Artificial neural network (ANN) has acquired noticeable interest from the research
community to handle complex problems in Construction and Building engineering (CB). This …

Because of the long duration, multiplicity of technical disciplines, large number of project
stakeholders, and high levels of complexity and uncertainty, project risk propagation control …

Occupational accident rates in construction projects are usually higher than other industries
in most countries, even though safety management systems are continuously improving …

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 …

A limited assessment of the development process and various stages of machine learning
(ML) based solutions for construction engineering (CE) problems are available in the …

In this work, the support vector regression method is combined with six metaheuristic
optimization models of particle swarm optimization, grey wolf optimization, multiverse …

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 …

Structural deformation prediction is important for maintaining the serviceability and safety of
civil infrastructure. However, current deep learning-based single prediction models face …