PhD Candidate | The University of Adelaide | Australia
Mr. Ahmadreza Khodayari is a mining engineering researcher whose work integrates rock mechanics, blasting science, fracture mechanics, and machine learning to advance precision modelling and optimization in mining operations. With a Published Documents 8 citation index comprising 110 citations, an h-index of 4, and an i10-index of 4, his contributions span experimentally grounded studies, data-driven prediction models and mechanistic simulations that address key challenges in rock breakage and material flow behaviour.His research portfolio includes several completed and ongoing projects focused on blast modelling rock fracture characterization and artificial intelligence applications in geo-materials engineering. Notable works include the calibration of mechanistic blast models using Ernest Henry Mine datasets the development of machine learning models for predicting blast-induced fragment sizes and advanced Blender Physics Engine simulations to assess sublevel caving (SLC) material flow. He has also executed misfire impact analyses on SLC gravity flow supporting safer and more predictable caving performance. Additionally his studies on AI-driven prediction of concrete and rock fracture toughness contribute to bridging traditional fracture mechanics with modern computational intelligence.Ahmadreza’s publications are featured in respected outlets such as Engineering Fracture Mechanics Theoretical and Applied Fracture Mechanics Steel and Composite Structures and the Journal of Mining and Environment. His 2022 work on predicting mixed-mode fracture toughness using extreme gradient boosting and metaheuristic optimization has accumulated significant citations reflecting strong community interest in AI-enhanced fracture modelling. His earlier experimental studies on freeze–thaw effects in Lushan Sandstone provided valuable insights into strength degradation mechanisms in cold-region geomaterials.He collaborates with researchers from the Lebanese French University Imam Khomeini International University and other international institutions strengthening global knowledge exchange in blasting and rock mechanics. His contributions to major conferences including FragBlast MassMin and ARMA demonstrate active engagement with both scientific and industry practitioners.Through a combination of high-fidelity numerical modelling physics-based simulations and advanced data-driven techniques Ahmadreza’s research aims to enhance fragmentation predictability mine productivity and geomechanical safety. His work continues to shape emerging methodologies in intelligent mining systems contributing to more efficient and sustainable resource extraction practices worldwide.
Featured Publications
1.Fakhri, D., Khodayari, A., Mahmoodzadeh, A., Hosseini, M., Ibrahim, H. H., & Others. (2022). Prediction of mixed-mode I and II effective fracture toughness of several types of concrete using the extreme gradient boosting method and metaheuristic optimization algorithms. Engineering Fracture Mechanics, 276, 108916. Cited By: 39
2.Khodayari, A. R. (2019). Effect of freeze–thaw cycle on strength and rock strength parameters (A Lushan sandstone case study). Journal of Mining and Environment, Cited By: 27
3.Fakhri, D., Mahmoodzadeh, A., Mohammed, A. H., Khodayari, A., Ibrahim, H. H., & Others. (2023). Forecasting failure load of sandstone under different freezing–thawing cycles using Gaussian process regression method and grey wolf optimization algorithm. Theoretical and Applied Fracture Mechanics, 125, 103876. Cited By: 24
4.Hosseini, M., & Khodayari, A. R. (2018). Effects of temperature and confining pressure on mode II fracture toughness of rocks (Case study: Lushan sandstone). Journal of Mining and Environment, 9(2), 379–391. Cited By: 17
5.Khodayari, A., Fakhri, D., Mohammed, A. H., Albaijan, I., Mahmoodzadeh, A., & Others. (2023). The gene expression programming method to generate an equation to estimate fracture toughness of reinforced concrete. Steel and Composite Structures, 48(2), 163–177. Cited By: 3
His research advances intelligent blasting and rock-mass behaviour prediction, enabling safer, more efficient, and data-driven mining practices that strengthen global resource sustainability.
