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

doi:10.1007/s11709-020-0623-6

Front. Struct. Civ. Eng.

2020, Vol. 14

Issue (3) : 609-622 https://doi.org/10.1007/s11709-020-0623-6

RESEARCH ARTICLE

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

Tiago Miguel FERREIRA¹(

), João ESTÊVÃO², Rui MAIO³, Romeu VICENTE³

¹. ISISE, Institute of Science and Innovation for Bio-Sustainability (IB-S), Department of Civil Engineering, University of Minho, Guimarães 4800-058, Portugal
². Department of Civil Engineering, University of Algarve, Faro 8005-139, Portugal
³. RISCO, Department of Civil Engineering, University of Aveiro, Aveiro 3810-193, Portugal

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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.

Keywords Artificial Neural Networks seismic vulnerability masonry buildings damage estimation vulnerability curves

Corresponding Author(s): Tiago Miguel FERREIRA

Just Accepted Date: 19 April 2020 Online First Date: 25 May 2020 Issue Date: 13 July 2020

Cite this article:

Tiago Miguel FERREIRA,João ESTÊVÃO,Rui MAIO, et al. The use of Artificial Neural Networks to estimate seismic damage and derive vulnerability functions for traditional masonry[J]. Front. Struct. Civ. Eng., 2020, 14(3): 609-622.

URL:

https://academic.hep.com.cn/fsce/EN/10.1007/s11709-020-0623-6
https://academic.hep.com.cn/fsce/EN/Y2020/V14/I3/609

Tab.1 Location and distribution of the assessed buildings considering the Macroseismic Intensity registered in situ

Tab.2 Location and distribution of the assessed buildings considering their observed damage grades

Tab.3 Vulnerability index parameters, classes and weights, adapted from [¹³].

Fig.1 Observed versus estimated discrete damage grade distributions, using the vulnerability index approach.

Fig.2 Confront between observed mean damage grades and the vulnerability functions for intensities: (a) I_EMS_-₉₈ = V; (b) VI; (c) VII; (d) VIII.

Fig.3 Relative deviation between observed and estimated damage, using the vulnerability index approach: (a) for each building assessed; and (b) histogram with best-fit Gaussian curve.

Fig.4 ANN inspired from human synapse.

Fig.5 Single artificial neuron representation.

Fig.6 Adopted ANN architecture.

Fig.7 Observed versus estimated mean damage grade distributions, resorting to the ANN.

Fig.8 Observed mean damage grades versus ANN-derived vulnerability functions: (a) I_EMS-98 = V; (b) I_EMS-98 = VI; (c) I_EMS-98 = VII; (d) I_EMS-98 = VIII.

Fig.9 Relative deviation between observed and estimated damage, resorting to the ANN: (a) for each building assessed; and (b) histogram with best-fit Gaussian curve.

Fig.10 Analytical vulnerability function for different macroseismic intensities.

Fig.11 Comparison between observed and estimated damage resorting to Eqs. (11) and (2) for macroseismic intensities: (a) I_EMS-98 = V; (b) I_EMS-98 = VI; (c) I_EMS-98 = VII; and (d) I_EMS-98 = VIII.

Fig.12 Vulnerability curves for different vulnerability index values, I_v.

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