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Smart city article details

Title	Deep Learning For Vibration-Based Data-Driven Defect Diagnosis Of Structural Systems
ID_Doc	17889
Authors	Tang H.; Xie Y.; Ran L.
Year	2022
Published	The Rise of Smart Cities: Advanced Structural Sensing and Monitoring Systems
DOI	http://dx.doi.org/10.1016/B978-0-12-817784-6.00018-7
Abstract	Structural health monitoring (SHM) plays a significant role in smart city for providing information regarding the performance of structures throughout their life spans. Defect/damage diagnosis is a crucial theme discussed in SHM systems. Recent advances in deep neural networks present encouraging evidence in data diagnosis for a wide range of SHM. However, one of the key limitations in this approach lies in the robustness and scalability with respect to data scarcity, because massive data collection for deep neural network training can expensive in a smart city. This chapter provides machine learning-based sensing and data processing strategies specialized for the next-generation SHM which considers the challenges of the structures and monitoring systems. In this respect, integrated frameworks based on deep learning and transfer learning as well as data-centric techniques are proposed to perform a sustainable, cost-effective, and reliable defect diagnosis of structural systems. The efficacy and robustness of this method are demonstrated on defect diagnosis of full-scale prefabricated concrete shear wall structures with different levels of data scarcity: dynamic responses collected using sensor network embedded on the structures are used, from which defect features can be extracted automatically, thus making structural defect diagnosis more efficient. At the same time, experimental results demonstrate that this approach can in general improve the diagnosis models on the dataset with limited samples. © 2022 Elsevier Inc. All rights reserved.
Author Keywords	Convolutional neural network; Deep learning; Defect diagnosis; Structural health monitoring; Transfer learning

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