A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges. (June 2016)

• Record Type: Journal Article
• Authors: Silva, Moisés; Santos, Adam; Figueiredo, Eloi; Santos, Reginaldo; Sales, Claudomiro; Costa, João C.W.A.
• Is Part Of: Engineering applications of artificial intelligence. Volume 52(2016:Apr.)
• Page Start: 168
• Page End: 180
• Publication Date: 2016-06
• Subjects: Structural health monitoring -- Genetic algorithm -- Concentric hypersphere algorithm -- Damage detection -- Environmental and operational variability -- Clustering; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2016.03.002
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: This paper proposes a novel unsupervised and nonparametric genetic algorithm for decision boundary analysis (GADBA) to support the structural damage detection process, even in the presence of linear and nonlinear effects caused by operational and environmental variability. This approach is rooted in the search of an optimal number of clusters in the feature space, representing the main state conditions of a structural system, also known as the main structural components. This genetic-based clustering approach is supported by a novel concentric hypersphere algorithm to regularize the number of clusters and mitigate the cluster redundancy. The superiority of the GADBA is compared to state-of-the-art approaches based on the Gaussian mixture models and the Mahalanobis squared distance, on data sets from monitoring systems installed on two bridges: the Z-24 Bridge and the Tamar Bridge. The results demonstrate that the proposed approach is more efficient in the task of fitting the normal condition and its structural components. This technique also revealed to have better classification performance than the alternative ones in terms of false-positive and false-negative indications of damage, suggesting its applicability for real-world structural health monitoring applications.