Cloud Model of Highway Damage under Master Natural Disasters
Donglan Su, Zhongyin Guo, Chengyu Hu · CICTP 2017 · 2018
As the main emergency rescue channel during a disaster, highway damage has important influence on rescue work. However, there is a complicated internal process of highway damage under major natural disasters, and its failure mechanism and estimate were difficult to describe accurately by a mathematical model. Considering the advantage of cloud models in solving uncertain problems, a cloud model was introduced into the digital characteristics research of road damage and application in this paper. First, the uncertainty of highway damage was analyzed, and the input format of a damage sample were given based on a backward cloud generator, then the algorithm steps of cloud model digital features were put forward. Based on the highway damage survey data in 2008’s Wenchuan earthquake and the water damage data of national and provincial roads in Yunnan Province, some cloud model digital characteristics of the road subgrade and road were generated according to a backward cloud generator algorithm. This highway damage cloud model figure was generated by MATLAB software, which implements the mathematical description and characterization of road damage uncertainty. The research provides an understanding of highway damage analysis and forecast, which could support disaster relief efficiency and reduce the losses of property.