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A machine learning approach to appraise and enhance the structural resilience of buildings to seismic hazards

Cere, Giulia, Rezgui, Yacine ORCID: https://orcid.org/0000-0002-5711-8400, Zhao, Wanqing ORCID: https://orcid.org/0000-0001-6160-9547 and Petri, Ioan ORCID: https://orcid.org/0000-0002-1625-8247 2022. A machine learning approach to appraise and enhance the structural resilience of buildings to seismic hazards. Structures 45 , pp. 1516-1529. 10.1016/j.istruc.2022.09.113

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Abstract

Earthquakes often affect buildings that did comply with regulations in force at the time of design, prompting the need for new approaches addressing the complex structural dynamics of seismic design. In this paper, we demonstrate how strucural resilience can be appraised to inform optimization pathways by utilising artificial neural networks, augmented with evolutionary computation. This involves efficient multi-layer computational models, to learn complex multi-aspects structural dynamics, through several levels of abstraction. By means of single and multi-objective optimization, an existing structural system is modelled with an accuracy in excess of 98% to simulate its structural loading behaviour, while a performance-based approach is used to determine the optimum parameter settings to maximize its earthquake resilience. We have used the 2008 Wenchuan Earthquake as a case study. Our results demonstrate that an estimated structural design cost increase of 20% can lead to a damage reduction of up to 75%, which drastically reduces the risk of fatality.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Publisher: Elsevier
ISSN: 2352-0124
Funders: Building Research Establishment (BRE) and the Natural Environment Research Council (NERC) under grant NE/N012240/1
Date of First Compliant Deposit: 6 October 2022
Date of Acceptance: 28 September 2022
Last Modified: 05 Nov 2022 03:18
URI: https://orca.cardiff.ac.uk/id/eprint/153101

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