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 …

Abstract Machine learning (ML) has become the most successful branch of artificial
intelligence (AI). It provides a unique opportunity to make structural engineering more …

As civil structures age and deteriorate, it becomes crucial to conduct structural health
monitoring (SHM) to ensure safety and timely maintenance. Surface defect detection plays a …

Previous research has shown the high accuracy of convolutional neural networks (CNNs) in
asphalt and concrete crack detection in controlled conditions. Yet, human-like generalisation …

Diagnosing surface cracks of concrete structures has been a critical aspect of assessing
structural integrity. Existing diagnosis technologies are time-consuming, subjective, and …

Over the last two decades, the integration of big data and deep learning technologies has
demonstrated remarkable effectiveness across various domains of civil engineering, leading …

With the rapid increase of ageing infrastructures worldwide, effective and robust inspection
techniques are highly demanding to evaluate structural conditions and residual lifetime. The …

Visual data captured at construction sites is a rich source of information for the day-to-day
operation of construction projects. The development of deep-learning-based methods has …

Crack monitoring has been a hot research topic in structural health monitoring. However, the
current research on deep learning-based crack image focuses more on cracks at a certain …

Defect detection of petrochemical pipelines is an important task for industrial production
safety. At present, pipeline defect detection mainly relies on closed circuit television method …