Education and Academic Background

• PhD in Civil Engineering (Geotechnical Engineering), National Institute of Technology Patna, 2020 to 2023

• MTech in Civil Engineering (Geotechnical Engineering), National Institute of Technology Patna, 2018 to 2020

• B.Tech in Civil Engineering, Nalanda College of Engineering Chandi nalanda (Honors with highest GPA), 2013 to 2017

Professional Experience 

• 1 June 2025 to Present --- Postdoc Research Scholar, Chulalongkorn University, Phayathai Road, Pathumwan, Bangkok 10330 Thailand

• 1 April 2024 to 31 March 2024  --- Postdoc Research Scholar, Research Unit in Data Science and Digital Transformation, Department of Civil Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathum Thani, Thailand 

• December 2023 to March 2024 --- Visiting Research Student, Department of Civil Engineering, Thammasat School of Engineering, Thammasat University

Research

• Geotechnical Stability Analysis

• Earthquake engineering

• Soil liquefaction

• Slope stability

• Reliability analysis

• Probabilistic analysis

• Soft Computing in Civil Engineering

• Machine learning and optimization algorithm

• Finite Element Limit Analysis

• Optimization in Civil Engineering

Publications

International Journal 

1. Kumar, S., Sunkpho, J., Kumar, D. R., & Wipulanusat, W. (2026). Slope stability analysis of Mount St. Helens slope using Scoops3D and machine learning. Transportation Geotechnics, 102212.

2. Sunkpho, J., Kumar, D. R., Kumar, M., & Wipulanusat, W. (2026). An Integrated Ensemble Gradient Boosting and Nature-Inspired Metaheuristic Framework for Swelling Pressure Prediction in Expansive Soils. Transportation Infrastructure Geotechnology, 13(5), 176.

3. Yousuf, S. M., Ahmad, F., Gulzar, S. M., Kumar, D. R., & Wipulanusat, W. (2026). Performance Analysis of Lime-Treated Geogrid-Reinforced Silty Sand for Rectangular Footings Using Experimental and Computational Approaches. Transportation Infrastructure Geotechnology, 13(5), 140.

4. Rao, B., Kumar, D. R., Kumar, S., Wipulanusat, W., Burman, A., & Roy, L. B. (2026). 3D reliability assessment of railway embankment under pore-water pressure and seismic loading using supervised machine learning models. Modeling Earth Systems and Environment, 12(3), 178. 

5. Kumar, S., Kumar, D. R., Singh, R., Maurya, N. S., & Wipulanusat, W. (2026). Integrated assessment of corrosion behaviour in drinking water distribution systems using experimental analysis, machine learning, and a unified corrosion–scaling index (Vol. 55, No. 3, p. e70201).

6. Saensuk, O., Kumar, D. R., Suksiripattanapong, C., & Nuaklong, P. (2026). Hybrid Least Square Support Vector Machine Model for Predicting Flexural Performance of Heavyweight Concrete made with Metakaolin and Granite Industry Waste. Engineered Science.

7. Sah, P. K., Kumar, D. R., Kumar, S. S., Devarangadi, M., & Wipulanusat, W. (2026). Experimental evaluation-based machine learning approach to estimate the volumetric heat capacity of bentonite-based soil mixtures. International Communications in Heat and Mass Transfer, 176, 111314.

8. Thamsatitdej, P., Kumar, D. R., & Wipulanusat, W. (2026). Compressive strength prediction of metakaolin mortar using CATBoost enhanced with genetic algorithm and particle swarm optimization. Asian Journal of Civil Engineering, 1-22.

9. Kumar, A., Bag, R., Nayak, B. P., & Kumar, D. R. (2026). Thermal performance evaluation of geothermal energy pile using deep learning-based approaches–A comparative study. Geothermics, 140, 103709.

10. Phulsawat, B., Senjuntichai, T., Senjuntichai, A., Kumar, D. R., & Keawsawasvong, S. (2026). Predicting vertical vibrations of circular foundation on transversely isotropic saturated soils using machine learning models. Innovative Infrastructure Solutions, 11(5), 252.

11. Sharma, D., Shivam, H., Kumar, S., Kumar, D. R., & Wipulanusat, W. (2026). Optimization of the compost C/N ratio in garden waste in-vessel composting using a machine learning. Chemical Papers, 1-17.

12. Kumar, S., Kumar, D. R., Quaff, A. R., & Wipulanusat, W. (2026). Optimizing COD removal using machine learning in a UASB-Clariflocculator wastewater treatment system. Environment, Development and Sustainability, 1-31.

