An improved hierarchical fuzzy TOPSIS approach to identify endangered earthquake-induced buildings. (November 2018)

• Record Type: Journal Article
• Authors: Ranjbar, Hamid Reza; Nekooie, Mohammad Ali
• Is Part Of: Engineering applications of artificial intelligence. Volume 76(2018)
• Page Start: 21
• Page End: 39
• Publication Date: 2018-11
• Subjects: Mitigation plan -- Building exposure rate -- Fuzzy multi-criteria decision making (FMCDM) -- Improved hierarchical fuzzy TOPSIS -- Decision support system; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.08.007
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: As societies increase their preparedness level for facing earthquakes, many unfortunate consequences of the events may significantly decrease. An example of this can be seen in cases where pre-earthquake mitigation activities taken like identifying and renovating vulnerable buildings, assessing road network vulnerability, locating the emergency centers and identifying hazardous materials warehousing. For mitigation decision making, the seismic risk for an individual building consists of the actual dangers of the building and the risk of damage to the building from surrounding environment in time of earthquake occurrence which this concept is considered as a building exposure rate to seismic hazards. Thus, the exploration of an index by using expert knowledge for quantifying the multi-dimensional concept of building exposure rate to seismic hazards before the incidence of earthquakes is vital. According to existence of imprecision and uncertainty in experts' opinions, this paper adopts the improved hierarchical fuzzy TOPSIS approach as a fuzzy multi criteria decision making technique (FMCDM) for integrating factors affecting building exposure rate in two scenarios (daytime and nighttime). This approach effectively considers the experts' expressions and the layered hierarchy of criteria. The obtained map was categorized into 4 classes including low, medium, high, and very high risk in one of the most vulnerable regions of Tehran. Then, the robustness of the approach is verified with a sensitivity analysis; 16 experiments are conducted for two scenarios which indicate partial changes in building exposure rate.