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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.
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 brought to realize the structural assessment with embedding expert knowledge into the assessment system.