13. Ahmad, F., Kumar, D. R., Wipulanusat, W., Samui, P., & Mishra, S. S. (2026). Enhancing Slope Stability Assessment Under Seismic Conditions: A Comparative Analysis of Monte Carlo Simulation, Subset Simulation, and a Second-order Reliability Method for Probabilistic Analysis. Transportation Infrastructure Geotechnology, 13(3), 47.

14. Saensuk, O., Kumar, D. R., Nuaklong, P., Jongvivatsakul, P., Tangaramvong, S., Pansuk, W., & Likitlersuang, S. (2026). Mechanical properties of heavyweight concrete made from metakaolin and granite industry waste using ANN-based model. Results in Engineering, 110057.

15. Bharat, A., Kumar, R., Kumar, D. R., Wipulanusat, W., & Kumar, S. (2026). Effect of nano silica dosage on the performance and durability of high-performance concrete using experimental and AI-based modeling approaches. Journal of Structural Integrity and Maintenance, 11(2), 2624145.

16. Kumar, D. R., Wipulanusat, W., Thangavel, P., & Boonyong, C. (2026). Integrating machine learning for efficient and safe tunnel design: an analysis of stability load coefficients in circular tunnels. Modeling Earth Systems and Environment, 12(2), 82.

17. Kumar, R., Kumar, D. R., Kumari, S., & Wipulanusat, W. (2026). Development of an affordable and eco-friendly composite liner for municipal solid waste landfills using locally available materials and industrial waste: A hybrid experimental–machine learning study. ENGINEERING Structure and Civil Engineering, 1-27.

18. Kumar, D. R., Kumar, S., Senjuntichai, T., & Kaewunruen, S. (2026). Hybrid explainable machine learning models for predicting rapid chloride penetration test and sorptivity of self-compacting concrete with fly ash and silica fume under thermal exposure. ENGINEERING Structure and Civil Engineering, 1-31.

19. Singh, C. K., Kumar, D. R., Lini Dev, K., & Wipulanusat, W. (2025). Predicting the UCS of Industrial Byproduct-Based CLSM Using Machine Learning and Experiments. Civil Engineering Journal, 11(11), 4488–4512. https://doi.org/10.28991/CEJ-2025-011-11-04

20. Kumar, D. R., Kannari, L. D., Senjuntichai, T., & Kaewunruen, S. (2025). Optimization of Pond-Ash-Based Controlled Low-Strength Materials with Lime and Superplasticizer via Experiments and Supervised Machine Learning. Results in Engineering, 108476.

21. Kumar, D. R., Thangavel, P., Wipulanusat, W., & Sunkpho, J. (2026). Application of Random Forest and Optimization Techniques in Forecasting the Axial Load Capacity of Lipped Channel Cold-Formed Steel Sections. Journal of Soft Computing in Civil Engineering, 10(2), e1950.

22. Sunkpho, J., Thangavel, P., Kumar, D. R., Wipulanusat, W., & Doh, J. H. (2025). Sustainable use of red mud in concrete: Assessing mechanical strength, durability, and performance through machine learning models. Green Technologies and Sustainability, 100283.

23. Devarangadi, M., Ali, S. S., Biradar, A., Kumar, D. R., & Wipulanusat, W. (2025). Predicting the hydraulic conductivity of fly ash-clay landfill liners with interpretable ensemble learning. Cleaner Materials, 100350.

24. Kumar, D. R., Kumar, M., Kumar, A., Kumar, N., Wipulanusat, W., & Keawsawasvong, S. (2026). Enhancing the Reliability-Based Design of Pile Foundations Using the Genetic Algorithm-Based XGBoost and ANFIS Approaches. Journal of Soft Computing in Civil Engineering, 10(1), e1943.

25. Sah, P.K., Kumar, D. R., Kumar, S.S., & Wipulanusat, W. (2026). Experimental and machine learning-based estimation of the thermal diffusivity of high-plastic soil. Geothermics, 134, 103489.

26. Ahmad, F., Kumar, D. R., Samui, P., & Wipulanusat, W. (2025). Modeling of slope stability for predicting classification steps and effective performance integrating supervised machine learning approaches. Modeling Earth Systems and Environment, 11(5), 1-20.

27. Khurshid, S., Kumar, S., Kumar, D. R., Quaff, A. R., Jha, R., & Wipulanusat, W. (2025). Machine learning approach for optimizing pesticide degradation in wastewater using a hybrid approach with ozone treatment and biological degradation. Green Technologies and Sustainability, 100254.

