eprintid: 10205869
rev_number: 7
eprint_status: archive
userid: 699
dir: disk0/10/20/58/69
datestamp: 2025-03-11 10:11:49
lastmod: 2025-03-11 10:11:49
status_changed: 2025-03-11 10:11:49
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Ding, Jia-Yi
creators_name: Feng, De-Cheng
creators_name: Galasso, Carmine
title: Seismic fragility assessment of regional building portfolios using machine learning and Poisson binomial distribution
ispublished: pub
divisions: UCL
divisions: B04
divisions: F44
keywords: Building portfolios, Fragility analysis, Geology, Geosciences, Multidisciplinary, Meteorology & Atmospheric Sciences, MODEL, Physical Sciences, Poisson binomial distribution, Probabilistic model, RC BUILDINGS, Science & Technology, Simplified multiple degree of freedom, SIMULATION, Water Resources
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
abstract: Ongoing urbanization trends and the growing concentration of buildings in earthquake-prone urban areas underscore the importance of probabilistic seismic performance assessments of building portfolios for earthquake risk management and reduction. While various machine learning (ML) techniques have been used in seismic fragility analysis of structures, their deterministic predictions often fall short in propagating input uncertainties (e.g., building-to-building variability within a given building type). This study introduces a probabilistic ML method for predicting the damage probability of individual buildings within a region. It also presents a probabilistic approach derived from the Poisson binomial distribution model that considers independent building functionalities to provide regional fragility models for building portfolios. A virtual testbed of simplified multi-degree of freedom (MDoF) models for each building in the portfolio is generated by randomly sampling uncertain structural design parameters. The results demonstrate that the probabilistic ML model can directly predict the damage probability parameters of each building, eliminating the need for deterministic ML methods to train models for different buildings repeatedly. The proposed probabilistic model derived by the Poisson binomial distribution effectively evaluates the seismic fragility of building portfolios, considering both structural and nonstructural damage.
date: 2025-01
date_type: published
publisher: Elsevier
official_url: https://doi.org/10.1016/j.ijdrr.2024.105044
full_text_type: other
language: eng
verified: verified_manual
elements_id: 2346818
doi: 10.1016/j.ijdrr.2024.105044
lyricists_name: Galasso, Carmine
lyricists_id: CGALA33
actors_name: Galasso, Carmine
actors_id: CGALA33
actors_role: owner
funding_acknowledgements: 2022YFC3803000 [National Key Research and Development Program of China]; 52311540017 [National Natural Science Foundation of China]; 52361135806 [National Natural Science Foundation of China]
full_text_status: restricted
publication: International Journal of Disaster Risk Reduction
volume: 116
article_number: 105044
pages: 17
issn: 2212-4209
citation:        Ding, Jia-Yi;    Feng, De-Cheng;    Galasso, Carmine;      (2025)    Seismic fragility assessment of regional building portfolios using machine learning and Poisson binomial distribution.                   International Journal of Disaster Risk Reduction , 116     , Article 105044.  10.1016/j.ijdrr.2024.105044 <https://doi.org/10.1016/j.ijdrr.2024.105044>.      
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10205869/1/Revised%20Manuscript.pdf