the field data could not be established, the proportion should not be read as an estimate of how widespread severe damage was across all hybrid buildings in the affected areas. Still, the sheer concentration of severe outcomes in a dataset drawn from official inspections underscores why the researchers felt these structures warranted dedicated analysis.

To convert the paired observations of shaking intensity and damage into something predictive, the team turned to binary logistic regression, a statistical technique widely used in empirical fragility work. For each of three damage thresholds, slight-or-worse, moderate-or-worse, and heavy-or-worse, they fitted a continuous fragility function that expresses the probability of exceeding that threshold as a function of the spectral acceleration demand. Fragility curves of this kind are the workhorses of earthquake loss estimation: feed in an expected ground motion and the curve returns the likelihood that a typical building of the class will sustain at least a given level of damage.

The resulting median capacities are strikingly close together. The spectral acceleration at 0.3 seconds corresponding to a 50 percent probability of exceedance came out at 1.238 g for the slight-or-worse threshold, 1.405 g for moderate-or-worse, and 1.490 g for heavy-or-worse. In many building classes, these medians would be spread much further apart, reflecting a gradual progression from cosmetic cracking to structural distress. The tight clustering here suggests that once short-period shaking in these hybrid buildings crosses a certain band, damage can escalate rapidly from mild or moderate levels to heavy damage, leaving little margin between repairable and irreparable states. The authors caution that part of this pattern may also reflect uncertainties in the dataset, including the coarse binary and rapid visual damage classifications on which the models rest, and they note that the relationship for the lowest threshold showed only marginal statistical significance.

Even with those caveats, the implications for seismic risk assessment are significant. If hybrid buildings can jump from moderate to severe damage within a narrow range of demand, then treating them as ordinary reinforced concrete frames or as masonry buildings could seriously misestimate both expected losses and the number of structures likely to need demolition after a major event. The study argues that low-rise hybrid structures should be evaluated as a candidate separate vulnerability group, one whose models explicitly account for infill-wall and frame interaction, stiffness mismatch between the two structural materials, and discontinuities in the load path. Such an approach would bring regional risk models closer to the behavior that laboratory experiments on infilled frames have documented for decades, from the pioneering experimental evaluations of masonry-infilled concrete frames to recent work on confined masonry and mixed construction.

The methodology itself is a template for post-earthquake science. Rather than waiting years for detailed structural surveys, the researchers showed that official rapid assessment data, when combined with coordinate-matched ShakeMap intensity values, can yield usable fragility functions for a building class that would otherwise remain invisible. This approach echoes statistical procedures developed for earthquake damage fragility curves over the past two decades and complements recent empirical fragility studies of reinforced concrete buildings in the same earthquake. It also aligns with a broader movement toward faster, data-driven damage assessment, including machine learning frameworks that infer ground-level damage from aerial imagery.

The authors are explicit about the limits of their findings. The dataset is geographically concentrated in two districts, and the ground motions it reflects carry the specific signature of the 2023 sequence, including directivity effects and site conditions particular to the region. The fragility curves should therefore be interpreted as broad statistical indicators of relative vulnerability rather than precise predictive tools for any individual building. Data availability is also constrained: the underlying damage records can be obtained from the corresponding author upon reasonable request, subject to permission from the Turkish ministry that collected them. Nevertheless, as Türkiye rebuilds and as other earthquake-prone countries confront their own stocks of informal, mixed-construction buildings, the message of the study is hard to ignore. The buildings that defy easy classification are precisely the ones that risk models most often get wrong, and the 2023 Kahramanmaraş earthquakes have now provided the empirical evidence to prove it.

Subject of Research: Empirical seismic fragility assessment of low-rise hybrid concrete-masonry buildings damaged in the 2023 Kahramanmaraş earthquake sequence in Türkiye

Article Title: Empirical fragility assessment of low-rise hybrid buildings following the 2023 Kahramanmaraş earthquake sequence

Article References: Türer, A., Çağlar, F. A., & Bahçacı, Y. (2026). Empirical fragility assessment of low-rise hybrid buildings following the 2023 Kahramanmaraş earthquake sequence. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02689-1

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02689-1

Keywords: hybrid buildings, empirical fragility, Kahramanmaraş earthquakes, seismic vulnerability, spectral acceleration, logistic regression, infill walls, rapid visual screening, Antakya, Nurdağı, ShakeMap, earthquake damage assessment

The research, conducted by Resat Oyguc and Evrim Oyguc of the Institute of Disaster Management, Earthquake Engineering Department at Istanbul Technical University, focuses on a class of buildings that keeps structural engineers awake at night: non-ductile reinforced concrete frames. These are structures designed before modern seismic codes demanded the careful reinforcement detailing that allows a building to bend and deform without collapsing. Many such buildings across seismically active regions, from Türkiye to California, remain in service today, and retrofitting them has become a global engineering priority.

Two of the most common retrofit strategies anchor the study. The first involves wrapping columns with bonded carbon fiber-reinforced polymer, or CFRP, jackets—thin, immensely strong composite sheets that confine the concrete and delay brittle failure. The second deploys steel buckling-restrained braces, ingenious devices in which a steel core designed to yield in both tension and compression is housed within a tube that prevents it from buckling, allowing the brace to dissipate seismic energy stably through hundreds of loading cycles. Both approaches can dramatically improve a frame’s performance, but they do so in fundamentally different ways, and this difference has long frustrated attempts at unified assessment.

