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

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 Earth-quake 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.

Automated summary

This paper demonstrates how structural resilience can be assessed using artificial neural networks and evolutionary computation, and presents a case study showing that increasing the structural design cost can significantly reduce earthquake damage and mortality risk.