Biography

Dr. Manoj Khandelwal

Federation University, Australia


Email: [email protected]


Qualifications

2004 – 2007 PhD, Indian Institute of Technology Bombay, India

2000 – 2002 M.Tech, Indian Institute of Technology (BHU), India


Publications (Selected)

  1. Qi, H., Zhou, J., Khandelwal, M., Onifade, M., Lawal, A. I., Li, C., ... & Genc, B. (2026). An optimized machine learning framework for prediction of coal abrasive index: Leveraging supervised learning, metaheuristic optimization, and interpretability analysis. Fuel, 403, 136065.
  2. Zvarivadza, T., Grobler, H., Rajpurohit, S. S., Moyo, S., Onifade, M., & Khandelwal, M. (2025). Advanced machine learning for pillar stress prediction and design optimisation in hardrock platinum mining: enhancing safety and sustainability on the Great Dyke of Zimbabwe. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 11(1), 113.
  3. Mudamburi, W., Zvarivadza, T., Muwirimi, T. B., Onifade, M., & Khandelwal, M. (2025). Optimisation of stope support system using kinematic analysis and numerical modelling–A sustainable mining approach. Results in Earth Sciences, 3, 100083.
  4. Zhou, J., Zhang, Y., Qiu, Y., Peng, K., & Khandelwal, M. (2025). Enhancing tunnel safety with machine learning models for ground behavior prediction. Tunnelling and Underground Space Technology, 165, 106888.
  5. Lawal, A. I., Mulenga, F., Kwon, S., Onifade, M., & Khandelwal, M. (2025). Optimized Slope Stability Assessment Using an Intuitionistic Fuzzy MCDM Approach for LEM Model Selection. Geotechnical and Geological Engineering, 43(5), 229.
  6. Chen, Y., Khandelwal, M., Onifade, M., Zhou, J., Lawal, A. I., Bada, S. O., & Genc, B. (2025). Predicting the hardgrove grindability index using interpretable decision tree-based machine learning models. Fuel, 384, 133953.
  7. Li, E., Zhang, Z., Zhou, J., Khandelwal, M., Yu, Z., & Monjezi, M. (2025). Indirect hazard evaluation by the prediction of backbreak distance in the open pit mine using support vector regression and chicken swarm optimization. Geohazard Mechanics, 3(1), 1-14.
  8. Yang, B., Jahed Armaghani, D., Fattahi, H., Afrazi, M., Koopialipoor, M., Asteris, P. G., & Khandelwal, M. (2025). Optimized random forest models for rock mass classification in tunnel construction. Geosciences, 15(2), 47.
  9. Zvarivadza, T., Grobler, H., Olubambi, P. A., Onifade, M., & Khandelwal, M. (2025). A simple kriging technique for characterising geotechnical zones of a Zimbabwean Great Dyke deposit. In ISRM International Symposium, Eurock 2025, June 16-20, 2025, Trondheim, Norway. International Society for Rock Mechanics and Rock Engineering.
  10. Zvarivadza, T., Grobler, H., Onifade, M., & Khandelwal, M. (2025). Geological and geotechnical challenges on the Great Dyke of Zimbabwe and their impact on hardrock pillar design. Deep Underground Science and Engineering.
  11. Qi, H., Zhou, J., Peng, K., & Khandelwal, M. (2025). Knowledge structure and research progress in earthquake-induced liquefaction assessment from 2000 to 2023: A scientometric analysis incorporating domain knowledge. Soil Dynamics and Earthquake Engineering, 188, 109075.
  12. Kundu, S. K., Dey, A. K., Sapkota, S. C., Debnath, P., Saha, P., Ray, A., & Khandelwal, M. (2024). Advanced predictive modelling of electrical resistivity for geotechnical and geo-environmental applications using machine learning techniques. Journal of Applied Geophysics, 231, 105557.
  13. Zhou, J., Qi, H., Peng, K., Zhang, Y., & Khandelwal, M. (2024). Comprehensive review and future perspectives on prediction and mitigation of tunnel-induced ground settlement: a bibliometric analysis and methodological overview (2002–2022). Tunnelling and Underground Space Technology, 154, 106081.
  14. Du, K., Bi, R., Khandelwal, M., Li, G., & Zhou, J. (2024). Occurrence mechanism and prevention technology of rockburst, coal bump and mine earthquake in deep mining. Geomechanics and Geophysics for Geo-Energy and Geo-Resources, 10(1), 98.
  15. Onifade, M., Lawal, A. I., Bada, S. O., & Khandelwal, M. (2024). Predictive modelling for coal abrasive index: Unveiling influential factors through Shallow and Deep Neural Networks. Fuel, 374, 132319.


Profile Details

WoS ResearcherID: B-8219-2009

https://orcid.org/0000-0003-0368-3188

https://federation.edu.au/institutes-and-schools/iiss/staff-profiles/staff-profiles/khandelwal,-manoj-dr

https://scholar.google.com/citations?user=Z4fhJrAAAAAJ&hl=en

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