Titlebook: Structural Health Monitoring Based on Data Science Techniques; Alexandre Cury,Diogo Ribeiro,Michael D. Todd Book 2022 The Editor(s) (if ap

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Vibration-Based Structural Damage Detection Using Sparse Bayesian Learning Techniques,ions based on analytical approximations or numerical sampling, including the expectation–maximization, Laplace approximation, variational Bayesian inference, and delayed rejection adaptive metropolis techniques. Numerical and experimental examples demonstrate that the proposed SBL method can accurat

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Bayesian Deep Learning for Vibration-Based Bridge Damage Detection,on. The uncertainty-adjusted reconstruction error of an unseen sequence is compared to a healthy-state error distribution, and the sequence is accepted or rejected based on the fidelity of the reconstruction. If the proportion of rejected sequences goes over a predetermined threshold, the bridge is

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Real-Time Machine Learning for High-Rate Structural Health Monitoring, the unique system characteristics and temporal constraint is the design and application of real-time learning algorithms. Here, we review and discuss a real-time learning algorithm for HRSHM applications. In particular, after introducing the HRSHM challenge, we explore fast real-time learning for t

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Real-Time Unsupervised Detection of Early Damage in Railway Bridges Using Traffic-Induced Responsesly damage, even in case of small stiffness reductions that do not impair structural safety, as well as highly robust to false detections. The ability to identify early damage, imperceptible in the original signals, while avoiding observable changes induced by environmental and operational variations

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Fault Diagnosis in Structural Health Monitoring Systems Using Signal Processing and Machine Learninhine learning (ML) techniques, (i) an ML regression algorithm used for fault detection, fault isolation, and fault accommodation, and (ii) an ML classification algorithm used for fault identification. The FD approach is validated using an artificial neural network as ML regression algorithm and a co

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