The problem, as the authors explain, lies in the very design of existing damage indices. Scalar indices calibrated against one intervention family record how much damage has occurred rather than which mechanism produced it. An index tuned to the behavior of CFRP-retrofitted frames transfers poorly to braced frames, and neither can sound an alarm when a brittle mode—such as joint panel shear failure or sliding—is about to take control of the response. In a severe earthquake, that distinction can mean the difference between a repairable structure and a catastrophic collapse.

The researchers’ answer is the Mechanism Evolution Index, or MEI, a framework that condenses six independently measurable response quantities into a bounded scalar on the unit interval, while simultaneously decomposing the structural state into a ternary coordinate that separates demand into three interpretable channels: ductility, shear, and connection. The six constituents include quantities familiar to any experimentalist: the normalized hysteretic energy dissipated during a run, the ratio of peak base shear to design-level capacity, the peak interstorey drift ratio, the residual drift left after shaking stops, the dynamic amplification between the shaking table and the structure above it, and the period softening ratio that captures how much the structure’s fundamental frequency has degraded from one excitation to the next.

What makes the framework remarkable is its economy of calibration. The six constituents are combined through a single frozen weight set, anchored once at the joint-panel cracking and sliding thresholds of a three-specimen calibration series. Each constituent passes through a sigmoid normalization function whose half-saturation value is fixed to the measured response of the bare specimen at design intensity. Once these anchors are set, the weights are frozen and applied without any re-tuning across retrofit type and shaking intensity. This is a deliberate departure from the prevailing practice, in which every new structural configuration demands its own bespoke index calibration, rendering comparisons across interventions nearly impossible.

The ternary coordinate is where the index earns its diagnostic power. The three components—ductility, shear, and connection—sum to one, placing every response state as a point within a triangular coordinate space whose corners represent pure ductile, pure shear, and pure connection demand. As a frame is shaken progressively harder, its point traces a trajectory through this space, and the leading axis at any moment names the mechanism that governs. The framework also defines a dominance margin, the difference between the two largest coordinates, which quantifies how decisively one mechanism leads over the others—a measure of confidence in the diagnosis itself.

To build and test the framework, the researchers drew on a one-third-scale specimen series: a bare non-ductile frame, the same frame retrofitted with a three-ply CFRP jacket, and a version strengthened with an innovative tube-in-tube buckling-restrained brace. All three configurations were reproduced by a single calibrated OpenSees finite element model, whose joint hysteresis was tuned to match measured displacement traces, base shear histories, hysteretic energy, and the fundamental periods identified from white-noise excitation before and after each strong-motion run. The calibration objective weighted all four error measures simultaneously, ensuring the simulation platform captured not just peak responses but the degradation pathways that unfold between them.

The findings tell a story that should concern anyone responsible for retrofit decisions. Under design-level intensity, the bare frame became connection-dominated, its ternary point migrating toward the joint sliding threshold exactly as its unreinforced beam-column panels began to fail. The CFRP jacket, perhaps counterintuitively, suppressed joint engagement through the design band but then collapsed through the very same connection channel when pushed to the ultimate level—the strengthening shifted the brittle mode upward in intensity rather than eliminating it. Only the braced frame remained on the ductility axis throughout, converting the failure mode into a stable, energy-dissipating one. The connection component of the index thus serves as an indicative retrofit-screening signal, flagging interventions that leave a residual brittle pathway even when headline performance metrics look satisfactory.

Transferability—the acid test for any index claiming generality—was examined on six external cases lying outside the calibration set, four bare and two retrofitted. In every labelled case, the mechanism reported by the experimental or analytical source landed on the leading ternary axis of the index. The classical scalars, by contrast, tracked damage severity faithfully but could not name the mechanism in any of them. This separation between knowing how much and knowing what kind is, in the authors’ framing, the essential contribution of the work.

The practical implications extend well beyond the laboratory. Post-earthquake reconnaissance following recent sequences in Türkiye has repeatedly documented the lethal consequences of brittle joint failures in non-ductile frames, and retrofit campaigns guided only by severity-based indices may inadvertently favor interventions that mask rather than remove the underlying vulnerability. A screening tool that can be applied identically across bare, jacketed, and braced configurations, using response quantities measurable from instrumentation or extracted from a calibrated simulation, offers engineers a common currency for comparing retrofit options on the dimension that matters most in a collapsing building: which mechanism will win.

The authors are careful to delineate the scope of their framework. It applies to regular planar systems under single-direction motion, and the frozen weight set is anchored to joint-panel thresholds specific to the calibration series used. Extending the approach to three-dimensional response, torsionally irregular buildings, and bidirectional ground motion remains open. The data supporting the study are available from the corresponding author upon reasonable request, and the authors report no external funding or competing interests. Even within these bounds, however, the Mechanism Evolution Index represents a meaningful conceptual advance: a damage measure that evolves alongside the structure, tracing not just the accumulation of harm but the shifting identity of the mechanism inflicting it.

