Aiming to solve the challenges of low detection accuracy, poor anti‐interference ability, and
slow detection speed in the traditional tunnel lining defect detection methods, a novel deep …

In the field of tunnel lining crack identification, the semantic segmentation algorithms based
on convolution neural network (CNN) are extensively used. Owing to the inherent locality of …

Big data is a rapidly growing field, and new developments are constantly emerging to
address various challenges. One such development is the use of federated learning for …

Cracks are common defects in slab tracks, which can grow and expand over time, leading to
a deterioration of the mechanical properties of slab tracks and shortening service life …

Ride comfort plays an important role in determining the public acceptance of autonomous
vehicles (AVs). Many factors, such as road profile, driving speed, and suspension system …

As the most common road distress, cracks have a substantial influence on the integrity of
pavement structures. Accurate identification of crack existence and quantification of crack …

Concrete defect information is of vital importance to building maintenance. Increasingly,
computer vision has been explored for automated concrete defect detection. However …

Deep learning provides an efficient automated method for pavement condition surveys, but
the datasets used for this model are usually images taken in good lighting conditions. If …

Structural health monitoring (SHM) plays an increasingly vital role in guaranteeing the
operation success of marine and offshore (MO) structures while reducing their risks of …

The motor imagery brain–computer interface (MI-BCI) system is currently one of the most
advanced rehabilitation technologies, and it can be used to restore the motor function of …