With the extensive adoption of artificial intelligence (AI), construction engineering and
management (CEM) is experiencing a rapid digital transformation. Since AI-based solutions …

Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Abstract Out-of-distribution (OOD) detection is critical to ensuring the reliability and safety of
machine learning systems. For instance, in autonomous driving, we would like the driving …

With recent advances in non-contact sensing technology such as cameras, unmanned aerial
and ground vehicles, the structural health monitoring (SHM) community has witnessed a …

Traditional methods of discovering new materials, such as the empirical trial and error
method and the density functional theory (DFT)‐based method, are unable to keep pace …

Data-driven methods in structural health monitoring (SHM) is gaining popularity due to
recent technological advancements in sensors, as well as high-speed internet and cloud …

Anomaly detection is an important problem that has been well-studied within diverse
research areas and application domains. The aim of this survey is two-fold, firstly we present …

Computer vision techniques, in conjunction with acquisition through remote cameras and
unmanned aerial vehicles (UAVs), offer promising non-contact solutions to civil infrastructure …

This paper compares the performance of common edge detectors and deep convolutional
neural networks (DCNN) for image-based crack detection in concrete structures. A dataset of …

In the past two decades, structural health monitoring (SHM) systems have been widely
installed on various civil infrastructures for the tracking of the state of their structural health …