As cities with vast stocks of pre-modern-code concrete buildings confront escalating seismic risk, the difference between a retrofit that redirects failure into a ductile pathway and one that merely postpones a brittle one is not an academic distinction—it is a matter of life and death. By giving engineers a single bounded number paired with an interpretable mechanism diagnosis, the Istanbul Technical University team has provided a tool that could make that distinction visible before the next earthquake makes it tragically apparent.

Subject of Research: A mechanism evolution index for the seismic assessment of retrofitted non-ductile reinforced concrete frames

Article Title: A mechanism evolution index for the seismic assessment of retrofitted non-ductile RC frames

Article References: Oyguc, R., & Oyguc, E. (2026). A mechanism evolution index for the seismic assessment of retrofitted non-ductile RC frames. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02679-3

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02679-3

Keywords: seismic retrofitting, reinforced concrete, damage index, ductility, CFRP, buckling-restrained brace, OpenSees, non-ductile frames, beam-column joints, shake table testing, earthquake engineering, structural assessment

The work, carried out by Shandy Rianto and Xinzheng Lu of the Department of Civil Engineering at Tsinghua University in Beijing, addresses a stubborn problem in regional seismic damage assessment. Traditional regional models typically describe buildings using a handful of coarse attributes—height, construction era, occupancy class—and then map those attributes to damage states through simplified relationships. The approach is computationally cheap, but it flattens away the very details that govern how a structure actually behaves under strong shaking: the arrangement of its frames, the distribution of member stiffness, the topology of its lateral-force-resisting system.

At the other extreme sit multi-degree-of-freedom, or MDOF, models, which condense a building into a stack of lumped masses connected by nonlinear shear or flexural springs. These models can reproduce hysteretic behavior—strength and stiffness deterioration under cyclic loading—reasonably well, but they must be parameterized from limited structural data. When those parameters are assigned from generic statistical rules rather than from the building’s actual design, the simulated response can drift far from reality. Meanwhile, machine learning surrogates trained to imitate finite-element analysis have grown popular, but most of them ingest buildings as fixed-length feature vectors. A vector, however long, cannot natively capture the fact that a structure is a network of connected members whose behavior depends on adjacency, hierarchy, and load paths.

The Tsinghua team’s insight was to represent each building as a graph. In this representation, nodes correspond to structural components or stories, and edges encode the physical connections between them—beam-to-column joints, story-to-story coupling, lateral load transfer. Node features carry engineering attributes such as member dimensions, material properties, and story masses, while edge features describe connectivity and geometry. Graph neural networks, or GNNs, are uniquely suited to such data because their message-passing architecture propagates information along the actual edges of the structure. During each layer of the network, a node aggregates features from its neighbors, transforms them, and passes the result onward. After several rounds of message passing, each node’s embedding reflects not just its own properties but the structural context in which it sits—exactly the kind of relational reasoning that fixed vectors struggle to supply.

The framework the researchers developed is not a single model but a spectrum of data-physics integration strategies, offering three distinct surrogate modeling approaches. Two of them are hybrid schemes that retain an MDOF model as the physics backbone and use graph-based learning to correct or enrich its predictions. In effect, the MDOF simulation provides a physically consistent first estimate of the seismic response, and the GNN learns the systematic discrepancies between that estimate and the true nonlinear finite-element response, using the graph-structured description of each building to explain why the simplified model falls short for a particular structure. This division of labor matters: the physics component constrains the prediction to plausible dynamic behavior, while the data component supplies building-specific nuance that generic parameterization cannot capture.

The third approach is more ambitious—a physics-aware, end-to-end GNN that estimates seismic responses directly from the graph representation, embedding structural mechanics knowledge into the network’s architecture and training rather than relying on an intermediate simulation step. This end-to-end route eliminates the need to run even a simplified MDOF simulation at prediction time, promising the greatest speed, while the embedded physics keeps the network from learning spurious correlations that would fail under earthquakes unlike those in its training set.

To train and validate these models, the researchers drew on established datasets of seismic designs, nonlinear models, and response simulations for steel moment-resisting frame buildings—a class of structure whose ductile steel connections and frame-based lateral systems make them a natural testbed for graph methods. Steel moment frames were modeled with nonlinear hysteretic behavior consistent with the deterioration phenomena documented in the earthquake engineering literature, and the ground-motion demand was characterized through intensity measures that capture spectral shape, not just peak acceleration. The graph-based structural representation dataset compiled for the study has been deposited in the Science Data Bank, making both the dataset and the GNN algorithms available to other researchers—a transparency move that matters in a field where reproducibility of machine learning benchmarks has often lagged behind headline accuracy claims.

The benchmark comparisons are where the framework makes its case. Against traditional parameterization-based MDOF modeling—the standard practice when only coarse building data are available—the proposed approaches reduced response estimation errors by more than 50 percent. They also outperformed commonly used data-driven surrogates that rely on fixed-length vector features, confirming the central hypothesis: the accuracy bottleneck in regional machine learning for earthquakes is not merely the amount of training data but the adequacy of the structural representation itself. When the model can see the building as a connected system rather than as a list of attributes, it learns correspondences that map far more faithfully onto actual structural behavior.