28. Kumar, S., Kumar, S., Kumar, D. R., Sharma, D., & Wipulanusat, W. (2025). Machine learning-based optimization of biogas and methane yields in UASB reactors for treating domestic wastewater. Biodegradation, 36(4), 55.

29. Kumar, D. R., & Wipulanusat, W. (2025). Advancements in predicting soil liquefaction susceptibility: a comprehensive analysis of ensemble and deep learning approaches. Scientific Reports, 15(1), 26453.

30. Kumar, D. R., Wipulanusat, W., Tran, D. T., & Keawsawasvong, S. (2025). Prediction of the limiting uniform normal pressure in deep contiguous piled walls using soft computing techniques. Advances in Engineering Software, 209, 103993.

31. Ahmad, F., Kumar, P., Kumar, D. R., Wipulanusat, W., & Samui, P. (2025). Geotechnical Evaluation of Embankment Stability in Seismic Zones Using Monte Carlo and Subset Simulations within an LRFD Framework Aided by Machine Learning. Results in Engineering, 106320.

32. Ahmad, F., Kumar, D. R., Samui, P., & Wipulanusat, W. (2025). Modeling of slope stability for predicting classification steps and effective performance integrating supervised machine learning approaches. Modeling Earth Systems and Environment, 11(5), 1-20.

33. Kumar, S., Kumar, S., Kumar, D. R., Sharma, D., & Wipulanusat, W. (2025). Machine learning-based optimization of biogas and methane yields in UASB reactors for treating domestic wastewater. Biodegradation, 36(4), 55.

34. Afriadi, I., Thongchom, C., Kumar, D. R., Keawsawasvong, S., & Wipulanusat, W. (2025). Ensemble boosting-based soft-computing models for predicting the bond strength between steel and CFRP plate. Scientific Reports, 15(1), 20459.

35. Kumar, S., Kumar, R., Sirimontree, S., Kumar, D. R., Wipulanusat, W., Keawsawasvong, S., & Thongchom, C. (2025). Smart prediction: Hybrid random forest for high-volume fly ash self-compacting concrete strength. Frontiers of Structural and Civil Engineering, 1-27.

36. Afriadi, I., Thongchom, C., Kumar, D. R., Wipulanusat, W., & Keawsawasvong, S. (2025). Boosting-based ensemble machine learning models to predict the bond behavior between concrete and fiberreinforced polymer bars. Frontiers of Structural and Civil Engineering, 1-14.

37. Thangavel, P., Palanisamy, M., Kumar, D. R., Wipulanusat, W., Sunkpho, J., & Roy, K. (2025). Investigation of cold formed steel angle compression through high throughput design FEA and machine learning. Scientific Reports, 15(1), 1-28.

38. Dev, K., Kumar, D. R., & Wipulanusat, W. (2025). Performance Evaluation of Pond Ash-Enhanced Flowable Fill for Plastic Concrete Cutoff Walls in Earthen Dams Using Advanced Machine Learning Models. Arabian Journal for Science and Engineering, 1-25.

39. Kumar, S., Weesakul, U., Kumar, D. R., Thangavel, P., Wipulanusat, W., & Sunkpho, J. (2025). A machine learning approach for corrosion rate modeling in Patna water distribution network of Bihar. Scientific Reports, 15(1), 11678.

40. Kumar, D. R., Kumar, P., Wipulanusat, W., Tran, D. T., & Keawsawasvong, S. (2025). Seismic stability of unsupported rectangular excavations in cohesive-frictional soils: FELA simulations and comparative analysis using MARS, GP, and GMDH models. Geotechnical and Geological Engineering, 43(3), 136.

41. Kumar, D. R., Kaorapapong, S., Wipulanusat, W., & Keawsawasvong, S. (2025). Mean Limiting Pressure Factors Determination in Contiguous Pile Walls using RAFELA and Nonlinear Regression Models in Spatially Random Soil. Results in Engineering, 104436.

42. Kumar, S., Kumar, D. R., Kumar, M., Wipulanusat, W., & Kaewmoracharoen, M. (2025). A novel approach to analyzing the 3D slope of Mount St. Helens via soft computing techniques. Earth Science Informatics, 18(2), 1-24.

