A novel deep learning-based method for damage identification of smart building structures:

TL;DR: A comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field, is presented in this paper, where the benchmark datasets and the principal 1D convolutional neural network software used in those applications are also publically shared in a dedicated website.

TL;DR: This paper aims to fulfill the gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.

TL;DR: The procedure and application of vibration-based, vision-based monitoring, along with some of the recent technologies used for SHM, such as sensors, unmanned aerial vehicles (UAVs), etc. are discussed.

TL;DR: An overview of ML techniques for structural engineering is presented in this article with a particular focus on basic ML concepts, ML libraries, open-source Python codes, and structural engineering datasets.

TL;DR: A hybrid neural-network for human face recognition which compares favourably with other methods and analyzes the computational complexity and discusses how new classes could be added to the trained recognizer.

TL;DR: This article proposes a vision‐based method using a deep architecture of convolutional neural networks (CNNs) for detecting concrete cracks without calculating the defect features, and shows quite better performances and can indeed find concrete cracks in realistic situations.

TL;DR: A fast and accurate patient-specific electrocardiogram (ECG) classification and monitoring system that achieves a superior classification performance than most of the state-of-the-art methods for the detection of ventricular ectopic beats and supraventricular ectopy beats.