The timing of this work is no accident. Cities worldwide are generating increasingly rich digital descriptions of their building stock—digital twins, semantic city information models, and detailed urban survey databases that record geometry, materials, and structural systems at a fidelity unimaginable when the first regional loss estimation tools were assembled in the 1980s. The missing ingredient has been a computational method that can exploit this detail at scale. Full finite-element analysis of a million-building portfolio remains impractical even with modern GPU clusters, but a trained GNN performs inference in a fraction of a second per building, once trained. The hybrid framework thus occupies a sweet spot: fidelity approaching refined structural simulation, speed approaching that of the crudest empirical tools.

The practical stakes extend well beyond academic benchmarks. Rapid post-earthquake damage assessment determines where rescue teams are sent first, which hospitals are presumed operational, and how emergency budgets are allocated in the hours and days after a major event. Pre-earthquake risk mapping, in turn, drives retrofit prioritization and insurance pricing for entire metropolitan building stocks. In both scenarios, systematic over- or under-estimation of damage has real consequences: misallocated resources, mispriced risk, and misplaced confidence in buildings that may not perform as the regional model assumes. A surrogate that halves the estimation error without slowing the analysis down changes the reliability of every downstream decision.

There are, of course, caveats inherent to the approach. Surrogate models inherit the distributional boundaries of their training data; a GNN trained on code-compliant steel moment frames cannot be trusted, without retraining, to predict the response of unreinforced masonry or irregular older buildings that behave in fundamentally different ways. The physics-aware design mitigates but does not eliminate extrapolation risk, which is precisely why the authors emphasize the hybrid tiered structure—users can choose the level of data-physics integration appropriate to their data availability and confidence requirements. The authors also note the framework’s applicability hinges on the quality of the urban-scale databases feeding it, though the trend line for such data availability is clearly pointing upward.

What the study ultimately demonstrates is a conceptual shift in how the field thinks about structural representation for machine learning. For a decade, progress in seismic surrogates came largely from better regression architectures stacked on top of the same hand-engineered feature vectors. This work argues that the representation itself—whether the model perceives a building as a graph of connected physical components—is a first-order determinant of accuracy. Given the field’s trajectory, graph-based hybrid surrogates are likely to spread quickly beyond steel moment frames to shear-wall buildings, reinforced concrete frames, and increasingly to million-scale urban clusters, where related AI frameworks are already being developed for city-scale simulation.

The research was supported by the National Natural Science Foundation of China. As urban populations in seismically active regions continue to grow, tools that can predict, quickly and accurately, how an entire city’s buildings will respond to shaking are moving from academic aspiration to operational necessity. This study offers a concrete, open, and demonstrably accurate step in that direction—teaching machines to see buildings the way structural engineers do: as systems, not statistics.

The research, led by Tao Wang of Heilongjiang University of Science and Technology and Guoshan Xu of Harbin Institute of Technology, together with Rui Pan, Liyan Meng and Jisheng Liu, introduces a novel quantitative method for analyzing the floor acceleration amplification (FAA) factor based on actual recorded earthquake response data from instrumented buildings. Rather than relying on the numerical simulations that have dominated the field, the team mined the archives of the Center for Engineering Strong Motion Data (CESMD), extracting recorded building responses that capture how structures genuinely behave when the ground moves beneath them.

The core problem the researchers set out to solve is well known in earthquake engineering circles. “The existing literature on FAA did not analyze various influencing factors quantitatively,” the authors note in their abstract. Design codes around the world — including ASCE 7 in the United States, Eurocode 8 in Europe, New Zealand’s NZS 1170.5 and China’s GB 50011 — contain provisions for computing seismic design forces on nonstructural components, and most of them lean heavily on a single variable: the height of the component within the building. The implicit assumption is that the higher you go, the more the acceleration is amplified, with the relationship varying according to the structural system. But how much do other factors — the soil beneath the building, the building’s height, its fundamental period, the intensity of the ground motion itself — actually matter? Previous work had hinted at their significance, but rarely in quantitative terms grounded in field data.

The new method treats the FAA factor as a multi-variable statistical problem. For each instrumented building and recorded event, the researchers characterized six influencing factors: structural type, structural height, site category, structural period, relative height (the component’s elevation expressed as a fraction of total building height) and ground motion intensity. They then computed FAA values from the recorded peak floor accelerations and peak ground accelerations, and applied formal correlation analysis to determine which of these factors were genuinely and strongly associated with amplification behavior — and which could be safely neglected or, conversely, demanded explicit inclusion in design formulations.

The results are striking in at least one respect that the authors say has been underappreciated. Site category — the classification of the soil or rock beneath a building, which governs how ground motion characteristics are modified before reaching the foundation — was found to significantly affect FAA values across different types of structures. “The site categories can significantly affect the FAA values of various types of structures,” the study reports, adding that this influence “has not been emphasized in previous studies.” In practical terms, this means that two structurally identical buildings on different soil conditions may subject their rooftop equipment to markedly different acceleration demands during the same earthquake — a distinction that current code formulas for nonstructural components largely fail to capture in a refined way.

