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

Giulia Cerè, Yacine Rezgui, Wanqing Zhao, Ioan Petri

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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.
Original languageEnglish
Pages (from-to)1516-1529
Number of pages14
Early online date6 Oct 2022
Publication statusPublished - 1 Nov 2022


  • Artificial neural networks
  • Building resilience
  • Optimisation
  • Performance-based analysis
  • Seismic hazards

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