Risk assessment for vehicle fires based on cloud model

Abstract

Fire accidents in transportation happen continuously and increasingly year by year, and cause great losses of lives and property. In order to reduce unexpected major vicious fires, it is very necessary to provide an effective risk assessment of vehicle fires, which could help fire brigade to devise a suitable inspection programme for the implementation of fire precaution regulations. Cloud model which is based on traditional fuzzy set theory and probability theory is an uncertain conversion model between some qualitative concept with a description of natural language value and its quantitative description. Based on risk assessment of vehicle fires, some qualitative description of index's weight and comment with natural languages could be converted to quantitative value by cloud model, and then make an integrated risk assessment on vehicle fires through cloud arithmetic rules. Exemplified case finally indicates that the model is feasible and the assessment result accords with the practice.