Research

Rapid Estimation of Truck Cycle Time in Open-Pit Mine Haulage Based on Feature-Optimized Machine Learning

Characterization of engineering properties in stabilized expansive soil: Integrating experimental investigation and machine learning modeling

Hybrid extreme gradient boosting regressor models for the multi-objective mixture design optimization of cementitious mixtures incorporating mine tailings as fine aggregates

Machine learning with SHapley additive exPlanations for evaluating mine truck productivity under real-site weather conditions at varying temporal resolutions

Deep Neural Network Models for Improving Truck Productivity Prediction in Open-pit Mines

Machine learning-assisted characterization of the thermal conductivity of cement-based grouts for borehole heat exchangers

Using deep neural networks coupled with principal component analysis for ore production forecasting at open-pit mines

Forecasting unconfined compressive strength of calcium sulfoaluminate cement mixtures using ensemble machine learning techniques integrated with shapely-additive explanations

A machine learning model to predict unconfined compressive strength of alkali-activated slag-based cemented paste backfill