This paper proposes a customized convolutional neural network for crack detection in
concrete structures. The proposed method is compared to four existing deep learning …

Surface cracks on the concrete structures are a key indicator of structural safety and
degradation. To ensure the structural health and reliability of the buildings, frequent structure …

Recent developments in networked and smart sensors have significantly changed the way
Structural Health Monitoring (SHM) and asset management are being carried out. Since the …

Deep learning has been widely applied to vision‐based structural damage detection, but its
computational demand is high. To avoid this computational burden, a novel crack detection …

The visual inspection of massive civil infrastructure is a common trend for maintaining its
reliability and structural health. However, this procedure, which uses human inspectors …

Dielectric elastomer sensors (DESs) have been known as highly stretchable strain sensors
for about two decades. They are composite films consisting of alternating dielectric and …

Visual damage inspection of steel frames by eyes alone is time-consuming and
cumbersome; therefore, it produces inconsistent results. Existing computer vision-based …

This paper explores the capabilities of decision tree ensembles (DTEs) for detecting and
localizing damage in structural health monitoring (SHM). Unlike research on many other …

Concrete surface crack detection is an important means to ensure infrastructure security,
including bridges and tunnels. In recent years, computer vision has been widely adopted for …

Fatigue cracks are critical types of damage in steel structures due to repeated loads and
distortion effects. Fatigue crack growth may lead to further structural failure and even induce …