Bridge risk assessment based on extended belief rule base with joint optimization

被引：0
|
作者：
Yang L. [1 ]
Ye F. [1 ]
Wang Y. [1 ,2 ]
机构：
[1] Decision Sciences Institute, Fuzhou University, Fuzhou
[2] Key Laboratory of Spatial Data Mining&Information Sharing of Ministry of Education, Fuzhou University, Fuzhou
来源：
Xitong Gongcheng Lilun yu Shijian/System Engineering Theory and Practice | 2020年 / 40卷 / 07期
基金：
中国国家自然科学基金;
关键词：
Bridge risk assessment; Extended belief rule base; Joint optimization; Number of parameters; Value of parameters;
D O I：
10.12011/1000-6788-2019-1080-12
中图分类号：
学科分类号：
摘要：
As an important approach to avoiding the safety accidents of bridges and ensuring the safety of the public, bridge risk assessments have been widely concerned by lots of scholars. However, the existing models of bridge risk assessment mostly ignored the optimization of the value of parameters and the number of parameters in modeling process, leading to the failure of improving the accuracy of bridge risk assessment. Therefore, based on the existing bridge risk assessment model using extended belief rule base (EBRB), this paper proposes a rule generation method and a rule reduction method based on parameter optimization and data envelopment analysis, then a joint optimization methods is further proposed for optimizing EBRB. Finally, a commonly used and well-known dataset about bridge risk assessment is applied to validate the efficiency of the proposed model. In comparison with the results of previous studies, the bridge risk assessment model based on EBRB with joint optimization has shown superior performance in both improving the evaluation accuracy and reducing the modeling complexity. © 2020, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.
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页码：1870 / 1881
页数：11
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