Towards a deep learning-based unified approach for structural damage detection, localisation and quantification. (May 2023)

• Record Type: Journal Article
• Authors: Lomazzi, Luca; Giglio, Marco; Cadini, Francesco
• Is Part Of: Engineering applications of artificial intelligence. Volume 121(2023)
• Publication Date: 2023-05
• Subjects: Damage detection -- Damage localisation -- Damage quantification -- Machine learning -- Ultrasonic guided wave -- SHM; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2023.106003
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Ultrasonic guided waves have been extensively employed for characterising structural damage thanks to their sensitivity to defects. Although they are easy to excite and acquire, heavy processing is often required to extract single-valued indicators of damage presence, or damage indices, from the acquired signals. Traditionally, damage indices have been elaborated through tomographic algorithms to generate damage probability maps, even though limitations affect the performance of such approach. Recently, the potentialities of machine learning have been leveraged to improve the accuracy of frameworks processing guided waves for damage diagnosis. However, most methods still require extracting damage indices from the acquired signals, which may bring to loss of diagnostic information and reduced accuracy. Furthermore, damage position and extent are usually roughly estimated through classification, while regression should be employed instead. In this context, this work aims (i) to test the capabilities of different supervised machine learning algorithms to localise and quantify damage through regression and (ii) to carry out a critical discussion about possible limitations of using damage indices instead of unprocessed signals. Results are compared to identify which algorithm performs better and if machine learning can improve the accuracy of damage diagnosis compared to traditional imaging methods. An experimentally validated numerical case study was used to test the capabilities of the proposed machine learning-based framework and to bring evidence of the accuracy of the algorithms involved to characterise damage with properties not seen during training.