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Chengkai Fan

Assistant Professor

Laval University

Canada

Research Interests

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Professors in Canada Professors in Mining Engineering Mining Engineering professors in Canada

About

Chengkai Fan is an Assistant Professor at Laval University, Canada. His research focuses on the application of machine learning in geotechnical engineering and mining operations, particularly in optimizing the characteristics of cement-based materials and predicting truck productivity in open-pit mines. Recent articles include studies on machine learning models for estimating truck cycle times and forecasting the compressive strength of various cement mixtures.

Articles (19)

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2026

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2025

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2023

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2023

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2023

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2023

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2023

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2022

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2021

Collaborators (3)

Bei Jiang

Professor

University of Alberta

CANADA

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Wei Victor Liu

Professor

University of Alberta

CANADA

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Simon Mathias

Professor in Environmental Engineering

University of Durham

UNITED KINGDOM

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Chengkai Fan

Research Interests

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About

Articles (19)

Excavation-induced damage mechanisms of gently inclined layered shale: Insights from physical model tests and DEM simulations

Geochemical dynamics and predictive modeling of water-rock interaction during geothermal reinjection in coal mine gobs

Impact of Dry–Wet Cycles on the Strength Characteristics of Phosphogypsum-Based Composite Stabilized Laterite Soils

Nature-inspired ML for strength estimation and multi-objective optimization of cement-supplementary material-stabilized soft soils

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

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

Optimizing the layout of geothermal energy piles to minimize ground temperature changes

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

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

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

Effects of site operating conditions on real site TKPH (tonne-kilometer-per-hour) of ultra-large off-the-road tires

Weighted ensembles of artificial neural networks based on Gaussian mixture modeling for truck productivity prediction at open-pit mines

Prediction of truck productivity at mine sites using tree-based ensemble models combined with Gaussian mixture modelling

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

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

Preprocessing large datasets using Gaussian mixture modelling to improve prediction accuracy of truck productivity at mine sites

Strain characteristics and permeability evolution of faults under stress disturbance monitoring by fibre bragg grating sensing and pressure pulses

Collaborators (3)

Bei Jiang

Wei Victor Liu

Simon Mathias

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