Abstract
Many kinds of uncertainties exist in in-situ structure performance assessment and damage detection in structure health monitoring (SHM). At present, most research in SHM field focuses on statistical analysis, data acquisition, feature extraction and data reduction. A method is introduced in the paper to improve pattern recognition and damage detection by supplementing intelligent structure health monitoring (ISHM) with fuzzy sets. Based on the developed method, a multi-class fuzzy comprehensive evaluation method (MFCEM) is used to identify the damage status of piezoelectric concrete frame structure during the service. First, a multi-level fuzzy comprehensive evaluation model and index system of the reinforced concrete frame structure is established according to the fuzzy evaluation theory. Second, the fuzzy evaluation matrix of the target system is obtained and the weight coefficient of index is reasonably determined. In the end, the evaluation result of each layer for the system is calculated by the fuzzy theory. In order to validate the proposed method, a two story reinforced concrete frame structure is built for the experimental research. Several pairs of smart aggregates (SA) used as transducers are embedded into the concrete frame structure. By analyzing the signal and combining with the developed
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