The correlation analysis also delivered a clear verdict on relative height. This factor, the study confirms, is strongly correlated with FAA, a finding consistent with the structure of several seismic design codes that scale nonstructural design forces with elevation. In other words, the codes’ long-standing intuition that amplification grows with height holds up under scrutiny with real data. But the analysis went further, revealing that three additional parameters — site category, structural height and structural type — are also significantly correlated with the FAA factor. On the strength of this evidence, the authors argue that these three factors should be incorporated into seismic design codes for acceleration-sensitive nonstructural components, a recommendation that, if adopted, would represent a meaningful refinement of the simplified equations engineers currently use.

The technical significance of the work lies partly in its grounding in instrumented buildings. Numerical models, however sophisticated, embed assumptions about damping, stiffness distribution, and inelastic behavior that may or may not reflect reality. Records from buildings during actual earthquakes — the kind curated by CESMD — carry no such assumptions. They are the ground truth of structural dynamics, capturing everything from soil-structure interaction to the softening effects of yielding that influence how accelerations propagate upward. By quantifying FAA influencing factors directly from such records, the method sidesteps a major source of modeling uncertainty, though it also inherits the practical constraints of any observational dataset: the available buildings, sites and shaking intensities are those that earthquakes happened to provide.

Why does this matter beyond the technical literature? Post-earthquake damage surveys have repeatedly shown that nonstructural components account for a large share of earthquake losses, both direct and indirect. Investigations after the 2010 Chile earthquake, the 2011 Christchurch earthquake and the 2016 Central Italy earthquake documented widespread failures of ceilings, facades, pipes and equipment, often in buildings whose primary structures performed well. Failed fire suppression lines can render a fire-fighting system useless precisely when it is needed; toppled switchgear can knock a hospital out of operation even if the building stands. Accurately predicting the acceleration that such components will experience — which is precisely what the FAA factor enables — is therefore central to estimating their seismic safety and to the broader goal of resilient, functional cities.

The study’s authors frame their contribution as offering “valuable insights and recommendations for the design of acceleration-sensitive nonstructural components in terms of FAA.” The quantitative method they propose gives code developers and practicing engineers a data-backed way to weigh the competing influences on floor acceleration demands, rather than relying on convention or on idealized simulations. It also provides a template for future studies: as seismic instrument arrays expand and more instrumented buildings record more events, the same statistical framework can be applied to ever-larger datasets, potentially refining amplification models region by region and structure type by structure type.

There are, of course, limitations inherent in any data-driven approach, and the researchers’ recommendation to add site category, structural height and structural type to code formulations will require careful implementation. Design equations must remain tractable; adding variables increases both computational burden and the potential for misclassification in practice. But the study’s central message is hard to dismiss: the acceleration that shakes a component on the tenth floor is not determined by height alone, and the ground beneath the building — the very first link in the chain of seismic response — deserves a more prominent place in how engineers compute nonstructural design forces.

The research was supported by the National Natural Science Foundation of China under grants 52278173, 52378150 and 52078398, and by the Foundation of the Key Laboratory of Structures Dynamic Behavior and Control (Ministry of Education) at Harbin Institute of Technology. Corresponding author Guoshan Xu led the collaboration spanning Heilongjiang University of Science and Technology and Harbin Institute of Technology, institutions at the heart of China’s earthquake engineering research community. As cities grow taller and their contents grow more technologically sensitive — data centers, laboratories, hospitals dense with equipment — the question of what accelerations those contents will feel in the next major earthquake is only becoming more consequential. This study offers a rigorous, record-based answer to part of that question, and a clear signal about where design codes should go next.

The method, developed by Rita Couto, Gianrocco Mucedero, Besim Yükselen, Rita Bento and Ricardo Monteiro, focuses on what the researchers call storey loss functions. These functions estimate the expected loss associated with each floor of a building as earthquake damage increases. Instead of modelling every beam, column, wall, pipe and electrical component individually, engineers can use a calibrated relationship between a building’s seismic response and the likely financial and environmental consequences on each storey. The approach is intended for poorly detailed reinforced-concrete buildings, a broad category that includes many older structures designed before modern seismic requirements became widespread.

The central insight is that earthquake loss is not distributed uniformly through a building. A floor with extensive masonry infill walls, damaged ceilings, heavily occupied rooms or vulnerable mechanical services may generate much greater repair costs than another floor, even when the primary concrete frame remains standing. Storey loss functions preserve this vertical resolution while reducing the computational burden of a detailed building assessment. They can identify which levels and component groups are likely to dominate total losses, helping analysts compare buildings and helping engineers target retrofitting where it may produce the greatest reduction in risk.

To construct the functions, the team combined several layers of earthquake-engineering information. First, they characterised representative reinforced-concrete building types and identified the structural and non-structural components that can be damaged by seismic shaking. They then used fragility curves to describe the probability that a component will reach a particular damage state as a function of an engineering demand parameter, such as interstorey drift. Drift is the relative horizontal displacement between two adjacent floors, usually expressed as a fraction of the storey height. It is a crucial indicator because excessive drift can crack infill walls, distort partitions, damage façades and overload connections even when the main frame avoids collapse.

