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The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry

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Abstract

This paper discusses the adoption of Artificial Intelligence-based techniques to estimate seismic damage, not with the goal of replacing existing approaches, but as a mean to improve the precision of empirical methods. For such, damage data collected in the aftermath of the 1998 Azores earthquake (Portugal) is used to develop a comparative analysis between damage grades obtained resorting to a classic damage formulation and an innovative approach based on Artificial Neural Networks (ANNs). The analysis is carried out on the basis of a vulnerability index computed with a hybrid seismic vulnerability assessment methodology, which is subsequently used as input to both approaches. The results obtained are then compared with real post-earthquake damage observation and critically discussed taking into account the level of adjustment achieved by each approach. Finally, a computer routine that uses the ANN as an approximation function is developed and applied to derive a new vulnerability curve expression. In general terms, the ANN developed in this study allowed to obtain much better approximations than those achieved with the original vulnerability approach, which has revealed to be quite non-conservative. Similarly, the proposed vulnerability curve expression was found to provide a more accurate damage prediction than the traditional analytical expressions.

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Acknowledgements

This work was funded by the Portuguese Foundation for Science and Technology (FCT) through the postdoctoral Grant SFRH/ BPD/122598/2016. The authors acknowledge to the Society of Promotion for Housing and Infrastructures Rehabilitation (SPRHI) and to the Regional Secretariat for Housing and Equipment (SRHE) of Faial for their support and contribution to the development of this work. They would also like to express their gratitude to the anonymous reviewer for their insightful and constructive comments.

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Authors and Affiliations

  1. ISISE, Institute of Science and Innovation for Bio-Sustainability (IB-S), Department of Civil Engineering, University of Minho, Guimarães, 4800-058, Portugal

    Tiago Miguel Ferreira

  2. Department of Civil Engineering, University of Algarve, Faro, 8005-139, Portugal

    João Estêvão

  3. RISCO, Department of Civil Engineering, University of Aveiro, Aveiro, 3810-193, Portugal

    Rui Maio & Romeu Vicente

Authors
  1. Tiago Miguel Ferreira
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  2. João Estêvão
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  3. Rui Maio
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  4. Romeu Vicente
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Correspondence to Tiago Miguel Ferreira.

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Ferreira, T.M., Estêvão, J., Maio, R. et al. The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry. Front. Struct. Civ. Eng. 14, 609–622 (2020). https://doi.org/10.1007/s11709-020-0623-6

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