This paper presents a new method of damage condition assessment that allows accommodating other types of uncertainties due to ambiguity, vagueness, and fuzziness that are statistically non-describable. In this method, healthy observations are used to construct a fuzzy set representing sound performance characteristics. Additionally, the bounds on the similarities among the structural damage states are prescribed by using the state similarity matrix. Thus, an optimal group fuzzy sets representing damage states such as little, moderate, and severe damage can be inferred as an inverse problem from healthy observations only. The optimal group of damage fuzzy sets is used to classify a set of observations at any unknown state of damage using the principles of fuzzy pattern recognition based on an approximate principle. This method can be embedded into the system of Structural Health Monitoring (SHM) to give advice about structural maintenance and life prediction. Finally, a case study, which comes from Reference [9] for damage pattern recognition is presented and discussed. The compared result illustrates our method is more effective and general, so it is very practical in engineering.
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