A fragility curve converts that physical demand into a probability of damage. At low drift, a wall or service system may have a small chance of being damaged; as drift rises, the probability of slight, moderate or severe damage increases. The researchers linked those damage probabilities to consequence functions, which estimate what each damage state means in practical terms. For economic loss, the consequence may be the cost of labour, replacement materials, equipment and other repair activities. For environmental impact, it may include the impacts associated with producing, transporting and installing replacement materials, as well as removing and disposing of damaged components.

This combination allows a storey-level calculation to retain information about different types of damage. A reinforced-concrete column, a masonry infill wall, a gypsum partition, a façade element and a building service can respond very differently to the same earthquake demand. Their fragility functions may depend on different measures of shaking, and their repairs may involve very different quantities of material and labour. By disaggregating the results, the framework can reveal whether a building’s losses are driven mainly by structural repairs, non-structural damage or systems such as plumbing and electrical installations. That distinction matters because a building that is safe to occupy structurally may still be expensive, carbon-intensive or time-consuming to restore if its non-structural systems are extensively damaged.

The environmental side of the framework is particularly significant because seismic risk assessments have traditionally concentrated on monetary losses, casualties and downtime. Repairing or replacing damaged building components produces what researchers often describe as embodied environmental impacts: emissions and resource use associated with materials and construction processes rather than with a building’s day-to-day operation. Concrete replacement can require cement production, an energy-intensive process; steel repairs involve extraction and manufacturing; and widespread replacement of partitions, finishes and services can create large flows of waste. In this study, environmental impacts are integrated into the same damage-assessment chain as repair costs, making it possible to examine both consequences together rather than treating sustainability as a separate calculation.

The researchers developed generalised functions for poorly detailed reinforced-concrete buildings in Europe and also derived versions tailored to Portugal. The national adaptation does not require rebuilding the entire methodology from the ground up. Instead, it can incorporate country-specific cost conversion factors, repair practices and environmental-impact factors. Labour prices, material costs, construction methods, waste-management systems and the carbon intensity of energy can vary substantially between countries, so a function calibrated in one setting should not automatically be interpreted as a precise prediction elsewhere. The Portuguese functions demonstrate how regional data can refine a broader European model while preserving a common technical structure for comparison.

The study’s case example applied both the storey-based and component-based approaches to a reinforced-concrete building in Portugal. The comparison was designed to test whether the more compact storey functions could reproduce the essential patterns identified by a detailed analysis. According to the researchers, the exercise illustrates the applicability of the proposed framework, while also showing why the two methods serve different purposes. Component-level modelling can provide highly detailed information when a building has been surveyed extensively, but it demands substantial data and computational effort. Storey functions are less granular, yet they can support rapid screening, portfolio-scale risk assessment and early-stage retrofit planning when only limited information is available.

That scalability could make the method useful for cities and national agencies facing large inventories of ageing buildings. A regional seismic model may contain thousands or millions of structures, many of which lack complete drawings, material records or component inventories. Running a fully detailed nonlinear analysis for each one would be impractical. Storey loss functions offer a middle ground between crude building-wide averages and exhaustive simulations. They can be combined with seismic hazard models to estimate losses across a portfolio, compare the expected benefits of retrofit programmes and identify buildings where structural safety and environmental performance should be considered together.

The framework also fits into a broader shift in earthquake engineering toward performance-based assessment. Rather than asking only whether a building collapses, performance-based methods examine several possible damage states, from minor repairable damage to severe damage requiring replacement or demolition. Each state can be associated with consequences for cost, occupancy, functionality and environmental impact. This is important because most earthquake-related losses do not necessarily arise from complete structural collapse. Non-structural damage can interrupt building use, force occupants to relocate and generate substantial repair demand, particularly in residential buildings where walls, finishes and services make up a large portion of the total replacement effort.

The approach may also influence decisions about retrofitting. Strengthening an existing building can reduce the probability of severe earthquake damage, but retrofit work itself consumes materials, energy and money. A decision based only on structural performance might favour the intervention that produces the greatest increase in strength or ductility. A combined economic and environmental assessment can ask a more complicated question: which intervention produces the best balance between reduced future earthquake damage and the impacts created by construction today? The authors’ framework is not presented as an optimisation result for every building, but its ability to identify critical loss contributors could supply the information needed for such comparisons.

The researchers emphasise that the methodology is transferable, not universally fixed. Applying it to another seismic region requires fragility models that represent the local building stock, particularly the behaviour of poorly detailed reinforced-concrete frames and their infill walls. It also requires suitable data on repair costs, construction practices and environmental-impact factors. Regional differences in reinforcement details, masonry materials, workmanship, building layouts and seismic design history can all alter vulnerability. The functions should therefore be treated as generalised tools for estimation rather than substitutes for detailed inspection where safety-critical decisions depend on building-specific evidence.

Data generated and analysed in the study are available from the corresponding author on reasonable request. The work was carried out through research activities associated with CONSTRUCT, CERIS and the SERENE project, with additional support from Italian seismic-risk and building-renovation initiatives. By connecting the mechanics of earthquake damage to both financial and environmental consequences, the study offers a framework for a problem that is becoming harder to ignore: existing buildings must be made safer, but the materials and construction required to repair or strengthen them also affect the planet. Faster, storey-resolved estimates could help turn that trade-off into a measurable part of seismic planning rather than an afterthought discovered only after the ground stops shaking.

