Field experiment on a PSC-I bridge for convolutional autoencoder-based damage detection. (July 2021)

• Record Type: Journal Article
• Authors: Lee, Kanghyeok; Jeong, Seunghoo; Sim, Sung-Han; Shin, Do Hyoung
• Other Names: Li Hui guest-editor.; Spencer Billie F. guest-editor.
• Is Part Of: Structural health monitoring. Volume 20:Number 4(2021)
• Page Start: 1627
• Page End: 1643
• Publication Date: 2021-07
• Subjects: Damage detection -- deep learning -- convolutional autoencoder -- field experiment -- PSC-I bridge -- autoencoder -- machine learning -- field demonstration; Structural health monitoring -- Periodicals; Structural stability -- Periodicals; Strength of materials -- Periodicals; Nondestructive testing -- Periodicals; Constructions -- Stabilité -- Périodiques; Résistance des matériaux -- Périodiques; Contrôle non destructif -- Périodiques; Electronic journals; 624.17
• DOI: 10.1177/1475921720926267
• Languages: English
• ISSNs: 1475-9217

Abstract

In this study, a field experiment was performed for damage detection on a PSC-I bridge based on a convolutional autoencoder using the damage detection approach proposed in a previous study by the authors. The field experiment measured the acceleration and strain data of the PSC-I bridge while a single vehicle passed the bridge; subsequently, these data were used to train and test the convolutional autoencoder–based damage detection model. The results of the test showed that the convolutional autoencoder–based model could perform accurate and robust damage detection. Furthermore, these findings indicate that the convolutional autoencoder–based damage detection could also perform satisfactorily in practice. The results of this study can form the basis to facilitate the adoption of the convolutional autoencoder–based damage detection method to monitor bridges in practice.