Machine Learning Based Crack Mode Classification From Unlabeled Acoustic Emission Waveform Features

TL;DR: This study mainly focuses on the scope and recent advancements of the Non-destructive Testing (NDT) application for SHM of concrete, masonry, timber and steel structures.

TL;DR: In this paper , machine learning approaches to detect, classify, locate, and quantify pipeline anomalies based on intelligent interpretation of routine operation data, nondestructive testing data, and computer vision data are discussed.

TL;DR: A comprehensive review of the state of the art in the field of acoustic emission waveform feature extraction can be found in this article, along with a detailed overview of waveform clustering, labeling, and error analysis strategies.

TL;DR: An overview of ML algorithms used for smart monitoring is presented, providing an overview of categories ofML algorithms for smart Monitoring that may be modified to achieve explainable artificial intelligence in civil engineering.

TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.

TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.

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TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.