Deep learning-based autonomous damage-sensitive feature extraction for impedance-based prestress monitoring. (15th May 2022)

• Record Type: Journal Article
• Authors: Nguyen, Thanh-Truong; Tuong Vy Phan, Thi; Ho, Duc-Duy; Man Singh Pradhan, Ananta; Huynh, Thanh-Canh
• Is Part Of: Engineering structures. Volume 259(2022)
• Publication Date: 2022-05-15
• Subjects: Deep learning -- 1-D CNN -- Prestress force -- Impedance signature -- Structural health monitoring -- Autonomous feature extraction -- Electromechanical impedance -- Prestress prediction; 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.2022.114172
• Languages: English
• ISSNs: 0141-0296

Abstract

Highlights: Impedance-based prestress monitoring with autonomous feature extraction. A newly-developed 1-D CNN-based regression method for prestress prediction. The developed method was of high accuracy. The effect of noises on the prediction error was quantified. Potentials for real-time prestress-loss monitoring of prestressed structures. Abstract: In the electromechanical impedance-based technique, the selection of proper impedance features and frequency bands has played a significant role in enhancing the results of structural damage assessment. Using hand-crafted features or inappropriate frequency bands could lead to the false alarm of structural damage and the erroneous estimation of severity and further prevent the usage of the technique for real-time structural health monitoring. This study proposes a deep learning-based autonomous feature extraction approach for impedance-based damage monitoring. A 1-dimensional convolutional neural network (1-D CNN) model is developed to automatically extract and directly learn the optimal features of damage from the raw impedance signals. The feasibility of the proposed approach is demonstrated via monitoring the prestress-loss of a post-tensioned reinforced concrete girder. As the result, it is shown that the proposed technique successfully estimates the true severity of prestress-loss in the girder, even for untrained prestress cases.