The stakes could hardly be higher. Kabul’s population has exploded from around one million in 2001 to more than 5.3 million in 2021, and over 70 percent of its residents live in unplanned or informal settlements, many of them perched on steep, deforested hillsides or crowded into low-lying districts with inadequate drainage. Between 2000 and 2020, built-up areas expanded by 40 percent while agricultural land shrank by 32 percent. Yet until now, hazard information for the city has been fragmented across consultancy reports and isolated single-hazard studies, leaving planners without a coherent map of where dangers converge. The new research was designed precisely to close that gap in a place where reliable environmental data is scarce and field campaigns are often impossible.

The team focused on five hydro-climatic and environmental hazards: flood, heat, drought, soil erosion and groundwater stress. Earthquakes and landslides were deliberately excluded, the authors explain, because they require fundamentally different assessment frameworks and mitigation approaches. For each of the five hazards, the researchers selected a set of conditioning parameters, nine in total across the analysis, including elevation, slope, soil type, drainage density, precipitation, land use and land cover, and groundwater depth, depletion and quality. These were rated and weighted using the Analytic Hierarchy Process, a structured multi-criteria decision analysis method developed by Thomas Saaty, implemented within a geographic information system. The heat hazard took a different route: the team retrieved land surface temperature from a Landsat 8 image acquired on 18 June 2020, the hottest day of the study period with the lowest cloud cover, and flagged every pixel exceeding 40 degrees Celsius as a high-hazard zone.

The data engineering behind the maps is a case study in making the most of thin evidence. Land use classifications were built by merging Esri Sentinel-2 and OSM Geofabrik datasets, achieving a Kappa coefficient of 0.85, a measure of classification agreement that indicates high reliability. Precipitation surfaces were interpolated from a decade of observations spanning 2013 to 2022, drawn from the CHIRPS v3.0 and PERSIANN-CCS satellite products and averaged using inverse distance weighting, a method chosen because it demands no assumptions about spatial autocorrelation in a region with sparse and unevenly distributed gauges. Groundwater nitrate measurements were spread across the city using Thiessen polygons, while groundwater depth and depletion trends were interpolated with kriging. Every raster layer was resampled to a common 30-metre resolution to keep the analysis spatially consistent.

The individual hazard maps tell striking stories on their own. Flood hazard dominates the picture: roughly 60 percent of the study area falls into the high category and another 10 percent into the very high category, concentrated in the central and eastern districts where gentle slopes, low elevations and outdated drainage allow surface water to accumulate. Very high flood zones cluster alarmingly in densely built districts such as D11 and D15, which likely host critical infrastructure. Heat hazard shows a different geography, with the very high class covering about 6 percent of the area, chiefly in southern and northeastern districts blanketed by impervious surfaces and starved of vegetation, the classic signature of the urban heat island effect. Drought hazard inverts the flood pattern, concentrating in the steeper, drier southern and southwestern districts, a spatial mutual exclusivity the authors attribute to climatic and geomorphological gradients.

Groundwater stress may be the quietest crisis of the five. Kabul depends almost entirely on two shallow Quaternary aquifers, the central Kabul and Paghman-Darulaman aquifers, which have been relentlessly overexploited. Water tables have dropped by up to 60 metres in some areas, and contamination from sewage infiltration and agricultural runoff is increasingly compromising quality. The assessment found 46 percent of the area in the high groundwater stress category and 17 percent in the very high category, with districts such as D11 and central D06 showing severe nitrate contamination alongside marked depletion. Soil erosion, by contrast, remains low across most of the city, though 4.3 percent of the area, particularly the sloping northern peripheries of districts D11 and D15, shows high susceptibility where bare, steep terrain accelerates soil detachment and runoff.

The real innovation lies in how these layers were combined. Each hazard map was reclassified into a binary layer, with moderate, high and very high zones coded as hazard presence and low zones as absence. Summing the binary values pixel by pixel produced a composite map showing how many hazards overlap at any location, while a second, more elegant technique assigned each hazard a unique power-of-two weight, from 1 for heat to 16 for drought, and multiplied the binary rasters together. Because five hazards yield 32 possible combinations, every pixel could be decoded to reveal its exact hazard cocktail. The result: single-hazard zones cover just 1.8 percent of the city, two-hazard zones 18.6 percent, three-hazard zones 44.3 percent, four-hazard zones 32.9 percent, and areas facing all five hazards 2.8 percent.

