Deep learning-based structural health monitoring through the infusion of optical photos and vibration data

• Published in: Advances in structural engineering Vol. 28; no. 3; pp. 532 - 552
• Main Authors: Al-Qudah, Saleh, Bai, Xin, Yang, Mijia, Gao, Zhili
• Format: Journal Article
• Language: English
• Published: London, England SAGE Publications 01.02.2025
• Subjects: structural health monitoring; deep-learning; vibration; damage identification; infusion of vibration data and optical photos
• ISSN: 1369-4332; 2048-4011
• DOI: 10.1177/13694332241289173

This paper reports an investigation of deep learning techniques in structural damage identification that can overcome the limitations of traditional visual inspection. First, a vibration-based deep learning model is established to locate the damage in a beam and a truss structure. Then an optical photo-based model is established and used to classify different defects. Based on the satisfactory outcomes of these two models, a new structural health monitoring technique is proposed through the infusion of optical photos and vibration data. Vibration signals and true structural images for a truss are used to demonstrate the capability of the proposed method. It was found that the infusion of vibration data and optical photos can enhance damage identification significantly and overcome the drawbacks in the existing deep learning models due to incomplete vibration signals or blurred optical photo inputs.