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Publication
The use of artificial neural networks to estimate seismic damage in traditional masonry buildings
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Advisor(s)
Abstract(s)
The present paper aims to discuss alternative strategies to estimate earthquake damage inflicted to traditional masonry buildings through a comparative analysis of the results obtained resorting to two different approaches: a seismic vulnerability index scoring method and physical damage estimation, widely used in the past in numerous large-scale earthquake risk assessment studies, and an innovative approach based on the use of Artificial Neural Networks. The post-earthquake damage data collected in the aftermath of the magnitude VII earthquake that struck the Azores archipelago (in Portugal) on July 9, 1998, was used to generate real damage data for a set of traditional masonry buildings located in the island of Faial. This data was then compared to the analytical results obtained through the referred approaches for different macroseismic intensities, IEMS-98. Finally, the fitting of the mean damage grade values estimated by the scoring method and calculated through the artificial neural network are compared and critically discussed.
Description
Keywords
Seismic vulnerability Damage estimation Masonry buildings Index-based approach Artificial neural networks
Citation
T. M. Ferreira, J. M. C. Estêvão, R. Maio, R. Vicente, The use of artificial neural networks to estimate seismic damage in traditional masonry buildings, In: Proceedings of 16th European Conference on Earthquake Engineering (16ECEE), Thessaloniki, Greece, 2018, pp. 1-10, paper 10827.