Deep learning is a new area of machine learning research. Deep learning technology
applies the nonlinear and advanced transformation of model abstraction into a large …

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 …

The integration of the shotcrete system with Ultra-High Performance Concrete (UHPC) to
reinforce deficient concrete structures has been recognized as having significant potential …

This work presents a process for automated end-to-end inspection of area defects—
specifically spalls and delaminations—in RC bridges. The process uses a mobile robotic …

There is a global research trend to enhance condition assessment of the concrete
infrastructure by the development of advanced nondestructive testing (NDT) methods …

This study investigates the semantic segmentation of common concrete defects when using
different imaging modalities. One pre-trained Convolutional Neural Network (CNN) model …

This paper proposes an approach for the reliable identification of subsurface damages in
thermal images of concrete structures. The work explores how to mitigate false positives in …

The reinforcement and imitation learning paradigms have the potential to revolutionise
robotics. Many successful developments have been reported in literature; however, these …

Urban heat islands (UHIs) have been worsening, and Tokyo, Japan, is among the worst
globally. The urban thermal environment requires measurement to formulate effective …

Annotated datasets play a significant role in developing advanced Artificial Intelligence (AI)
models that can detect bridge structure defects autonomously. Most defect datasets contain …