| Title: |
Explainable Ensemble Learning for Structural Damage Prediction under Seismic Events |
| Authors: |
Baldassini, Michele; Foglia, Pierfrancesco; Lazzerini, Beatrice; Pistolesi, Francesco; Prete, Cosimo Antonio |
| Contributors: |
Collectif; Baldassini, Michele; Foglia, Pierfrancesco; Lazzerini, Beatrice; Pistolesi, Francesco; Prete, Cosimo Antonio |
| Publisher Information: |
i6doc.com; BEL; Louvain-la-Neuve |
| Publication Year: |
2025 |
| Collection: |
ARPI - Archivio della Ricerca dell'Università di Pisa |
| Subject Terms: |
explainable AI; ensemble learning; structural damage; masonry buildings; seismic events |
| Description: |
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 (R2=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 MEDEAproject(EUGrantn. 10101236), it supports disaster response and enhances seismic resilience. |
| Document Type: |
conference object |
| File Description: |
ELETTRONICO |
| Language: |
English |
| Relation: |
info:eu-repo/semantics/altIdentifier/isbn/9782875870933; ispartofbook:ESANN 2025 - Proceedings - 33rd European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning; European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning; volume:1; firstpage:609; lastpage:614; numberofpages:6; https://hdl.handle.net/11568/1338491 |
| DOI: |
10.14428/esann/2025.ES2025-198 |
| Availability: |
https://hdl.handle.net/11568/1338491; https://doi.org/10.14428/esann/2025.ES2025-198; https://www.esann.org/sites/default/files/proceedings/2025/ES2025-198.pdf |
| Accession Number: |
edsbas.9A1A351A |
| Database: |
BASE |