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Title	Structural Health Monitoring Of Existing Building Structures For Creating Green Smart Cities Using Deep Learning
ID_Doc	53267
Authors	Kapoor N.R.; Kumar A.; Arora H.C.; Kumar A.
Year	2022
Published	Recurrent Neural Networks: Concepts and Applications
DOI	http://dx.doi.org/10.1201/9781003307822-15
Abstract	The structure health monitoring of a concrete structure requires several sensors and numerous non-destructive tests (NDT) to identify different kinds of damages and defects in the various structural elements. However, to study, observe, and assess the damages and defects by NDT require extraordinary amounts of time and effort. Therefore, to reduce human efforts and cost, artificial intelligence (AI) is one technique established to predict the structural health of buildings. Computational techniques (CT) for structural health monitoring (SHM) also provide numerical simulation tools that are essential for the interpretation of experimental measurements, as well as for identifying the damages and their characterization. Machine learning (ML) and deep learning (DL) techniques are the subfields of AI. In ML, the data is inputted manually in the system to generate the predictions. In the meantime, DL works to process the image through hidden layers and predict the results based on the damage that the machine learns from past experiences. In this chapter the authors discuss how AI tools like ML and DL help predict the health of buildings by presenting a case study of the Indian region. © Ron Waddams/Bridgeman Images.
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