Application of fuzzy theory on earthquake damage rate estimation of buildings

Abstract

Variations between earthquakes result in many factors that influence post-earthquake building damage (e.g., ground motion parameters, building structure, site information, and quality of construction). Consequently, it is necessary to develop an appropriate building damage-rate estimation model. The building damage survey data were recorded and constructed into files by the Architecture and Building Research Institute (ABRI), Taiwan for the 1999 Chi-Chi earthquake in the Nantou region as a basis for developing a building damage rate estimation model by applying fuzzy theory to express the fragility curves of buildings as a membership function. Empirical verification was performed using post-earthquake building damage data in the Taichung city that suffered relatively severe damage. Results indicate that fuzzy theory can be applied to predict building damage rates and that the estimated results are similar to actual disaster figures. Prediction of disaster damage using building damage rates can provide a reference for immediate disaster response during earthquakes and for regular disaster prevention and rescue planning.

Keywords

fuzzy theory / membership function / fragility curve / earthquake damage rate

References

[1] ErdikM, ŞeseşetyanK, DemircioĝluM B, HancilarU, ZülfikarC. Rapid earthquake loss assessment after damaging earthquakes [J]. Soil Dynamics and Earthquake Engineering, 2011, 31(2): 247-266

[2] WenZ P, HuY X, ChauK T. Site effect on vulnerability of high-rise shear wall buildings under near and far field earthquakes [J]. Soil Dynamics and Earthquake Engineering, 2002, 22(9/10/11/12): 1175-1182

[3] ChenD-s, DongW-m, HareshC S. Earthquake recurrence relationships from fuzzy earthquake magnitudes [J]. Soil Dynamics and Earthquake Engineering, 1988, 7(3): 136-142

[4] ZadehL A. Fuzzy sets [J]. Information and Control, 1965, 8: 338-353

[5] WangQ, FuJ-h, WangZ-y, TongJie. A seismic intensity estimation method based on the fuzzy-norm theory [J]. Soil Dynamics and Earthquake Engineering, 2012, 40: 109-117

[6] JorjiashviliN, YokoiT, JavakhishviliZ. Assessment of uncertainties related to seismic hazard using fuzzy analysis [J]. Natural Hazards, 2012, 60(2): 501-515

[7] LiuY-b, QiaoZ, WangG-yuan. Fuzzy random reliability of structures based on fuzzy random variables [J]. Fuzzy Sets and Systems, 1997, 86(3): 345-355

[8] WangX-y, NieG-z, WangSong. Evaluation criteria of landslide hazards induced by Wenchuan earthquake using fuzzy mathematical method [J]. Rock and Soil Mechanics, 2011, 32(2): 403-410

[9] RahmanM S, WangJ. Fuzzy neural network models for liquefaction prediction [J]. Soil Dynamics and Earthquake Engineering, 2002, 22(8): 685-694

[10] RohmerJ, BaudritC. The use of the possibility theory to investigate the epistemic uncertainties within scenario-based earthquake risk assessments [J]. Natural Hazards, 2011, 56(3): 613-632

[11] KimJ-h, LeeS-h, WangH-mei. Similarity measure application to fault detection of flight system [J]. Journal of Central South University of Technology, 2009, 16(5): 789-793

[12] ColangeloF. A simple model to include fuzziness in the seismic fragility curve and relevant effect compared with randomness [J]. Earthquake Engineering and Structural Dynamics, 2012, 41(5): 969-986

[13] DongW M, ChiangW L, ShahH C. Fuzzy information processing in seismic hazard analysis and decision making [J]. Soil Dynamics and Earthquake Engineering, 1987, 6(4): 220-226

[14] HashemiM, AlesheikhA A. A GIS-based earthquake damage assessment and settlement methodology [J]. Soil Dynamics and Earthquake Engineering, 2011, 31(11): 1607-1617

[15] ChiouJ-s, ChiangC-h, YangH-h, HsuS-yi. Developing fragility curves for a pile-supported wharf [J]. Soil Dynamics and Earthquake Engineering, 2011, 31(5/6): 830-840

[16] RotaM, PennaA, StrobbiaC L. Processing Italian damage data to derive typological fragility curves [J]. Soil Dynamics and Earthquake Engineering, 2008, 28(10/11): 933-947

[17] MiuraH, MidorikawaS, FujimotoK, PachecoB M, YamanakaH. Earthquake damage estimation in Metro Manila, Philippines based on seismic performance of buildings evaluated by local experts’ judgments [J]. Soil Dynamics and Earthquake Engineering, 2008, 28(10/11): 764-777

[18] Dumova-JovanoskaE. Fragility curves for reinforced concrete structures in Skopje (Macedonia) region [J]. Soil Dynamics and Earthquake Engineering, 2000, 19(6): 455-466

[19] AlvanitopoulosP F, AndreadisI, ElenasA. Neuro-fuzzy techniques for the classification of earthquake damages in buildings [J]. Measurement, 2010, 43(6): 797-809

[20] HuangC-f, YeeLeung. Estimating the relationship between isoseismal area and earthquake magnitude by a hybrid fuzzy-neural-network method [J]. Fuzzy Sets and Systems, 1999, 107(2): 131-146

[21] WangW, ZhangY. Group decision making in safety planning for earthquake disaster area reconstruction [C]. 2011 2nd International Conference on Mechanic Automation and Control Engineering MACE 2011-Proceedings. Hohhot, China, 2011, 5988545: 6552-6555

[22] FischerT, AlvarezM, De La LleraJ C, RiddellR. An integrated model for earthquake risk assessment of buildings [J]. Engineering Structures, 2002, 24(7): 979-998

[23] ZhaoY, WangY, LiuW, NiuY, HuangM, ZhaoY. The earthquake disaster prediction and evaluation method of the highway system based on fuzzy comprehensive evaluation [J]. World Information on Earthquake Engineering, 2010, 26(3): 139-144

[24] KarimK R, YamazakiF. Effect of isolation on fragility curves of highway bridges based on simplified approach [J]. Soil Dynamics and Earthquake Engineering, 2007, 27(5): 414-426

[25] HwangaH, LinaC K, YehbY T, ChengbS N, ChencK C. Attenuation relations of Arias intensity based on the Chi-Chi Taiwan earthquake data [J]. Soil Dynamics and Earthquake Engineering, 2004, 24(7): 509-517

[26] YehC H, LohC H, TsaiK C. Overview of Taiwan earthquake loss estimation system [J]. Natural Hazards, 2006, 37(1–2): 23-37

[27] LeeS-h, LeeS-m, SohnG-y, KimJ-yung. Fuzzy entropy design for non convex fuzzy set and application to mutual information [J]. Journal of Central South University of Technology, 2011, 18(1): 184-189

[28] LeeS-h, ParkW-j, JungD-yean. Similarity measure design and similarity computation for discrete fuzzy data [J]. Journal of Central South University of Technology, 2011, 18(5): 1602-1608

[29] MoulikP. An earthquake warning system using fuzzy logic and geospatial analysis [J]. Geotechnical Special Publication, 2008, 178: 1187-1194

[30] WeiP, ChenH, ZhangY. Study on the application of semi-active variable stiffness fuzzy control system to a residential building [J]. Advanced Materials Research, 2012, 446–449: 3197-3201