Supervised fuzzy logic modeling for building earthquake hazard assessment
Building hazard assessment prior to earthquake occurrence exposes interesting problems especially in earthquake prone areas. Such an assessment provides an early warning system for building owners as well as the local and central administrators about the possible hazards that may occur in the next scenario earthquake event, and hence pre- and post-earthquake preparedness can be arranged according to a systematic program. For such an achievement, it is necessary to have efficient models for the prediction of hazard scale of each building within the study area. Although there are subjective intensity index methods for such evaluations, the objective of this paper is to propose a useful tool through fuzzy logic (FL) to classify the buildings that would be vulnerable to earthquake hazard. The FL is a soft computing intelligent reasoning methodology, which is rapid, simple and easily applicable with logical and rational association between the building-hazard categories and the most effective factors. In this paper, among the most important factors are the story number (building height), story height ratio, cantilever extension ratio, moment of inertia (stiffness), number of frames, column and shear wall area percentages. Their relationships with the five hazard categories are presented through a supervised hazard center classification method. These five categories are none, slight, moderate, extensive, and complete hazard classes. A new supervised FL classification methodology is proposed similar to the classical fuzzy c-means procedure for the allocation of hazard categories to individual buildings. The application of the methodology is presented for Zeytinburnu quarter of Istanbul City, Turkey. It is observed that out of 747 inventoried buildings 7.6%, 50.0%, 14.6%, 20.1%, and 7.7% are subject to expected earthquake with none, slight, moderate, extensive, and complete hazard classes, respectively.
earthquake hazard categories moment of inertia stiffness supervised fuzzy model