The combination analysis is where the findings become genuinely actionable. The most widespread pairing is groundwater stress with heat, covering 17.79 percent of the area, followed by the triple combination of soil erosion, groundwater stress and heat at 18.81 percent. Flood paired with groundwater stress covers 8.47 percent, soil erosion with heat 8.54 percent, and drought with groundwater stress and heat 8.16 percent. These clusters, the authors argue, are exactly the kind of spatial intelligence needed to target nature-based solutions, interventions such as floodplain restoration, vegetated slope stabilization, urban green corridors and recharge area protection that can address multiple hazards at once while delivering co-benefits for biodiversity, climate resilience and public well-being. A flood-prone corridor might call for water retention landscapes and river management, while drought-exposed southern districts need soil moisture enhancement and drought-resilient vegetation.

The researchers are candid about the limits of their work. The maps represent relative hazard susceptibility, not probabilistic forecasts: they do not quantify hazard magnitude, recurrence intervals or expected losses, and they exclude population exposure and socio-economic vulnerability. Validation relied on qualitative comparison with previous studies and official reports rather than field verification, which was infeasible under prevailing conditions in Afghanistan. Temporal inconsistency is another caveat, with heat derived from a single 2020 satellite image while precipitation data span a decade. Interpolation of sparse groundwater measurements and the binary simplification of hazard zones introduce further uncertainty. Still, the framework is transparent, replicable and explicitly designed for data-scarce cities of the Global South, and the authors sketch a research agenda that includes machine-learning comparisons, climate scenario analysis and dynamic hazard modelling.

For Kabul’s planners and for cities across the rapidly urbanizing Global South, the study marks a foundational shift from asking which hazard threatens a neighbourhood to asking which combination does. With more than 90 percent of the city exposed to at least one hazard and vast swaths facing three or more, single-hazard thinking is no longer defensible. What this map offers is a starting point for risk-sensitive land-use planning and ecosystem-based resilience strategies in one of the world’s most challenging urban environments, a city where, as the data now make unmistakably clear, the crises are not waiting their turn.

Subject of Research: Integrated geospatial multi-hazard assessment of flood, heat, drought, soil erosion and groundwater stress in Kabul, Afghanistan

Article Title: Unveiling Multi-Hazard Hotspots in Kabul: An Integrated Geospatial Assessment in a Data-Scarce Urban Environment

Article References: Rafiee, M., Ansari, M. R., Kempa, D., Kuhn, T., Esmail, B. A., Biber-Freudenberger, L., & Albert, C. (2026). Unveiling Multi-Hazard Hotspots in Kabul: An Integrated Geospatial Assessment in a Data-Scarce Urban Environment. Environmental Management, 76(9), Article 300. https://doi.org/10.1007/s00267-026-02588-w

Image Credits: AI Generated

DOI: 10.1007/s00267-026-02588-w

Keywords: Kabul, multi-hazard assessment, GIS, flood hazard, urban heat island, groundwater stress, drought, soil erosion, nature-based solutions, Analytic Hierarchy Process, remote sensing, urban planning

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

Coastal vulnerability indices have been around since the early 1990s, when researchers first tried to condense the many ingredients of coastal risk into a single number that planners could act on. The classic recipe is deceptively simple: measure a set of variables, such as shoreline erosion rate, coastal slope, tidal range, wave height, sea level rise and population density, rescale them all to a common range, weight them, and add or multiply them together. The result is a map that flags which segments of coastline deserve the most urgent attention. But the traditional approach carries a well-known weakness. It treats every indicator as if it behaved independently, when in reality the factors that drive coastal disaster are deeply intertwined. A gently sloping shore amplifies storm surge, which in turn erodes beaches, which removes the natural buffer protecting dense settlements behind them. Adding or multiplying numbers cannot represent that chain of dependency.

