Outlier analysis combined with Gaussian mixture model for structural damage detection

• Published in: Materials today : proceedings
• Main Authors: Prawin, J., Vijaya Bhaskara, G.S.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.04.2023
• Subjects: Damage; Gaussian mixture model; Mahanabolis distance; Natural frequencies; Outlier analysis; Damage; Mahanabolis distance; Natural frequencies; Gaussian mixture model; Outlier analysis
• ISSN: 2214-7853; 2214-7853
• DOI: 10.1016/j.matpr.2023.03.533

Damage diagnostic techniques based on changes in natural frequency has two major problems, limiting their applicability to practical structures. The limitations are low sensitivity to smaller-size damage and the influence of ambient and operational factors (temperature, humidity, wind, traffic, etc.). The change in natural frequency due to environmental and operational factors may be even larger than the change due to damage. A reliable vibration-based structural damage detection distinguishing damage and other confounding factors is needed to avoid false alarm of damage. This paper presents an enhanced approach combining the gaussian mixture model (GMM) and outlier analysis for damage detection considering the effect of changing environmental and operational conditions. In the proposed work, GMM is first applied to classify the feature vectors (first two natural frequencies) into different clusters. The natural frequencies obtained under similar environmental and operational conditions were classified into the same cluster by satisfying the same probability distribution. Outlier analysis is then performed for each different cluster using the Mahanabolis Distance (MD) measure. The MD is a measure of the distance between a point and a distribution. In the current study, the MD between the current data sample and the reference data of each mixture is computed and the minimum MD among all the clusters is used as the damage index (DI). The threshold to conclude the presence of damage (DI > threshold) is established based on Generalized Extreme Value Distribution using the datasets of the healthy structure with various environmental and operational conditions. The proposed method is verified using benchmark examples of numerically simulated simply supported beam and experimental wooden bridge structure. The results of the investigations concluded that the proposed DI considering the patterns in the data using GMM proves better in decoupling the damage and other confounding factors following the conventional outlier analysis of damage detection.