This paper establishes an intelligent framework for real-time prediction of trajectory
deviations in the process of earth pressure balance (EPB) tunnelling. A hybrid model was …

This study proposes a novel hybrid MFO-XGBoost model that integrates the moth-flame
optimization (MFO) algorithm and the extreme gradient boosting (XGBoost) to predict the …

The present study deals with the evaluation of municipal solid waste incinerator bottom ash
under the various design parameters considering Geotechnical and Geoenvironmental …

The infusion of generative artificial intelligence (AI), as exemplified by models such as
ChatGPT and Bard is proving to be a revolutionary catalyst within the building and …

Since deep excavation will inevitably generate great disturbances in the surrounding
environment, excessive vertical displacement of adjacent buildings is one of the crucial …

The prediction of shear strength between soil-structure interactions is of great significance to
the stability of geotechnical engineering. In this study, 480 morphological data with seven …

This research aimed to develop a comprehensive model for the sustainable development of
creative industries in Iran through digital transformation and interpretive structural modeling …

The rockburst phenomenon is the major source of the high number of casualties and
fatalities during the construction of deep underground projects. Rockburst poses a severe …

Accurately predicting tunnel boring machine (TBM) performance is beneficial for excavation
efficiency enhancement and risk mitigation of TBM tunneling. In this paper, we develop a …

Mine blasting has adopted machine learning (ML) into its practices with the aims of
performance optimization, better decision-making process, and work safety. This study is …