AI Strips Earthquake Risk Prediction Down to a Handful of Vital Clues

Every earthquake-prone city carries a hidden ledger of vulnerability: thousands of reinforced concrete buildings whose behavior under shaking is largely unknown. Assessing them one by one with full structural analysis is slow, expensive, and often impossible before disaster strikes. A new study published in the Bulletin of Earthquake Engineering by Mertcan Yilmaz and Tamer Dirikgil of Erciyes University in Türkiye tackles this problem with a question that sounds deceptively simple: how few building parameters do you actually need to predict how risky a building is before an earthquake happens? Their answer, delivered through a carefully constructed machine learning framework, is remarkably few — and the implications for urban-scale seismic screening could be profound.

The researchers set out to evaluate how feature importance and feature selection methods perform when the goal is rapid, reliable estimation of seismic risk levels for reinforced concrete buildings. Rather than treating machine learning as an opaque oracle, they deliberately interrogated it, asking which input parameters drive predictions and whether a trimmed-down set of inputs could match, or even beat, models fed the complete feature set. This dual focus on accuracy and interpretability places the work squarely within the growing field of explainable artificial intelligence, where the goal is not just prediction but understanding.

To build a trustworthy foundation, the team used a simulation-based dataset representing an existing building stock rather than a purely synthetic or idealized collection of structures. This choice matters. Rapid screening methods have a long history in earthquake engineering, from the FEMA P-154 rapid visual screening handbook in the United States to the P25 screening method developed in Türkiye and the Japanese Building Disaster Prevention Association’s evaluation standard. These approaches typically rely on a handful of visually observable parameters. Machine learning promises to go further by capturing subtle, nonlinear interactions among many parameters, but only if the underlying data reflect real buildings with realistic variability.

The study employed three machine learning models to classify seismic risk, allowing the authors to compare how different algorithms weigh and use the same information. Across their analyses, a consistent pattern emerged: combining feature importance measures with explicit feature selection improved prediction performance compared with using either approach alone. Feature importance methods, such as permutation importance, quantify how much each input contributes to a model’s predictions by measuring how performance degrades when that input is shuffled or removed. Feature selection methods, including correlation-based filtering rooted in classical statistical measures such as Pearson’s correlation coefficient, systematically prune redundant or uninformative variables before training.

The standout result came from LightGBM, a gradient boosting framework known for its efficiency on tabular data, when paired with normalized feature importance and correlation-based feature selection. This combination achieved a macro-F1 score of approximately 0.91 — a strong result for a multi-class classification task — while using fewer input parameters than models trained on the full feature set. The macro-F1 metric is particularly meaningful in seismic risk classification because it averages performance across all risk classes, preventing a model from looking good simply by mastering the most common category while neglecting rare but critical high-risk cases.

That a leaner model outperformed the full-feature version is not a paradox but a well-documented phenomenon in machine learning. Irrelevant and redundant features can act as noise, encouraging models to fit spurious patterns that do not generalize. By identifying and retaining only the most influential parameters, the selection process acts as a regularizer, sharpening the model’s attention on the structural characteristics that genuinely govern seismic behavior. For low-rise reinforced concrete buildings, such characteristics typically relate to geometry, structural system, material properties, and code-era design provisions — the same factors that decades of empirical screening methods have converged upon, but now quantified and weighted by data rather than expert judgment alone.

The interpretability dimension is central to the study’s practical value. Engineers and municipal officials cannot act on a black-box score; they need to know why a building is flagged as high risk and which interventions might matter. Frameworks like SHAP, introduced by Lundberg and Lee in 2017, and LIME, proposed by Ribeiro and colleagues in 2016, have made it standard practice to attach explanations to model predictions. By combining normalized feature importance with explicit selection, Yilmaz and Dirikgil provide a transparent chain of reasoning: here are the parameters that matter most, here is the evidence that they suffice, and here is the resulting risk classification. This aligns with a broader movement in structural engineering toward explainable machine learning, exemplified by recent work applying explainable frameworks to XGBoost-based seismic assessment.

The timing of this research is not incidental. The February 2023 Kahramanmaraş earthquake sequence in Türkiye, which included magnitude 7.8 and 7.6 events, caused catastrophic damage to vast numbers of reinforced concrete buildings and prompted intensive post-earthquake damage surveys. Those surveys documented recurring vulnerabilities tied to design era, detailing practices, and construction quality, reinforcing the urgency of pre-earthquake screening. Türkiye’s successive earthquake codes, from the 1975 provisions through the current Turkish Building Earthquake Code of 2018, mean that a building’s design vintage alone carries substantial predictive information. Machine learning models trained on simulation data grounded in these code generations can encode that knowledge and apply it consistently across an entire urban building stock.

What makes the proposed framework genuinely scalable is the economy of its input requirements. Traditional detailed assessment requires structural drawings, material testing, and nonlinear analysis — resources that no city can mobilize for every building. Rapid visual screening reduces the burden but introduces subjectivity and coarse granularity. A model that achieves roughly 0.91 macro-F1 with a reduced set of critical parameters offers a middle path: data-driven consistency at a fraction of the data collection cost. The authors frame this as enabling rapid and efficient large-scale building stock assessments, with direct applications to regional seismic risk evaluation and urban-scale vulnerability screening. For municipalities prioritizing retrofit budgets, the ability to rank thousands of buildings quickly and defensibly could redirect scarce resources to the structures that need them most.

The study also contributes methodologically to the machine learning literature on feature selection. Recent comparative analyses have emphasized that different selection methods vary in both performance and stability, and that the choice of importance measure interacts with the choice of algorithm. By systematically testing combinations of three models with multiple importance and selection strategies, the researchers provide a template that other domains facing high-dimensional structural data can adapt. Their finding that the combination of normalized importance and correlation-based selection with LightGBM yields the best results is an actionable recommendation, not merely an observation. The authors note that some supporting data are included in the article, with expanded datasets available from the corresponding author upon reasonable request, supporting reproducibility in a field where simulation pipelines are often proprietary. As cities worldwide confront aging building stocks and tightening retrofit budgets, the message from Kayseri is clear: predicting which buildings will fail in the next earthquake may require far less information than anyone assumed — provided you know exactly which information to ask for.

Subject of Research: Interpretable machine learning for seismic risk prediction of low-rise reinforced concrete buildings

Article Title: Interpretable machine learning and feature selection for seismic risk prediction of low-rise reinforced concrete buildings

Article References: Yilmaz, M., & Dirikgil, T. (2026). Interpretable machine learning and feature selection for seismic risk prediction of low-rise reinforced concrete buildings. Bulletin of Earthquake Engineering. https://doi.org/10.1007/s10518-026-02667-7

Image Credits: AI Generated

DOI: 10.1007/s10518-026-02667-7

Keywords: seismic risk prediction, machine learning, reinforced concrete buildings, explainable AI, feature selection, feature importance, LightGBM, macro-F1 score, rapid screening, earthquake engineering, urban vulnerability, Turkish building stock