EARTHQUAKE VULNERABILITY ASSESSMENT FOR HOSPITAL BUILDINGS USING A GIS-BASED GROUP MULTI CRITERIA DECISION MAKING APPROACH: A CASE STUDY OF TEHRAN, IRAN
- 1Center of Excellence in Geomatic Engineering in Disaster Management, School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
- 2School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
- 3Control and Intelligent Processing, Centre of Excellence, School of ECE, University of Tehran, Tehran, Iran
Keywords: Group Multi Criteria Decision Making, GIS, Disaster Management, Earthquake Vulnerability Assessment
Abstract. Nowadays, urban areas are threatened by a number of natural hazards such as flood, landslide and earthquake. They can cause huge damages to buildings and human beings which necessitates disaster mitigation and preparation. One of the most important steps in disaster management is to understand all impacts and effects of disaster on urban facilities. Given that hospitals take care of vulnerable people reaction of hospital buildings against earthquake is vital. In this research, the vulnerability of hospital buildings against earthquake is analysed. The vulnerability of buildings is related to a number of criteria including age of building, number of floors, the quality of materials and intensity of the earthquake. Therefore, the problem of seismic vulnerability assessment is a multi-criteria assessment problem and multi criteria decision making methods can be used to address the problem. In this paper a group multi criteria decision making model is applied because using only one expert’s judgments can cause biased vulnerability maps. Sugeno integral which is able to take into account the interaction among criteria is employed to assess the vulnerability degree of buildings. Fuzzy capacities which are similar to layer weights in weighted linear averaging operator are calculated using particle swarm optimization. Then, calculated fuzzy capacities are included into the model to compute a vulnerability degree for each hospital.