Shield tunneling has been prevalent in tunnel construction since its introduction into the
field. To take advantage of the massive data generated during tunneling and to assist in …

Preventing/mitigating natural disasters in urban areas can indirectly be part of the 17
sustainable economic and social development intentions according to the United Nations in …

The application of ensemble learning models has been continuously improved in recent
landslide susceptibility research, but most studies have no unified ensemble framework …

Building a high-accuracy utilization factor prediction model for tunnel boring machine with
limited available data is a research challenge. To solve the problem mentioned above, a …

This paper proposes a deep reinforcement learning (DRL)-based model as a valuable tool
to improve the performance of the driving system (ie thrust force and cutterhead torque) of a …

This study investigates the safety status for a shield machine during tunnelling. An
enhancement of the existing set pair analysis (SPA) method incorporating with interval …

In constructing long-distance shield tunnels, it is a difficult challenge to maintain the
tunneling trajectory consistent with the design tunnel axis. The accurate prediction of the …

This study aims to examine the performance of artificial neural network (ANN) model based
on 1137 datasets of super-large (1.0–2.5 m in equivalent diameter) and long (40.2–99 m) …

Accurate and computationally efficient prediction of wave run-up is required to mitigate the
impacts of inundation and erosion caused by tides, storm surges, and even tsunami waves …

In the last few decades, deep learning (DL) has afforded solutions to macroscopic problems
in petroleum engineering, but mechanistic problems at the microscale have not benefited …