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Frontiers of Structural and Civil Engineering

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:  
http://journal.hep.com.cn/fsce/EN/10.1007/s11709-020-0623-6
http://journal.hep.com.cn/fsce/EN/Y2020/V14/I3/609
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Tiago Miguel FERREIRA
João ESTÊVÃO
Rui MAIO
Romeu VICENTE
geographical location observed macroseismic intensity, IEMS-98
V VI VII VIII
Angústias (7/90) 7
Castelo Branco (5/90) 4 1
Cedros (6/90) 4 2
Conceição (12/90) 7 5
Feteira (5/90) 5
Flamengos (5/90) 5
Matriz (16/90) 13 3 -
Pedro Miguel (5/90) 5
Praia de Almoxarife (16/90) 5 1 10
Ribeirinha (8/90) 8
Salão (5/90) 5
number and percentage of buildings per intensity 25 (27.8%) 25 (27.8%) 25 (27.8%) 15 (16.6%)
Tab.1  Location and distribution of the assessed buildings considering the Macroseismic Intensity registered in situ
geographical location observed damage grades, Di
no damage (D0) D1 D2 D3 D4 D5
Angústias (7/90) 7
Castelo Branco (5/90) 1 3 1
Cedros (6/90) 3 3
Conceição (12/90) 1 4 5 2
Feteira (5/90) 2 1 1 1
Flamengos (5/90) 3 0 2
Matriz (16/90) 1 13 2
Pedro Miguel (5/90) 2 3
Praia de Almoxarife (16/90) 2 3 4 7
Ribeirinha (8/90) 1 2 1 4
Salão (5/90) 1 1 1 2
number and percentage of buildings 4 (4.4%) 32 (35.6%) 22 (24.4%) 16 (17.8%) 6 (6.7%) 10 (11.1%)
Tab.2  Location and distribution of the assessed buildings considering their observed damage grades
parameters vulnerability class cvi weight,
wi
relative weight
A B C D
Group 1. structural building system P1 Type of resisting system 0 5 20 50 2.50 50/100
P2 Quality of resisting system 0 5 20 50 2.50
P3 Conventional strength 0 5 20 50 1.00
P4 Maximum distance between walls 0 5 20 50 0.50
P5 Number of floors 0 5 20 50 0.50
P6 Location and soil conditions 0 5 20 50 0.50
Group 2. irregularities and interactions P7 Aggregate position and interaction 0 5 20 50 1.50 20/100
P8 Plan configuration 0 5 20 50 0.50
P9 Height regularity 0 5 20 50 0.50
P10 Wall facade openings and alignments 0 5 20 50 0.50
Group 3. floor slabs and roofs P11 Horizontal diaphragms 0 5 20 50 0.75 18/100
P12 Roofing system 0 5 20 50 2.00
Group 4. conservation status and other elements P13 Fragilities and conservation status 0 5 20 50 1.00 12/100
P14 Non-structural elements 0 5 20 50 0.75
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) IEMS - 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) IEMS-98 = V; (b) IEMS-98 = VI; (c) IEMS-98 = VII; (d) IEMS-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) IEMS-98 = V; (b) IEMS-98 = VI; (c) IEMS-98 = VII; and (d) IEMS-98 = VIII.
Fig.12  Vulnerability curves for different vulnerability index values, Iv.
1 T M Ferreira, R Maio, A A Costa, R Vicente. Seismic vulnerability assessment of stone masonry façade walls: Calibration using fragility-based results and observed damage. Soil Dynamics and Earthquake Engineering, 2017, 103: 21–37
https://doi.org/10.1016/j.soildyn.2017.09.006
2 A J Kappos. An overview of the development of the hybrid method for seismic vulnerability assessment of buildings. Structure and Infrastructure Engineering, 2016, 12(12): 1573–1584
https://doi.org/10.1080/15732479.2016.1151448
3 T M Ferreira, N Mendes, R Silva. Multiscale seismic vulnerability assessment and retrofit of existing masonry buildings. Buildings, 2019, 9(4): 91
https://doi.org/10.3390/buildings9040091
4 S Rezaei, A J Choobbasti. Liquefaction assessment using microtremor measurement, conventional method and artificial neural network (Case study: Babol, Iran). Frontiers of Structural and Civil Engineering, 2014, 8(3): 292–307
https://doi.org/10.1007/s11709-014-0256-8
5 P Zakian. An efficient stochastic dynamic analysis of soil media using radial basis function artificial neural network. Frontiers of Structural and Civil Engineering, 2017, 11(4): 470–479
https://doi.org/10.1007/s11709-017-0440-8
6 G Abdollahzadeh, S M Shabanian. Experimental and numerical analysis of beam to column joints in steel structures. Frontiers of Structural and Civil Engineering, 2018, 12(4): 642–661
https://doi.org/10.1007/s11709-017-0457-z
7 J Reyes, A Morales-Esteban, F Martínez-Álvarez. Neural networks to predict earthquakes in Chile. Applied Soft Computing, 2013, 13(2): 1314–1328
https://doi.org/10.1016/j.asoc.2012.10.014
8 C S Huang, S L Hung, C M Wen, T T Tu. A neural network approach for structural identification and diagnosis of a building from seismic response data. Earthquake Engineering & Structural Dynamics, 2003, 32(2): 187–206
https://doi.org/10.1002/eqe.219
