The physical risk component of the framework is built on a Python-based computational tool that couples disaggregated exposure data on a 30-arc-second grid with site-specific probabilistic hazard outputs. In practical terms, this means the model divides the country into cells of roughly one kilometre at the equator, each carrying information about the building stock within it, and then overlays probabilistic seismic hazard curves that describe the intensity of ground shaking expected at each location over given return periods. Exposure modelling at this scale requires careful taxonomy: buildings must be classified by material, structural system, height and age, because a reinforced concrete frame with soft storeys responds to shaking very differently from a load-bearing masonry house. The researchers map building taxonomies to fragility functions, which are probabilistic descriptions of how likely a structure class is to reach defined damage states under given shaking intensity.

A key technical feature of the methodology is its logic tree approach to fragility assignment. Rather than committing to a single vulnerability model for each building class, the framework draws on both regional fragility functions developed specifically for Turkish building stocks and global models developed for broader seismic risk analyses, weighting them in a logic tree that propagates model uncertainty into the final results. This is a deliberate response to one of the most stubborn problems in catastrophe modelling: different fragility curves derived for nominally similar buildings can produce materially different loss estimates, and pretending that a single curve is the truth understates the true uncertainty. The logic tree generates a suite of risk metrics for every province, including economic losses, casualties and the number of displaced households, each expressed as a distribution across the model branches rather than a single deterministic number.

The socioeconomic side of the framework departs from the common practice of appending a social vulnerability index as a supplementary layer. The researchers constructed a socioeconomic vulnerability index from thirty indicators spanning economic, health and educational dimensions, drawing on national statistical data. Indicators such as income, employment, access to health services and educational attainment are known from decades of disaster research to influence both the immediate toll of an earthquake and the speed of recovery, but combining them raises a methodological trap: how much weight should each indicator receive? Subjective expert weighting introduces bias, while naive equal weighting ignores the fact that many socioeconomic indicators measure overlapping aspects of the same underlying condition. To resolve this, the team employed an objective weighting technique designed to manage interdependencies between indicators, using the statistical structure of the data itself to determine weights and eliminate potential biases in the aggregation.

Perhaps the most consequential design decision is how the two halves of the analysis are combined. Instead of treating socioeconomic vulnerability as a separate variable to be added to the physical risk score, the methodology integrates it as a multiplier that scales the structural risk outputs. The logic is multiplicative rather than additive: two provinces with identical building fragility and hazard exposure will not experience equivalent disasters if one has substantially weaker coping capacity, and the multiplier structure captures that interaction directly. A province with moderate physical risk but severe socioeconomic vulnerability can therefore rise in the integrated ranking above a province with high physical risk but strong coping capacity, a reordering that conventional engineering-only rankings cannot produce. The final output is an integrated seismic risk index for each province, intended to identify hotspot areas where long-term mitigation strategies are most urgently needed.

The results demonstrate that the integrated ranking differs in meaningful ways from the traditional physical risk ranking. Provinces whose populations face compounding economic, health and educational disadvantages move up the priority list when their diminished coping capacity is factored in, while some areas that score highly on structural risk alone slip when social resilience is accounted for. For decision-makers charged with allocating scarce retrofitting funds, this difference is not academic. Türkiye has enacted legislation on the transformation of areas under disaster risk, and national reconstruction assessments after the 2023 earthquakes have grappled with the scale of rebuilding required. A screening tool that surfaces where combined physical and social risk is concentrated gives policymakers a defensible, data-driven basis for deciding where detailed, finer-resolution assessment and investment appraisal should be directed first, before expensive building-by-building surveys are commissioned.

The framework also situates itself within a broader methodological lineage. Integrated risk assessment, in which social vulnerability is combined with physical loss modelling, has been developed for other countries and regions, including applications within the OpenQuake computational platform and national studies for countries such as Portugal. The Turkish application extends this line of work to one of the most seismically active countries on Earth, where the North Anatolian Fault and the convergence of the Anatolian plate with surrounding plates have produced a long historical record of destructive earthquakes. The exposure and hazard inputs draw on the European seismic risk and hazard modelling infrastructure, including regional exposure models and site response characterisations, adapted to the national scale of a country with tens of millions of residential buildings.

Technically, the framework’s dependence on 30-arc-second gridded exposure data and probabilistic hazard outputs reflects a deliberate trade-off between coverage and resolution. National-scale prioritisation cannot wait for building-by-building surveys of the entire stock, which would take years and rapidly go out of date. Grid-based exposure modelling, in which the spatial distribution and characteristics of buildings are inferred from statistical and census data disaggregated to cells, allows the entire country to be assessed consistently within a single computational run. The cost is that local effects, such as soil amplification at specific sites or the particular deficiencies of an individual structure, are only represented in aggregated form. The authors are explicit that the framework is a screening and prioritisation instrument, not a substitute for detailed structural evaluation, and that its proper role is to direct finer-grained studies toward the places where integrated risk is highest.

The 2023 Kahramanmaraş earthquake sequence provided a stark real-world validation of the framework’s premise. Post-disaster assessments by international institutions documented catastrophic damage and loss across the affected region, and empirical studies of the damaged building stock have since generated new fragility evidence for Turkish reinforced concrete and masonry construction. The researchers’ insistence that coping capacity is inseparable from structural vulnerability echoes what reconnaissance teams observed on the ground: recovery trajectories, shelter needs and long-term displacement were shaped as much by the socioeconomic condition of affected communities as by the shaking itself. By encoding that insight into a quantitative, reproducible methodology, the study offers a template that other earthquake-prone countries can adapt.

The larger significance of the work lies in what it changes about the conversation around seismic mitigation. For decades, retrofitting priorities have been argued largely in engineering terms, measured in expected monetary loss, collapse probabilities and casualties. Those metrics matter, but they capture only part of the disaster equation. A national-scale index that makes social vulnerability an explicit multiplier of physical risk reframes the question from ‘where are the most dangerous buildings?’ to ‘where will earthquakes cause the greatest and least recoverable harm?’, and the answer to the second question is frequently a different map. For Türkiye, a country where earthquake preparedness is a matter of intense public debate and where large parts of the building stock predate modern seismic codes, that reframing could influence how limited public investment is targeted in the years ahead, and for the international risk-modelling community, it provides a concrete demonstration that integrated, data-driven prioritisation frameworks are now computationally feasible at full national scale.

Subject of Research: Integrated seismic risk estimation and retrofitting prioritisation for Türkiye combining physical loss modelling with socioeconomic vulnerability

Article Title: An integrated seismic risk estimation framework for Türkiye

Article References: Aydin, M. F., Mucedero, G., & Monteiro, R. (2026). An integrated seismic risk estimation framework for Türkiye. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02718-z

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02718-z

Keywords: seismic risk, Türkiye, earthquake engineering, socioeconomic vulnerability, fragility functions, probabilistic hazard, building retrofitting, risk prioritisation, loss estimation, Kahramanmaraş earthquakes, exposure modelling, integrated risk index