The UNU-INWEH report highlights that over 13,000 buildings sustained either severe damage or complete collapse within a 50-kilometer radius from the earthquake’s epicenter. Villages in the affected zone suffered near-total devastation, with some losing upwards of 90% of their housing stock. The overwhelming majority of structures in this earthquake-prone region consist of mud bricks and rudimentary stone constructions, lacking seismic resilience or modern engineering principles. This construction typology, combined with decades of economic deprivation, has left inhabitants with dwellings particularly susceptible to even moderate seismic shaking.

Seismic experts emphasize the earthquake’s shallow focal depth of approximately 8 kilometers beneath the surface. Such shallow earthquakes generate intense ground accelerations directly beneath populated areas, exacerbating structural damage. Compounding the problem, the tremor struck just before midnight, a time when residents were indoors and asleep, markedly increasing casualties as buildings gave way without warning. This timing, coupled with pre-existing ground saturation caused by weeks of heavy monsoon rainfall, triggered landslides that not only devastated villages but also hampered rescue and relief operations by blocking vital roadways.

Afghanistan’s prolonged conflict and political instability have severely impeded progress in disaster preparedness and resilient infrastructure development. The Taliban’s ascendancy in 2021 further isolated the country diplomatically and economically, leading to a significant reduction in international aid flows. This isolation crippled emergency response capabilities at a critical moment when humanitarian assistance was urgently needed. The UNU-INWEH assessment underscores that a fragile governance environment and dwindling financial support have severely handicapped both mitigation and response mechanisms.

The report also draws attention to the disproportionate suffering of women and girls in the aftermath of the earthquake. Gender-based restrictions deeply entrenched in Afghan society restrict women’s access to timely healthcare, especially when female patients are prohibited from being treated by male doctors without a chaperone. Furthermore, bans on female education in medicine and restrictions on female aid workers limited the availability of female healthcare professionals, compounding barriers to essential medical care for women and girls trapped in disaster zones.

From a geophysical perspective, ground deformation measurements using satellite and geospatial technologies recorded surface displacements of up to 23 centimeters during the mainshock. A significant aftershock on September 4 added approximately 17 centimeters of ground movement in affected areas, prolonging structural instabilities and complicating recovery efforts. This precision geospatial data was crucial in mapping the most impacted zones and coordinating targeted relief and rebuilding initiatives.

The combination of geology, infrastructure vulnerability, and socio-political constraints presented a perfect storm for tragedy. Traditional construction materials and building techniques in Kunar Province, while customary, failed to withstand the dynamic stresses imposed by the earthquake. With slopes already saturated by monsoonal rains, the resulting landslides displaced communities and blocked crucial access routes, delaying aid and leaving survivors isolated.

Disaster experts warn that without sustained international engagement and investment in resilient infrastructure, Afghanistan remains perilously exposed to future seismic events. The UNU-INWEH team advocates for a comprehensive approach embedding seismic risk reduction within urban planning, engineering, and social frameworks. This includes enforcing building codes adapted to local seismic hazard profiles and integrating gender-sensitive disaster response protocols to ensure that vulnerable groups receive equitable assistance.

Moreover, the report brings forth the critical need to reform global humanitarian frameworks to decouple the provision of disaster relief from political considerations. Afghanistan’s diplomatic isolation significantly shortened the reach and effectiveness of global aid agencies, illustrating how geopolitical factors can exacerbate natural disaster impacts. Ensuring that populations under sanctioned or politically contentious regimes are not excluded from life-saving humanitarian aid is essential for global disaster resilience.

This earthquake has delivered a stark reminder that seismic hazards transcend physical phenomena and demand integrated approaches that address infrastructure, governance, social equity, and international cooperation. The compelling case of Kunar Province underscores how socio-economic fragility and political isolation amplify natural disasters’ toll, turning moderate tremors into catastrophic human suffering.

Looking ahead, the UNU-INWEH findings call for urgent, collaborative global efforts to rebuild Afghanistan’s disaster management systems, paired with long-term investments in health, education, and infrastructure to fortify communities. Only through such multifaceted efforts can similar calamities be mitigated in the future, ensuring safer living conditions for millions caught in the seismic crosshairs of this geopolitically complex region.

The earthquake in Afghanistan’s eastern borderlands represents a convergence of natural forces with human vulnerabilities shaped by history and policy. Seismologists and humanitarian experts alike agree that investments in resilient construction and inclusive disaster response, alongside political engagement, are imperative to reduce future calamities. As this region faces a prolonged recovery, the lessons learned offer critical insights into disaster risk management in geopolitically fragile zones worldwide.

