The width and buried depth of hidden cracks in tunnel lining are important indicators for
measuring the development degree of crack propagation, and evaluating the risk of lining …

Within seismology, geology, civil and structural engineering, deep learning (DL), especially
via generative adversarial networks (GANs), represents an innovative, engaging, and …

Non-destructive Testing (NDT) techniques and data-driven technologies are increasingly
applied in underground infrastructure maintenance, which can facilitate predictive …

It is not practical to obtain a large number of labeled data to train a supervised learning
network in tunnel lining nondestructive testing with ground‐penetrating radar (GPR). To …

In the last decades, the majority of the existing infrastructure heritage is approaching the end
of its nominal design life mainly due to aging, deterioration, and degradation phenomena …

The overall assessment of tunnel lining, including shapes, categories, and depths of tunnel
internal defects as well as the thickness of tunnel linings is vital to the safe operation of …

Ground-penetrating radar (GPR) is widely used for non-destructive detection of hidden
defects behind railway tunnel linings, including non-compactness, air-and water-filled voids …

Abstract Ground-Penetrating Radar (GPR) has been widely used in the backfill grouting
detections of shield tunnel construction. The proposed approach of automatic annular …

The tunnel diseases of high-speed railways present significant risks under the substantial
dynamic loads from trains. Train-mounted ground-penetrating radar detection data for …

Existing lightweight artificial intelligence models for interpreting tunnel lining Ground
Penetrating Radar (GPR) data often suffer from inadequate accuracy and robustness owing …