Titlebook: Artificial Intelligence in Vision-Based Structural Health Monitoring; Khalid M. Mosalam,Yuqing Gao Book 2024 The Editor(s) (if applicable)

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Book 2024oring (SHM). In this data explosion epoch, AI-aided SHM and rapid damage assessment after natural hazards have become of great interest in civil and structural engineering, where using machine and deep learning in vision-based SHM brings new research direction. As researchers begin to apply these co

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Book 2024ion-based SHM? .This book introduces and implements the state-of-the-art machine learning and deep learning technologies for vision-based SHM applications. Specifically, corresponding to the above-mentioned scientific questions, it consists of: (1) motivation, background & progress of AI-aided visio

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Correction to: Artificial Intelligence in Vision-Based Structural Health Monitoring,

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Artificial Intelligence in Vision-Based Structural Health Monitoring

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Introduction,tructural damage in an instrumented structural system and can be classified in terms of their scale–local or global damage detection methods. Whereas global methods employ numerical models that intake global characteristics of a structure (such as modal frequencies) that are indicative of possible d

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Vision Tasks in Structural Imagesion detection. Soukup and Huber-Mörk [.] applied CNN to detect steel surface defects of the railway, which is a binary classification problem. Cha et al. [.] used a deep CNN to detect concrete cracks as a binary classification without calculating the defect features.

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Basics of Machine Learningng, . learning, and . learning based on the data characteristics. Supervised learning trains a model that can learn and infer the mapping function between the input data and their corresponding labels. Compared with the other two categories, supervised learning is the most active branch in ML resear

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Basics of Deep Learningple periods of research and development [.]. The concept of DL originated from the study in neuroscience and its objective was to simulate the mechanisms of the human brain to understand and interpret data, e.g., images, texts, and sounds.

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Structural Image Classificationhnologies into the field of SHM is not straightforward. Therefore, in this chapter, the feasibility of applying AI methods in vision-based SHM is explored. This is mainly evaluated by the accuracy and efficiency of the trained AI models. ML and DL can achieve very accurate or promising results throu