This article provides a comprehensive review of deep learning-based structural health
monitoring (DL-based SHM). It encompasses a broad spectrum of DL theories and …

This paper deals with state of the art in structural health monitoring (SHM) methods in
offshore and marine structures. Most SHM methods have been developed for onshore …

Structural damage detection using unsupervised learning methods has been a trending
topic in the structural health monitoring (SHM) research community during the past decades …

Using deep learning (DL) to recognize building and infrastructure damage via images is
becoming popular in vision‐based structural health monitoring (SHM). However, many …

Structural Health Monitoring (SHM) of civil structures has been constantly evolving with
novel methods, advancements in data science, and more accessible technology to address …

In structural health monitoring (SHM) systems, data loss inevitably occurs and reduces the
applicability of SHM techniques, such as condition assessment and damage identification …

The demand for resilient and smart structures has been rapidly increasing in recent
decades. With the occurrence of the big data revolution, research on data‐driven structural …

Abstract Design of an automated and continuous framework is of paramount importance to
structural health monitoring (SHM). This study proposes an innovative multi-task …

An image-based deep learning framework is developed to predict nonlinear stress
distribution and failure pattern in microstructural representations of composite materials in …

Unsupervised learning is an effective and practical methodology for structural health
monitoring when the preparation of labeled training data regarding damaged states is …