The Kerala team’s answer is to move from deterministic arithmetic to probabilistic statistics. Their framework begins by organizing the drivers of vulnerability into three components: coastal characteristics, which cover the geological and geomorphological makeup of the shore; coastal forces, which capture the physical energy acting on it, from waves and tides to sea level rise; and socioeconomic conditions, which describe the people and infrastructure exposed to harm. Each component is first processed separately, and then the three resulting sub-indices are combined in three different ways. Two of them, an additive index and a multiplicative index, follow the conventional deterministic route. The third, the probabilistic index, uses a copula, a mathematical construct that models the joint probability distribution of several variables while allowing each to keep its own individual distribution and allowing the dependencies among them to be nonlinear and asymmetric.

Copulas have become a workhorse of modern hydrology and risk analysis precisely because they separate the question of how a variable behaves on its own from the question of how it moves together with other variables. In the Kerala application, the copula-based index effectively asks how likely it is that a given taluk simultaneously experiences adverse geological conditions, adverse physical forcing and adverse socioeconomic exposure, rather than simply averaging the three. The distinction matters because extremes cluster. A place with fragile geology is often also a place where the physical forces are harshest, and where the population has the least capacity to absorb a loss. A deterministic average can dilute that coincidence; a probabilistic joint model preserves and quantifies it.

Before any of that aggregation could happen, the raw data had to be made comparable. The indicators in the study come from wildly different sources and scales: satellite-derived sea level measurements, bathymetric grids, geomorphological maps, land use classifications, population density rasters and infrastructure datasets. The researchers used fuzzy logic-based normalization to transform every criterion value onto a common scale, an approach that handles the vagueness inherent in terms like high vulnerability better than a simple linear rescaling. The weights, which determine how much each indicator counts in the final score, were then assigned objectively using the entropy method, which derives weights from the spatial variability of the data itself. Indicators that vary sharply from place to place, and therefore carry more information for discriminating between locations, receive higher weights, while indicators that are nearly uniform along the coast receive lower ones. This removes a layer of subjective judgment that has long troubled vulnerability mapping.

From the weighted, normalized indicators the team computed three component indices: a Geological Vulnerability Index, a Physical Vulnerability Index and a Socio-Economic Vulnerability Index. Each taluk along the Kerala coast received a score on all three, and the three were then fused into the additive, multiplicative and probabilistic versions of the overall index. The comparison among the three aggregation schemes is one of the most instructive parts of the study, because it shows how much the choice of mathematics changes the map. The additive and multiplicative indices, built on deterministic assumptions, produce one ranking of vulnerable places. The probabilistic index, which lets the components interact statistically, produces another, and the differences are not cosmetic. They shift which communities appear at the top of the priority list.

The decisive test came from history. On 26 December 2004, the Indian Ocean tsunami struck the Kerala coast, and the pattern of inundation it left behind has been documented in detail by earlier coastal scientists. If a vulnerability index is doing its job, the places it flags as most vulnerable should broadly match the places the tsunami actually flooded most severely. When the researchers checked their three indices against the tsunami’s coastal impacts, the copula-based probabilistic index matched the observed pattern most faithfully. By accounting for the nonlinear dependencies among geological, physical and socioeconomic factors, it produced a more realistic representation of which stretches of coast were genuinely at risk, outperforming the deterministic alternatives that the field has relied on for decades.

The geographic findings carry immediate weight for Kerala, a densely populated state whose coastline is lined with fishing communities, backwater ecosystems, ports and a tourism economy that depends on the sea. The identification of Ambalappuzha, Kochi and Karunagapalli as the most vulnerable taluks gives the Kerala State Disaster Management Authority and local planners a spatially explicit priority list. Kochi, the state’s commercial hub, combines low-lying terrain and heavy infrastructure exposure with the hydrodynamic setting of a major estuary. Ambalappuzha and Karunagapalli sit on low coastal plains where modest elevations meet energetic wave and tidal forcing and dense rural settlement. The authors frame the framework as providing critical spatial intelligence for enhancing coastal disaster preparedness, adaptive planning and evidence-based policy formulation, and they emphasize that the method is transferable: any coastline with the requisite data can be scored the same way.

