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JDR Vol.11 No.3 pp. 559-565
doi: 10.20965/jdr.2016.p0559
(2016)

Paper:

Structural Repair Prioritization of Buildings Damaged After Earthquake Using Fuzzy Logic Model

Koraphon Saicheur and Chayanon Hansapinyo

Department of Civil Engineering, Chiang Mai University
239 Huay Kaew Road, Muang District, Chiang Mai, Thailand

Received:
December 7, 2015
Accepted:
February 3, 2016
Published:
June 1, 2016
Keywords:
prioritization, repair, damaged buildings, earthquake, fuzzy logic
Abstract

Chiangrai is a city located in the seismic risk area. The recent earthquake with magnitude of 6.3 occurred on May 5, 2014 caused widespread damage to buildings. However, with limitation of engineers, equipment and budget, it is impossible to repair all buildings in the same time. Therefore, this research proposes a method to identify critical buildings and prioritize their repairing requirements using fuzzy logic. The strength of fuzzy logic is that it can approximate the vague information, unable to make decision, to the numerical data. The evaluated factors were composed of building damaged level, indirect impact and building occupancy. With the vague information, the IF-THEN rule based form was adopted to evaluate an important index of each building. Results of the analysis was found that the buildings having more important, severely damaged and high indirect impacts on the community, such as hospital buildings and power plants will be considered with higher priority to repairs. The important indexes of the buildings were 0.718 and 0.500, respectively. For buildings with less important as garage buildings, the important index was 0.114 which identified as non-urgent repair.

References
  1. [1] T. Ornthammarath, P. Warnitchai, K. Worakanchana, S. Zaman, R. Sigbjornsson, and C. G. Lai, “Probabilistic seismic hazard assessment for Thailand,” Bulletin of Earthquake Engineering, Vol.9, No.2, pp. 367-394, 2011.
  2. [2] V. Weerachart and K. Suvit, “Mae Lao Earthquake in Chiang Rai and the Mae Lao Segment of the Phayao Fault,” Proceedings of the Mae Lao Earthquake in Chiang Rai lesson to learned, Bangkok, Thailand, pp. 39-52, 2014.
  3. [3] T. Ornthammarath, “Mae Lao Earthquake 5 May 2014,” Proceedings of the Mae Lao Earthquake in Chiang Rai lesson to learned, Bangkok, Thailand, 2014, p. 32.
  4. [4] S. Tanaka, “Building damage inspection analysis in the 2007 Niigata Chuetsu-Oki earthquake, Kashiwazaki: Self-inspection analysis for damage evaluation,” Journal of Disaster Research, Vol.3, No.6, pp. 372-380, 2008.
  5. [5] S. K. Deb and G. T. Kumar, “Seismic damage assessment of reinforced concrete buildings using fuzzy logic,” The 13th World Conference on Earthquake Engineering, No.3098, Vancouver, B. C., Canada, August 1-6, 2004.
  6. [6] Z. Sen, “Rapid visual earthquake hazard evaluation of existing buildings by fuzzy logic modeling,” Expert Systems with Applications, Vol.37, No.8, pp. 5653-5660, 2010.
  7. [7] Z. Sen, “Supervised fuzzy logic modeling for building earthquake hazard assessment,” Expert Systems with Applications, Vol.38, pp. 14564-14573, 2011.
  8. [8] H. Haoxiang, C. Maolin, and L. Yongwai, “Earthquake damage assessment for RC structures based on fuzzy sets,” Mathematical Problems in Engineering, 2013.
  9. [9] N. Shiraishi, H. Furuta, M. Umano, and K. Kawakami, “Knowledge-based expert system for damage assessment based on fuzzy reasoning,” Artificial intelligence tools and techniques for civil and structural engineers, Edinburgh, United Kingdom: B.H.V. Topping, Civil-Comp Press, p. 658, 2005.
  10. [10] L. A. Zadeh, “Fuzzy sets,” Information and Control, Vol.8, pp. 338-353, 1965.
  11. [11] S. Tesfamariam and M. Saatcioglu, “Seismic risk assessment of reinforced concrete buildings using fuzzy rule based modeling,” The 14th World Conference on Earthquake Engineering, Beijing, China, October 12-17, 2008.
  12. [12] S. K. Deb and G. T. Kumar, “Seismic damage assessment of reinforced concrete buildings using fuzzy logic,” The 13th World Conference on Earthquake Engineering, No.3098, Vancouver, B.C., Canada, August 1-6, 2004.
  13. [13] A. Nieto-Morote and F. Ruz-Vila, “A fuzzy approach to construction project risk assessment,” International Journal of Project Management, Vol.29, pp. 220-231, 2011.
  14. [14] T. L. Saaty, “Decision making with the analytic hierarchy process,” International Journal of Services Sciences, Vol.1, No.1, pp. 83-98, 2008.
  15. [15] T. L. Saaty, “The Analytic Hierarchy Process,” The McGraw-Hill, New York, 1980.
  16. [16] K. Vahdat, N. J. Smith, and G. G. Amiri, “Fuzzy multicriteria for developing a risk management system in seismically prone areas,” Socio-Economic Planning Sciences, Vol.48, pp. 235-248, 2014.
  17. [17] T. J. Ross, “Fuzzy logic with engineering applications,” 2nd ed. Chichester, U.K., Wiley, chapter 4, p. 101, 2004.

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