Structural damage detection via phase space based manifold learning under changing environmental and operational conditions. (15th July 2022)

• Record Type: Journal Article
• Authors: Peng, Zhen; Li, Jun; Hao, Hong
• Is Part Of: Engineering structures. Volume 263(2022)
• Publication Date: 2022-07-15
• Subjects: Manifold learning -- Gaussian process regression -- Environmental effects -- Operational condition -- Damage detection -- Real-world structures; Structural engineering -- Periodicals; Structural analysis (Engineering) -- Periodicals; Construction, Technique de la -- Périodiques; Génie parasismique -- Périodiques; Pression du vent -- Périodiques; Earthquake engineering; Structural engineering; Wind-pressure; Periodicals; 624.105
• DOI: 10.1016/j.engstruct.2022.114420
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: A novel structural damage detection approach under changing environmental conditions. The proposed new approach is based on phase space embedding and manifold learning. The effectiveness and superiority are demonstrated on two real-world structures. It is sensitive to structural damage but insensitive to environmental conditions. The environmental effects can be efficiently characterized with only partial datasets. Abstract: The feasibility and performance of existing vibration-based damage detection methods to real world civil engineering structures are inevitably affected by the varying environmental and operational conditions. Reliable damage detection methods with damage features that are sensitive to structural condition change but robust to environmental and loading effects are desirable for practical applications. This paper proposes a novel structural damage detection approach based on manifold learning for the effective condition assessment of real-world structures under environmental and operational conditions. The phase space representation of the vibration characteristics is reconstructed using the identified natural frequencies of structures. Then, the intrinsic nonlinear manifold between the environmental variables and natural frequencies in the high dimensional phase space is projected to a low-dimensional representation via manifold learning. The Gaussian process regression technique is introduced to extract reliable damage index from the learned manifold structure. The effectiveness and superiority of the proposed approach are demonstrated by two real-world engineering structures, that is, the Dowling Hall Footbridge and Z24 bridge. Damage detection results obtained from the proposed approach are compared with those from the current state-of-the-art Kernel PCA method, which is a representative nonlinear dimensionality reduction method to alleviate the environmental effects. The results demonstrate that the proposed approach is sensitive to structural damage but insensitive to changes in environmental and operational conditions. More importantly, the nonlinear environmental effects can be efficiently characterized by the proposed approach, using only partial datasets with environmental variations in the training datasets.