Rapid visual earthquake hazard evaluation of existing buildings by fuzzy logic modeling

作者:

Highlights:

摘要

Earthquake hazard assessment of existing buildings is among the most important issues for pre- and post-earthquake warning, preparation, vulnerability, and mitigation works. In any potential earthquake prone area, it is necessary to classify the existing building stoke into different categories according to rapid, simple, reliable, logical and expert view based models and software. This paper presents a soft computation methodology based on the fuzzy logic model (FLM) and system principles for the classification of buildings into five distinctive but mutually inclusive classes in terms of fuzzy sets as “without”, “slight”, “moderate”, “heavy”, and “complete” hazard categories. The preliminary modeling stages in reinforced concrete building evaluation against possible earthquakes of magnitude seven or over in Istanbul City municipality area are presented with specific emphasis on existing building grading. The essence of this model is the fuzzy logic with its logical rule bases and inference system methodology. Visually assessable variables, namely, storey number, cantilever extension, soft storey, weak storey, building quality, pounding effect, hill-slope effect, and peak ground velocity are considered as inputs with a single output variable as earthquake hazard category. The model inputs and outputs are fuzzified with expert views and logical implications (fuzzy-rules, associations) are proposed between the input variables and output. The application of the proposed model is presented for 1249 existing reinforced concrete buildings on the European side of Istanbul, Turkey. It is found that about 49% of the buildings fall within the “complete” and “heavy” hazard categories. Majority of the buildings falls in the “moderate” hazard category.

论文关键词:Classification,Earthquake,Existing buildings,Fuzzy,Hazard,Logic,Model,Rule

论文评审过程:Available online 14 February 2010.

论文官网地址:https://doi.org/10.1016/j.eswa.2010.02.046