43. Kumar, R., Kumar, D. R., Kumari, S., & Wipulanusat, W. (2025).  Assessing the significance of the particle size of Ganga sand Sone sand and bentonite mixtures for hydraulic containment liners integrated with machine learning-based UCS predictions. Constr Build Mater 465:140236. https://doi.org/10.1016/j.conbuildmat.2025.140236

44. Kumar, M., Kumar, D.R., Wipulanusat, W. et al. Deep Learning and Genetic Programming-Based Soft-Computing Prediction Models for Metakaolin Mortar. Transp. Infrastruct. Geotech. 12, 75 (2025). https://doi.org/10.1007/s40515-025-00532-9 

45. Tran, D. T., Shiau, J., Kumar, D. R., & Keawsawasvong, S. (2025). Optimization of ANN using metaheuristic algorithms for predicting failure envelope of ring foundations on anisotropic clay. Applied Ocean Research, 154, 104375. 

46. Kumar, R., Kumar, D. R., Wipulanusat, W., Thongchom, C., Samui, P., & Rai, B. (2024). Estimation of the Compressive Strength of Ultrahigh Performance Concrete using Machine Learning Models. Intelligent Systems with Applications, 200471. 

47. Chansavang, B., Kounlavong, K., Kumar, D. R., Nguyen, T. S., Wipulanusat, W., Keawsawasvong, S., & Jamsawang, P. (2024). Regression Machine Learning Models for Probabilistic Stability Assessment of Buried Pipelines in Spatially Random Clays. Arabian Journal for Science and Engineering, 1-28. 

48. Biswas, R., Kumar, M., Kumar, D. R., Samui, P., Rajak, M. K., Armaghani, D. J., & Singh, S. (2024). Application of novel deep neural network on prediction of compressive strength of fly ash based concrete. Nondestructive Testing and Evaluation, 1-31. 

49. Dev, K. L., Kumar, D. R., & Wipulanusat, W. (2024). Machine learning prediction of the unconfined compressive strength of controlled low strength material using fly ash and pond ash. Scientific Reports, 14(1), 27540. 

50. Kumar, M., Kumar, D. R., & Wipulanusat, W. (2024). Reliability-Based Design for Strip-Footing Subjected to Inclined Loading Using Hybrid LSSVM ML Models. Geotechnical and Geological Engineering, 1-21. 

51. Jitchaijaroen, W., Kumar, D. R., Keawsawasvong, S., Wipulanusat, W., & Jamsawang, P. (2024). Hybrid artificial neural network models for bearing capacity evaluation of a strip footing on sand based on Bolton failure criterion. Transportation Geotechnics, 48, 101347. 

52. Kumar, D. R., Wipulanusat, W., & Keawsawasvong, S. (2024). Application of Advanced Machine Learning Models for Uplift and Penetration Resistance in Clay-Embedded Dual Interfering Pipelines. Modeling Earth Systems and Environment, 1-25. 

53. Tran, D. T., Kumar, D. R., Keawsawasvong, S., Wipulanusat, W., & Jamsawang, P. (2024). Innovative approaches for predicting seismic stability of circular and rectangular tunnels in cohesive-frictional soils using machine learning and finite element limit analysis. Modeling Earth Systems and Environment, 10(4), 5831-5849. 

54. Kumar, D. R., Samui, P., Burman, A., Biswas, R., & Vanapalli, S. (2024). A novel approach for assessment of seismic induced liquefaction susceptibility of soil. Journal of Earth System Science, 133(3), 128. 

55. T. P., kumar, D. R., Kumar, M., Samui, P., & Armaghani, D. J. (2024). A novel approach to estimate rock deformation under uniaxial compression using a machine learning technique. Bulletin of Engineering Geology and the Environment, 83(7), 278. 

56. Sangjinda, K., Kumar, D. R., Keawsawasvong, S., Wipulanusat, W., & Jamsawang, P. (2024). Novel neural network-based metaheuristic models for the stability prediction of rectangular trapdoors in anisotropic and non-homogeneous clay. Advances in Engineering Software, 193, 103668. 

57. Kumar, M., Kumar, D. R., Khatti, J., Samui, P., & Grover, K. S. (2024). Prediction of bearing capacity of pile foundation using deep learning approaches. Frontiers of Structural and Civil Engineering, 1-17. 

58. Rabbani, A., Kumar, D. R., Fissha, Y., Bhavani, N. P., Ahirwar, S. K., Sharma, S., ... & Adachi, T. (2024). Optimization of an Artificial Neural Network Using Four Novel Metaheuristic Algorithms for the Prediction of Rock Fragmentation in Mine Blasting. Journal of The Institution of Engineers (India): Series D, 1-20. 