Subject of Research: Damage Assessment and Socio-Political Impact of the August 2025 Earthquake in Afghanistan

Article Title: Damage Assessment of Afghanistan’s August 2025 Earthquake

News Publication Date: 18 September 2025

Web References:
https://collections.unu.edu/eserv/UNU:10335/UNU_INWEH_INCIDENT_BRIEF_Afg_Earthquake_2025.pdf
https://unu.edu/inweh/collection/damage-assessment-afghanistans-august-2025-earthquake

References:
Shirzaei, M., Daqiq, M. T., Lucy, J., Werth, S., Sharma, R., Velasco, M. J., Matin, M., Madani, K. (2025). Damage Assessment of Afghanistan’s August 2025 Earthquake. United Nations University Institute for Water, Environment and Health (UNU-INWEH), Richmond Hill, Ontario, Canada, doi: 10.53328/INR25MSIR002

Keywords: Earthquakes, Seismology, Disaster Risk Reduction, Infrastructure Vulnerability, Gender and Disaster, Humanitarian Aid, Afghanistan Earthquake 2025, Geospatial Analysis, Political Isolation, Human Security

Earthquakes are among the most destructive natural events, causing significant infrastructure damage and loss of life. Effective assessment of the damage following such events is crucial for the timely provision of aid and recovery resources. Traditionally, assessments relied on optical imagery from satellites, which, while useful, has its limitations, particularly under certain atmospheric conditions. The study reveals how radar satellites can overcome these challenges, providing a clearer and more detailed picture of earthquake-affected areas.

The researchers conducted a comparative analysis of the data from radar satellites and traditional optical satellites in the aftermath of a significant earthquake. The findings were striking; radar technology enables sensing through clouds, rain, and even during nighttime. This consistent performance under various weather conditions is critical in regions prone to seismic activity, where timely access to reliable data can directly impact the effectiveness of emergency responses.

In their research, the authors employed advanced synthetic aperture radar (SAR) techniques that allow for high-resolution imaging of the Earth’s surface. SAR captures the reflections of microwave signals emitted towards the ground, which are then processed to create detailed images of the landscape. The resulting data from SAR can effectively highlight changes in the land surface caused by seismic events, such as ground displacement and structural damage.

Ainscoe and his team meticulously studied different urban areas affected by earthquakes. They provided compelling evidence that radar imagery not only captures the extent of the destruction but also reveals subtle deformations in the landscape. These details can help engineers and urban planners assess the structural integrity of buildings and roads, enabling targeted interventions where they are needed most.

Furthermore, the data demonstrates that the analytical capabilities of radar satellites can enhance situational awareness for disaster management agencies. The precision of radar mapping allows for the identification and prioritization of areas that require urgent attention, thereby optimizing resource allocation and response efforts. The implications for humanitarian logistics are profound, particularly in regions with limited infrastructure or accessibility following a disaster.

The study’s authors emphasize that the technology transitions the paradigm of disaster response from reactive to proactive. By utilizing real-time radar data, agencies can better prepare for future earthquakes, implement risk reduction strategies, and enhance community resilience against seismic threats. This proactive approach ultimately reduces vulnerability and enhances safety in disaster-prone areas.

Moreover, the integration of radar satellite data into existing disaster management frameworks represents an exciting frontier in geographic information systems (GIS). The balance between technological innovation and practical application is paramount. As governments and NGOs increasingly turn to digital solutions for disaster preparedness, radar technology can provide the critical insights needed to inform policy and planning.

Collaboration between scientists and policymakers is essential for operationalizing these findings. The engagement of stakeholders ensures that radar imagery is effectively incorporated into disaster management protocols. This integration leads to a more robust understanding of seismic risk and informs engineering practices, ultimately saving lives and infrastructure.

In summary, the study by Ainscoe et al. not only advances our understanding of the potential of radar satellites in disaster response but also paves the way for future research in remote sensing applications. The findings call for a broader adoption of radar technology in monitoring various natural disasters, not just earthquakes. As we continue to face the challenges posed by climate change and natural hazards, embracing innovative solutions like radar imaging could become indispensable in safeguarding populations around the globe.

In conclusion, the implications of this research extend far beyond the immediate benefits of improved damage assessment. The findings represent a significant leap toward harnessing the power of modern technology for better disaster resilience. As we embark on this new chapter in earthquake monitoring, it is essential to recognize the transformative impact of satellite technology in protecting lives and shaping safer, more prepared communities.

While this study sheds light on the effectiveness of radar satellites, it also opens the door for future explorations. Questions remain regarding the potential of integrating radar data with other remote sensing technologies, such as drones and ground-based sensors, maximizing the breadth and depth of analysis available to disaster response teams. The future of disaster readiness lies in such interdisciplinary approaches, combining technological advancements with strategic planning to create a holistic framework for managing natural disasters.

Subject of Research: Earthquake damage assessment using radar satellite technology

Article Title: Earthquake damage mapped more comprehensively and accurately by radar satellites than optical imagery

Article References:

Ainscoe, E.A., Swaminathan, R., Way, L. et al. Earthquake damage mapped more comprehensively and accurately by radar satellites than optical imagery.
Commun Earth Environ 6, 631 (2025). https://doi.org/10.1038/s43247-025-02623-4

Image Credits: AI Generated

DOI: 10.1038/s43247-025-02623-4

Keywords: Earthquake, radar satellites, optical imagery, damage assessment, disaster management, remote sensing