Automation of construction machines has grown rapidly in recent years as a response to the
need to increase productivity, increase construction safety, decrease costs, and overcome …

Because of the disasters associated with slope failure, the analysis and forecasting of slope
stability for geotechnical engineers are crucial. In this work, in order to forecast the factor of …

To accurately estimate the rock shear strength parameters of cohesion (C) and friction angle
(φ), triaxial tests must be carried out at different stress levels so that a failure envelope can …

In this article, maximum surface settlement (MSS) of urban tunnels was investigated on the
basis of three operational parameters of tunnel width, tunnel depth, excavation method, as …

During the construction of a tunnel, water inflow is one of the most common and complex
geological disasters and has a large impact on the construction schedule and safety. When …

This study aims to propose four Machine Learning methods of Gaussian process regression
(GPR), support vector regression (SVR), decision trees (DT), and K-nearest neighbors …

This paper constructs the prediction intervals (PIs) of the tunnels' settlement caused by the
shielding steering process. The hybrid genetic algorithm-neural network (GA-NN) is …

This research intends to use machine learning approaches to predict tunnel geology and its
construction time and costs. For this purpose, the Gaussian Process Regression (GPR) …

Given the frequent cost overruns and schedule delays associated with tunnel construction
projects, it is imperative that a detailed estimation of both be developed and considered prior …

The purpose of this study is to apply a modern intelligent method of Gaussian process
regression (GPR) to predict the geological parameter of Rock Quality Designation (RQD) …