The broader significance of the work lies in what it says about how risk science is evolving. Vulnerability assessment began as an exercise in expert judgment and simple arithmetic, and it is now absorbing the machinery of modern statistics and machine learning, from fuzzy normalization and entropy weighting to copula-based joint probability modeling. As climate change accelerates sea level rise and intensifies storm surges, the cost of misranking a coastline grows. A map that understates the joint probability of fragile geology, fierce physical forcing and dense vulnerable populations can steer seawalls, early warning systems and evacuation planning to the wrong places. The Kerala study suggests that probabilistic aggregation is not a statistical nicety but a practical improvement in where the money and attention should go.

There are, of course, limits that the authors themselves acknowledge in the structure of the work. The index is a snapshot built from present-day data, and its accuracy depends on the quality and currency of the underlying datasets, from satellite altimetry to census-derived population grids. The entropy weighting, though objective in derivation, reflects the spatial variability of the study region and would need recalibration elsewhere. Yet the validation against the 2004 tsunami gives the framework an empirical anchor that many vulnerability studies lack, and the fact that the probabilistic version outperformed its deterministic siblings makes a clear case for the approach. For the fishing families living within meters of the high tide line on the Kerala coast, and for the millions more who inhabit low-lying deltas around the world, the message is that the mathematics of vulnerability is finally catching up with the physics and the human geography of the places most likely to flood first.

Subject of Research: A copula-based probabilistic coastal vulnerability index applied to the Kerala coast, India

Article Title: An integrated coastal vulnerability index using a copula-based approach: a case study of Kerala, India

Article References: An integrated coastal vulnerability index using a copula-based approach: a case study of Kerala, India. (n.d.). https://doi.org/10.1007/s11069-026-08418-w

Image Credits: AI Generated

DOI: 10.1007/s11069-026-08418-w

Keywords: coastal vulnerability index, copula, Kerala, sea level rise, fuzzy normalization, entropy weighting, tsunami validation, natural hazards, disaster preparedness, multi-hazard assessment, coastal management, India

The concept at the heart of the paper is seismic uplift fragility, defined as the conditional probability that pile uplift will occur given a particular ground motion intensity measure. Fragility analysis is a cornerstone of modern earthquake engineering because it converts the messy, uncertain physics of soil-structure interaction into probabilistic statements that can feed directly into risk assessment and design codes. Previous research had established that scour removes overburden soil around the pile cap, amplifies pile-cap rotations during shaking, and can trigger uplift of the piles themselves. But the authors note that the seismic uplift behavior of scoured pile-group foundations had never been probabilistically quantified, leaving a significant gap in multi-hazard bridge assessment.

To close that gap, the team built an extensive numerical modeling campaign. They established 49 archetype bridge configurations by varying twelve structural and geotechnical parameters, including pile length, pile spacing, axial load ratio, and the longitudinal reinforcement ratio of the piles. The bridges were founded in cohesionless soils, meaning sandy, non-clayey sediments of exactly the kind that scour attacks most aggressively and that are common beneath river and coastal crossings. Each archetype was subjected to suites of ground motions, and the resulting simulations were mined for the quantities that best track uplift behavior.

A central technical contribution of the study is the careful selection of the response metrics used in the probabilistic demand models. The researchers systematically compared candidate engineering demand parameters and intensity measures, and concluded that the peak rotation of the pile cap is the optimal engineering demand parameter for quantifying uplift, while the Housner intensity, a measure based on the energy content of the ground motion, is the optimal intensity measure. Crucially, they found that pile-cap rotation is more efficient and more practical than the pile cap’s lateral displacement for characterizing uplift demand. Rotation, it turns out, directly captures the rocking mechanism that precedes uplift, whereas lateral displacement conflates rocking with sliding and bending responses, diluting the statistical relationship.

With demand quantified, the team then turned to capacity. Uplift capacity models were derived from the pile-cap rotations at the onset of uplift, effectively defining the rotation threshold beyond which part of the pile group begins to pull free of the surrounding soil. When a rocking foundation reaches this point, load redistributes to the remaining compressed piles, soil resistance on the uplifted side vanishes, and the energy dissipation and recentering behavior of the foundation change fundamentally. Combining the probabilistic demand models with these capacity models yielded the study’s headline output: seismic uplift fragility curves for scoured pile-group foundations in cohesionless soils.

