Work

Explainable ensemble learning for structural damage prediction under seismic events

Year: 2025

Type: conference-paper

Abstract: This paper presents an explainable ensemble learning framework using Bootstrap Aggregating to predict structural damage in masonry buildings during seismic events.It estimates the peak ground acceleration (PGA) leading to the damage control limit state (significant damage) based on structural parameters.The model achieves high accuracy (R 2 =0.9536,MAE=0.0057) and interpretability through SHAP, aligning with structural engineering principles.Compared to finite element analyses, it offers faster computations (milliseconds) and scalability, enabling rapid intervention planning after earthquakes.Developed under the MEDEA project (EU Grant n. 10101236), it supports disaster response and enhances seismic resilience.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-198

Authors: Michele Baldassini, Pierfrancesco Foglia, Beatrice Lazzerini, Francesco Pistolesi, Cosimo Antonio Prete

Institution: University of Pisa

Language: English

Topic: Seismic Performance and Analysis

Subfield: Civil and Structural Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Sustainable cities and communities

Open Access status: bronze

Funders: Dipartimenti di Eccellenza, European Commission