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Damage Pattern Recognition for Structural Health Monitoring Using Fuzzy Similarity Prescription
Author(s) -
Altunok Erdogan,
Taha Mahmoud M. Reda,
Epp David S.,
Mayes Randy L.,
Baca Thomas J.
Publication year - 2006
Publication title -
computer‐aided civil and infrastructure engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.773
H-Index - 82
eISSN - 1467-8667
pISSN - 1093-9687
DOI - 10.1111/j.1467-8667.2006.00457.x
Subject(s) - structural health monitoring , vagueness , fuzzy logic , fuzzy set , identification (biology) , ambiguity , computer science , similarity (geometry) , set (abstract data type) , pattern recognition (psychology) , data mining , membership function , artificial intelligence , mathematics , structural engineering , engineering , botany , image (mathematics) , biology , programming language
Structural health monitoring (SHM) is a systematic method for non‐destructive evaluation of a structure's performance by sensing, extracting, patterning, and recognizing features of the structural response. Most SHM approaches focus on statistical analysis for damage identification considering only random uncertainties. This article introduces a method that allows accommodating other types of uncertainties due to ambiguity, vagueness, and fuzziness which are statistically non‐describable. The proposed method deals primarily with epistemic uncertainty. The method improves damage identification by performing damage pattern recognition using fuzzy sets. In this approach, healthy observations are used to construct a fuzzy set representing healthy performance characteristics. Additionally, the bounds on the similarities among the structural damage states are prescribed. Thus, an optimal group of fuzzy sets representing damage states such as little, moderate, and severe damage can be inferred as an inverse problem from healthy observations only. Piecewise linear functions are used as fuzzy membership functions representing the states of healthy and damaged. 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 maximum approaching degree. A case study for damage pattern recognition of a model steel bridge is presented and discussed. The approach is capable of identifying damage patterns accurately.