The numbers are sobering. For the base bridge model, the probability of uplift reaches 82 percent under a seismic hazard level associated with a 475-year return period, the design-level event around which many modern seismic codes are organized. In other words, a bridge whose foundations have been scoured has a very high likelihood of experiencing pile uplift when struck by an earthquake of the magnitude engineers already plan for. Because uplift initiates the transition from a ductile rocking response toward potentially uncontrolled foundation movement, an 82 percent probability at the design event represents a serious vulnerability, particularly for the aging river and coastal bridges that dominate many transportation networks.

The parametric analysis that followed is where the study becomes most directly useful to practitioners. The fragility decreases as pile length increases, as pile spacing increases, and as the axial load ratio rises, while it increases as the longitudinal reinforcement ratio of the piles decreases. Among all the parameters examined, pile length was identified as the single most influential factor. Each of these trends has an intuitive physical basis. Longer piles provide deeper embedment and greater resistance to pullout. Wider spacing reduces the overlapping stress zones between adjacent piles, letting each pile mobilize more of the surrounding soil. Higher axial load on the foundation provides a stabilizing, self-weight-driven resistance to rocking and uplift. Conversely, less longitudinal reinforcement weakens the capacity of individual piles to sustain the tension that develops during rocking, making uplift and its consequences more damaging.

These findings resonate with a broader body of experimental work on rocking foundations. Quasi-static tests and shaking table studies by the same research group and by others in recent years have shown that scoured pile-group foundations exhibit distinctive uplift behavior and energy dissipation mechanisms, and that rocking can sometimes even be harnessed as a deliberate design strategy when the foundation is detailed to recenter after shaking. The new study adds the probabilistic layer that such experimental work lacks, translating deterministic observations into fragility statements that can be integrated into regional risk models, resilience assessments, and retrofit prioritization for bridge stocks exposed to both flood scour and seismic hazard.

Recognizing that running full nonlinear finite element simulations of 49 archetype bridges is impractical for routine design offices, the authors also distilled their results into practical tools. They established and validated multivariate linear regression models that allow rapid estimation of uplift fragility from the key design parameters, without the need for expensive simulation. This surrogate modeling approach mirrors a wider trend in earthquake engineering, in which machine-learned and regression-based approximations of physics-based analyses are used to bring probabilistic assessment within reach of everyday engineering workflows. For a bridge engineer wondering whether a scour-critical crossing needs longer piles, wider spacing, or enhanced reinforcement, the regression models offer a first-order answer in seconds.

The broader significance of the work lies in its treatment of concurrent hazards. Flood-induced scour and earthquakes are usually assessed separately, yet they are physically coupled in ways that can dramatically worsen outcomes. Scour weakens exactly the soil-foundation system that must resist seismic demands, and climate-driven changes in flood frequency suggest that more bridges will spend more of their service lives with compromised foundations. By delivering the first probabilistic quantification of seismic uplift fragility for scoured pile-group foundations in cohesionless soils, the Fuzhou University and Tongji University team has given the engineering community both a warning and a toolkit: multi-hazard assessment is not optional for river and coastal bridges, and the levers that most reduce uplift risk, above all pile length, are already in the hands of designers.

Subject of Research: Seismic uplift fragility analysis of scour-affected bridge pile-group foundations in cohesionless soils

Article Title: Seismic uplift fragility of scoured bridge pile-group foundations in cohesionless soils

Article References: Wang, J., Luo, H., Wang, X., & Gu, Y. (2026). Seismic uplift fragility of scoured bridge pile-group foundations in cohesionless soils. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02686-4

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02686-4

Keywords: bridge scour, pile-group foundation, seismic fragility, pile uplift, cohesionless soils, Housner intensity, pile-cap rotation, probabilistic seismic demand, multi-hazard assessment, sensitivity analysis, regression surrogate model, earthquake engineering