9 G L Molas, F Yamazaki. Neural networks for quick earthquake damage estimation. Earthquake Engineering & Structural Dynamics, 1995, 24(4): 505–516
https://doi.org/10.1002/eqe.4290240404
10 K Bani-Hani, J Ghaboussi, S P Schneider. Experimental study of identification and control of structures using neural network. Part 2: Control. Earthquake Engineering & Structural Dynamics, 1999, 28(9): 1019–1039
https://doi.org/10.1002/(SICI)1096-9845(199909)28:9<1019::AID-EQE852>3.0.CO;2-X
11 E Ferrario, N Pedroni, E Zio, F Lopez-Caballero. Bootstrapped Artificial Neural Networks for the seismic analysis of structural systems. Structural Safety, 2017, 67: 70–84
https://doi.org/10.1016/j.strusafe.2017.03.003
12 K Morfidis, K Kostinakis. Approaches to the rapid seismic damage prediction of R/C buildings using artificial neural networks. Engineering Structures, 2018, 165: 120–141
https://doi.org/10.1016/j.engstruct.2018.03.028
13 K Morfidis, K Kostinakis. Seismic parameters’ combinations for the optimum prediction of the damage state of R/C buildings using neural networks. Advances in Engineering Software, 2017, 106: 1–16
https://doi.org/10.1016/j.advengsoft.2017.01.001
14 S M Vazirizade, S Nozhati, M A Zadeh. Seismic reliability assessment of structures using artificial neural network. Journal of Building Engineering, 2017, 11: 230–235
https://doi.org/10.1016/j.jobe.2017.04.001
15 C Anitescu, E Atroshchenko, N Alajlan, T Rabczuk. Artificial neural network methods for the solution of second order boundary value problems. Computers, Materials & Continua, 2019, 59(1): 345–359
https://doi.org/10.32604/cmc.2019.06641
16 H Guo, X Zhuang, T. Rabczuk A deep collocation method for the bending analysis of Kirchhoff plate. Computers, Materials & Continua 2019; 59(2): 433–456
https://doi.org/10.32604/cmc.2019.06660
17 Z Wang, N Pedroni, I Zentner, E Zio. Seismic fragility analysis with artificial neural networks: Application to nuclear power plant equipment. Engineering Structures, 2018, 162: 213–225
https://doi.org/10.1016/j.engstruct.2018.02.024
18 J M C Estêvão. Feasibility of using neural networks to obtain simplified capacity curves for seismic assessment. Buildings, 2018, 8(11): 151–164
https://doi.org/10.3390/buildings8110151
19 H O Wood, F Neumann. Modified Mercalli intensity scale of 1931. Bulletin of the Seismological Society of America, 1931, 21(4): 277–283
20 T M Ferreira, R Maio, R Vicente. Seismic vulnerability assessment of the old city centre of Horta, Azores: Calibration and application of a seismic vulnerability index method. Bulletin of Earthquake Engineering, 2017, 15(7): 2879–2899
https://doi.org/10.1007/s10518-016-0071-9
21 C S Oliveira, A Costa, J C Nunes. The 1998 Açores Earthquake: A Decade Later. São Miguel: Azores Regional Government, 2008 (in Portuguese)
22 G Zonno, C S Oliveira, M A Ferreira, G Musacchio, F Meroni, F Mota-de-Sá, F Neves. Assessing seismic damage through stochastic simulation of ground shaking: The case of the 1998 Faial Earthquake (Azores Islands). Surveys in Geophysics, 2010, 31(3): 361–381
https://doi.org/10.1007/s10712-009-9091-1
23 A Bernardini, S Giovinazzi, S Lagomarsino, S Parodi. Vulnerability and damage prediction at the territorial scale according to a macroseismic methodology consistent with the EMS-98 scale. In: Proceedings of the 12th Conference of the Italian National Association of Earthquake Engineering. Pisa: ANIDIS, 2007
24 G Grünthal. European Macroseismic Scale 1998 (EMS-98). Luxembourg: European Center for Geodynamics and Seismology, 1998
25 R Vicente, S Parodi, S Lagomarsino, H Varum, J A R M Silva. Seismic vulnerability and risk assessment: Case study of the historic city centre of Coimbra, Portugal. Bulletin of Earthquake Engineering, 2011, 9(4): 1067–1096
https://doi.org/10.1007/s10518-010-9233-3
26 S Lagomarsino, S Giovinazzi. Macroseismic and mechanical models for the vulnerability and damage assessment of current buildings. Bulletin of Earthquake Engineering, 2006, 4(4): 415–443
https://doi.org/10.1007/s10518-006-9024-z
27 F Bramerini, G Di Pasquale, A Orsini, A Pugliese, R Romeo, F Sabetta. Seismic Risk of the Italian Territory. Proposal for a Methodology and Preliminary Results. Technical Report N. SSN/RT/95/01. Roma, 1995 (in Italian)
28 P J Drew, J R T Monson. Artificial neural networks. Surgery, 2000, 127(1): 3–11
https://doi.org/10.1067/msy.2000.102173
29 P J Werbos. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. Cambridge: Harvard University, 1974
30 J M C Estêvão. Computer Model for Buildings Seismic Risk Assessment. Lisbon: Instituto Superior Técnico, UTL, 1998 (in Portuguese)
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