Skip to main content
Log in

Application of fuzzy theory on earthquake damage rate estimation of buildings

  • Published:
Journal of Central South University Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. ERDIK M, ŞESEŞETYAN K, DEMIRCIOĜLU M B, HANCILAR U, ZÜLFIKAR C. Rapid earthquake loss assessment after damaging earthquakes [J]. Soil Dynamics and Earthquake Engineering, 2011, 31(2): 247–266.

    Article  Google Scholar 

  2. WEN Z P, HU Y X, CHAU K 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.

    Article  Google Scholar 

  3. CHEN Da-sheng, DONG Wei-min, HARESH C S. Earthquake recurrence relationships from fuzzy earthquake magnitudes [J]. Soil Dynamics and Earthquake Engineering, 1988, 7(3): 136–142.

    Article  Google Scholar 

  4. ZADEH L A. Fuzzy sets [J]. Information and Control, 1965, 8: 338–353.

    Article  MATH  MathSciNet  Google Scholar 

  5. WANG Qian, FU Ji-hua, WANG Zhong-yu, TONG Jie. A seismic intensity estimation method based on the fuzzy-norm theory [J]. Soil Dynamics and Earthquake Engineering, 2012, 40: 109–117.

    Article  Google Scholar 

  6. JORJIASHVILI N, YOKOI T, JAVAKHISHVILI Z. Assessment of uncertainties related to seismic hazard using fuzzy analysis [J]. Natural Hazards, 2012, 60(2): 501–515.

    Article  Google Scholar 

  7. LIU Yu-bin, QIAO Zhong, WANG Guang-yuan. Fuzzy random reliability of structures based on fuzzy random variables [J]. Fuzzy Sets and Systems, 1997, 86(3): 345–355.

    Article  MATH  MathSciNet  Google Scholar 

  8. WANG Xiu-ying, NIE Gao-zhong, WANG Song. Evaluation criteria of landslide hazards induced by Wenchuan earthquake using fuzzy mathematical method [J]. Rock and Soil Mechanics, 2011, 32(2): 403–410.

    Google Scholar 

  9. RAHMAN M S, WANG J. Fuzzy neural network models for liquefaction prediction [J]. Soil Dynamics and Earthquake Engineering, 2002, 22(8): 685–694.

    Article  Google Scholar 

  10. ROHMER J, BAUDRIT C. 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.

    Article  Google Scholar 

  11. KIM Jae-hyung, LEE Sang-hyuk, WANG Hong-mei. Similarity measure application to fault detection of flight system [J]. Journal of Central South University of Technology, 2009, 16(5): 789–793.

    Article  Google Scholar 

  12. COLANGELO F. 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.

    Article  Google Scholar 

  13. DONG W M, CHIANG W L, SHAH H C. Fuzzy information processing in seismic hazard analysis and decision making [J]. Soil Dynamics and Earthquake Engineering, 1987, 6(4): 220–226.

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. CHIOU Jiunn-shyang, CHIANG Chi-han, YANG Ho-hsiung, HSU Shang-yi. Developing fragility curves for a pile-supported wharf [J]. Soil Dynamics and Earthquake Engineering, 2011, 31(5/6): 830–840.

    Article  Google Scholar 

  16. ROTA M, PENNA A, STROBBIA C L. Processing Italian damage data to derive typological fragility curves [J]. Soil Dynamics and Earthquake Engineering, 2008, 28(10/11): 933–947.

    Article  Google Scholar 

  17. MIURA H, MIDORIKAWA S, FUJIMOTO K, PACHECO B M, YAMANAKA H. 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.

    Article  Google Scholar 

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

    Article  Google Scholar 

  19. ALVANITOPOULOS P F, ANDREADIS I, ELENAS A. Neuro-fuzzy techniques for the classification of earthquake damages in buildings [J]. Measurement, 2010, 43(6): 797–809.

    Article  Google Scholar 

  20. HUANG Chong-fu, YEE Leung. 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.

    Article  MATH  Google Scholar 

  21. WANG W, ZHANG Y. 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.

    Google Scholar 

  22. FISCHER T, ALVAREZ M, DE LA LLERA J C, RIDDELL R. An integrated model for earthquake risk assessment of buildings [J]. Engineering Structures, 2002, 24(7): 979–998.

    Article  Google Scholar 

  23. ZHAO Y, WANG Y, LIU W, NIU Y, HUANG M, ZHAO Y. 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.

    Google Scholar 

  24. KARIM K R, YAMAZAKI F. Effect of isolation on fragility curves of highway bridges based on simplified approach [J]. Soil Dynamics and Earthquake Engineering, 2007, 27(5): 414–426.

    Article  Google Scholar 

  25. HWANGA H, LINA C K, YEHB Y T, CHENGB S N, CHENC K 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.

    Article  Google Scholar 

  26. YEH C H, LOH C H, TSAI K C. Overview of Taiwan earthquake loss estimation system [J]. Natural Hazards, 2006, 37(1–2): 23–37.

    Article  Google Scholar 

  27. LEE Sang-hyuk, LEE Sang-min, SOHN Gyo-yong, KIM Jaeh-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.

    Article  MathSciNet  Google Scholar 

  28. LEE Sang-hyuk, PARK Wook-je, JUNG Dong-yean. Similarity measure design and similarity computation for discrete fuzzy data [J]. Journal of Central South University of Technology, 2011, 18(5): 1602–1608.

    Article  Google Scholar 

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

    Google Scholar 

  30. WEI P, CHEN H, ZHANG Y. 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.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yu-shiang Wu  (吴玉祥).

Additional information

Foundation item: Project(93-2625-Z-027-006) supported by the National Science Council of Taipei, China

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shao, Yw., Wu, Ys., Kao, Sf. et al. Application of fuzzy theory on earthquake damage rate estimation of buildings. J. Cent. South Univ. 21, 2454–2459 (2014). https://doi.org/10.1007/s11771-014-2199-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11771-014-2199-6

Key words

Navigation