Amin Hekmatnejad
email ahekmatnejad@uc.cl
Research Lines:
Rock mechanics underground mine desing geostatistics machine learning applied in mining application probabilistic damage and fracture mechanics rock mass characterizationProfile
Dr. Amin Hekmatnejad is a Mining Engineer with a Ph.D. from the University of Chile, specializing in rock mechanics, digital geomechanics, and underground mine design. He is currently a full time professor at Pontificia Universidad Católica de Chile and associate researcher at the Center for Mathematical Modeling (CMM), where he leads interdisciplinary projects on digital rock mass twins, underground hydrogen storage, and AI-assisted geomechanical risk assessment. He is also involved in computational neuroscience research focused on seizure prediction from ECG signals, in collaboration with the CMM and UC Neuroscience Center.
He previously held academic positions at the University of Talca and Pontificia Universidad Católica de Valparaíso (PUCV), contributing to master's and Ph.D. programs. Earlier in his career, he worked in the oil and gas industry as a reservoir geomechanics specialist and geostatistical modeler at the Iranian Offshore Oil Company, gaining hands-on experience in borehole stability, reservoir modeling, and drilling operations.
He is the director of the Digital Rock Mass Twin & Supercomputing Laboratory, where AI, DFN-FDEM simulation, spatial statistics, and real-time visualization are used to solve large-scale rock engineering challenges. He developed the Universal Discontinuity Index (UDi) and R-Dis-Frag modeling system, applied in projects like El Teniente Mine.
With over 30 indexed publications, Dr. Hekmatnejad has led FONDECYT and FONDEF research grants, received the “Best Research Award on New Science Inventions” (2021), and currently supervises seven Ph.D. students and two postdoctoral researchers. He maintains active collaborations with universities in Canada, China, France, Japan, Australia, and beyond.
Research Lines
Network
Keywords from publications
| Title | Year | Doi |
|---|---|---|
| Quantitative Semantic Models in Digital Twin Representations of Rock Masses Using Universal Discontinuity Index (UDi) | 2025 | https://doi.org/10.1007/s00603-024-04279-6 |
| Toward intelligent tunnel construction: The universal discontinuity index for rapid probabilistic prediction of progressive batch rock-block failure-A theoretical, numerical, and experimental validation framework | 2025 | https://doi.org/10.1016/j.tust.2025.107017 |
| A High-Precision Method for Determining Elastic Parameters in Hard Rock Using Cellular Automata and Simulated Annealing Particle Swarm Optimization | 2025 | https://doi.org/10.1142/S1758825125500188 |
| Development of an Adaptive Algorithm for PDC Bit Wear Rate Prediction in Oil and Gas Well Drilling Considering Formation's Geomechanical Characteristics | 2025 | https://doi.org/10.22044/jme.2025.15545.2980 |
| Mitigating the Uncertainties of Geomechanical Models by Estimating the Shear Wave Slowness Using Highly Accurate Deep Neural Network Models | 2025 | https://doi.org/10.22044/jme.2025.15294.2932 |
| Enhancing production rates at El Teniente's black cave mine through optimizing HF hole distribution using discrete fracture network modeling and geostatistical simulation methods | 2025 | https://doi.org/10.1016/j.rockmb.2024.100165 |
| Integrating 1D and 3D geomechanical modeling to ensure safe hydrogen storage in bedded salt caverns: A comprehensive case study in canning salt, Western Australia | 2024 | https://doi.org/10.1016/j.ijhydene.2024.07.341 |
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