| A Condition States Assessment System for Concrete Bridges Using Neural Networks |
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| 引用本文： | Hu Zhijian,Jia Lijun,Xiao Rucheng. A Condition States Assessment System for Concrete Bridges Using Neural Networks[J]. 中国工程科学, 2006, 4(3): 67-76 |
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| 作者姓名： | Hu Zhijian Jia Lijun Xiao Rucheng |
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| 作者单位： | [1]Department of Bridge Engineering,Tongji University, Shanghai 200092 [2]Communication College, Wuhan University of Technology, Wuhan 430063 |
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| 摘 要： | Due to continuing aging and heavy utilization of many bridges and the limited available funds, the of proper bridge condition state assessment has risen recently, which is the crucial point for rational decision-making on MR＆R activities. This paper presents a prototype of the concrete bridge condition state assessment system （CBCSAS） with the following sub-modules： inspection, parameter recognition, structural assessment, main cause identification and priority-to-action. And multi-layer neural networks, which may combine with fuzzy set theory or not, are performed to realize the structural assessment with embedding expert knowledge into the assessment system.
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| 关 键 词： | 混凝土桥梁 多层神经网络 模糊集合论 条件状态评价系统 |
| 收稿时间： | 2005-12-30 |
A Condition States Assessment System for Concrete Bridges Using Neural Networks |
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| Hu Zhijian,Jia Lijun and Xiao Rucheng. A Condition States Assessment System for Concrete Bridges Using Neural Networks[J]. Engineering Sciences, 2006, 4(3): 67-76 |
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| Authors: | Hu Zhijian Jia Lijun Xiao Rucheng |
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| Affiliation: | 1.Department of Bridge Engineering, Tongji University, Shanghai 200092; 2.Communication College, Wuhan University of Technology, Wuhan 430063;Department of Bridge Engineering, Tongji University, Shanghai 200092;Department of Bridge Engineering, Tongji University, Shanghai 200092 |
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| Abstract: | Due to continuing aging and heavy utilization of many bridges and the limited available funds, the importance of proper bridge condition state assessment has risen recently, which is the crucial point for rational decision-making on MR&R activities. This paper presents a prototype of the concrete bridge condition state assessment system (CBCSAS) with the following sub-modules: inspection, parameter recognition, structural assessment, main cause identification and priority-to-action. And multi-layer neural networks, which may combine with fuzzy set theory or not, are performed to realize the structural assessment with embedding expert knowledge into the assessment system. |
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| Keywords: | concrete bridges the condition state assessment system multi-layer neural networks fuzzy-set theory |
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