Faculty details

Prof.Sudhir Kumar Singh

Designation: Assistant Professor

Department: Mining Engineering

Email: sudhir[at]iitism[dot]ac[dot]in

Contact Number: 8299264748

Personal Page: Click Here

About Me: Dr. Sudhir Kumar Singh is an Assistant Professor in the Department of Mining Engineering at IIT (ISM) Dhanbad, working at the intersection of geotechnical engineering and artificial intelligence with a focus on slope stability prediction, mine automation, PSInSAR-based deformation monitoring, digital twins, and responsible AI. He obtained his Ph.D. in Application of Machine Learning in Geotechnical Engineering and B.Tech. in Mining Engineering from IIT Kharagpur, and has led consultancy projects and student research on advanced data analytics, infrastructure monitoring, and mining applications across India.

Research Interest: Artificial Intelligence and Machine Learning for Mining and Geotechnical Applications; Internet of Things (IoT) and Sensor-based Monitoring Systems; Digital Twin Technology for Mining and Industrial Operations; Slope Stability Analysis using Data-driven and Hybrid Models; Mine Automation and Intelligent Control Systems; Digitalisation of Mining Operations and Smart Manufacturing; Deep Learning Architectures including Convolutional and Variational Autoencoders; Predictive Modeling for Geotechnical and Geomechanical Systems; Remote Sensing and PSInSAR-based Deformation Monitoring; Computational Geomechanics and Numerical Simulation; Natural Language Processing and Generative AI for Engineering; Ethics, Responsible AI, and Regulatory Frameworks in Artificial Intelligence.

Teaching

  • Mining Methods for Critical Mineral Deposits (MNE)    NMNC532
  • Geospatial Technologies for Natural Resources    NMND502
  • Advanced Surveying Practical     NMNC514

Academics

Position

Awards and Honors

Publications

List Of Research Publications (only in Peer-reviewed Journals)

  1.  S.K. Singh, D. Chakravarty. The Evolution and Future of Machine Learning in Rock Slope Stability: A Systematic Review and Meta-Analysis. Mining, Metallurgy & Exploration (2026). SCIE. https://doi.org/10.1007/s42461-026-01624-x
  2. A. Kumar, S.K. Singh, B. Samanta, et al. Risk assessment in sociotechnical systems based on functional resonance analysis method and hierarchical fuzzy inference tree. Sci Rep 15, 23827 (2025). SCIE. DOI: https://doi.org/10.1038/s41598-025-10063-5
  3. A Pandey, S.K. Singh, SJ Sridharan, et al. Predictive control of underground mine spray chambers: An integrated machine learning and IoT Approach. International Journal of Refrigeration 179, 142-155 (2025). SCIE. DOI:https://doi.org/10.1016/j.ijrefrig.2025.08.009
  4. S.K. Singh, D. Chakravarty, Predicting the Stability of Rock Slopes in the Presence of Diverse Joint Networks and External Factors Using Machine Learning Algorithms. Mining, Metallurgy Exploration. 41, 2421–2440 (2024). SCIE. DOI: 10.1007/s42461-024-01060-9
  5. S.K. Singh, D. Chakravarty (2024), Advanced Machine Learning for Slope Stability Analysis Under Non-homogeneous Conditions: A Comprehensive Mine Study. In: Gorai, A.K., Ram, S., Bishwal, R.M., Bhowmik, S. (eds) Sustainable and Innovative Mining Practices. ICSIMP 2023. Springer Proceedings in Earth and Environmental Sciences. Springer, Cham. DOI: 10.1007/978-3-031-76614-5_22
  6. S.K. Singh, D. Chakravarty, Assessment of Slope Stability using Classification and Regression Algorithms Subjected to Internal and External Factors. Archives of Mining Sciences. 68 (1), 87–102 (2023). SCIE. DOI: 10.24425/ams.2023.144319
  7. S.K. Singh, D. Chakravarty, Efficient and Reliable Prediction of Dump Slope Stability in Mines using Machine Learning: An in-depth Feature Importance Analysis. Archives of Mining Sciences. (2023). SCIE. DOI: 10.24425/ams.2023.148157
  8. S.K. Singh, D. Chakravarty (2023). Interpretable Predictions: Machine Learning Approaches to Understand Slope Stability in the Presence of Joint Networks. In: Sinha, A., Sarkar, B.C., Mandal, P.K. (eds) Proceedings of the 10th Asian Mining Congress 2023. AMC 2023. Springer Proceedings in Earth and Environmental Sciences. Springer, Cham. DOI: https://doi.org/10.1007/978-3-031-46966-4_16

 

Projects & Activities

Guidance