59. Kumar, S., Kumar, D. R., Wipulanusat, W., & Keawsawasvong, S. (2024). Development of ANN-Based Metaheuristic Models for the Study of the Durability Characteristics of High-Volume Fly Ash Self-Compacting Concrete with Silica Fume. Journal of Building Engineering, 109844. 

60. Pradeep, T., Kumar, D. R., Kumar, N., Wipulanusat, W., Keawsawasvong, S., & Sunkpho, J. (2024). Performance Evaluation and Triangle Diagram of Deep Learning Models for Embedment Depth Prediction in Cantilever Sheet Piles. Eng. Sci, 28(1082), 1082. 

61. Kumar, D. R., Wipulanusat, W., Sunkpho, J., Keawsawasvong, S., Jitchaijaroen, W., & Samui, P. (2024). Machine learning approaches for the prediction of the seismic stability of unsupported rectangular excavation. Engineered Science, 28, 1083. 

62. Tran, D. T., Onjaipurn, T., Kumar, D. R., Chim-Oye, W., Keawsawasvong, S., & Jamsawang, P. (2024). An eXtreme Gradient Boosting prediction of uplift capacity factors for 3D rectangular anchors in natural clays. Earth Science Informatics, 1-15. 

63. Kumar, M., Samui, P., Kumar, D. R., & Asteris, P. G. (2024). State-of-the-art XGBoost, RF and DNN based soft-computing models for PGPN piles. Geomechanics and Geoengineering, 1-16. 

64. Kumar, D. R., Wipulanusat, W., Kumar, M., Keawsawasvong, S., & Samui, P. (2024). Optimized neural network-based state-of-the-art soft computing models for the bearing capacity of strip footings subjected to inclined loading. Intelligent Systems with Applications, 21, 200314. 

65. Jitchaijaroen, W., Keawsawasvong, S., Wipulanusat, W., Kumar, D. R., Jamsawang, P., & Sunkpho, J. (2024). Machine learning approaches for stability prediction of rectangular tunnels in natural clays based on MLP and RBF neural networks. Intelligent Systems with Applications, 200329. 

66. Kumar, D. R., Bharti, A., Samui, P., Kurup, P., & Kumar, S. (2024). Development of fragility curve for railway embankment. International Journal of Mining and Geo-Engineering. 

67. Kumar, D. R., Samui, P., Wipulanusat, W., Keawsawasvong, S., Sangjinda, K., & Jitchaijaroen, W. (2023). Machine learning approaches for prediction of the bearing capacity of ring foundations on rock masses. Earth Science Informatics, 16(4), 4153-4168. 

68. Kumar, M., Biswas, R., Kumar, D. R., Samui, P., Kaloop, M. R., & Eldessouki, M. (2023). Soft computing-based prediction models for compressive strength of concrete. Case Studies in Construction Materials, 19, e02321. 

69. Kumar, D. R., Samui, P., Burman, A., Wipulanusat, W., & Keawsawasvong, S. (2023). Liquefaction susceptibility using machine learning based on SPT data. Intelligent Systems with Applications, 20, 200281. 

70. Kumar, D. R., Samui, P., Burman, A., & Kumar, S. (2023). Seismically induced liquefaction potential assessment by different artificial intelligence procedures. Transportation Infrastructure Geotechnology, 1-22. 

71. Kumar, R., Kumar, A., & Kumar, D. R. (2023). Buckling response of CNT based hybrid FG plates using finite element method and machine learning method. Composite Structures, 117204.

72. Kumar, D. R., Samui, P., & Burman, A. (2023). Suitability assessment of the best liquefaction analysis procedure based on SPT data. Multiscale and Multidisciplinary Modeling, Experiments and Design, 6(2), 319-329.

73. Kumar, D. R., Samui, P., Wipulanusat, W., Keawsawasvong, S., Sangjinda, K., & Jitchaijaroen, W. (2023). Soft-Computing Techniques for Predicting Seismic Bearing Capacity of Strip Footings in Slopes. Buildings, 13(6), 1371. 

74. Kumar, D. R., Samui, P., Wipulanusat, W., Keawsawasvong, S., Sangjinda, K., & Jitchaijaroen, W. (2023). Soft computing techniques for predicting penetration and uplift resistances of dual pipelines in cohesive soils. Engineered Science, 24, 897.

75. Kumar, M., Fathima, N. Z., & Kumar, D. R. (2023, March). A novel XGBoost and RF-based metaheuristic models for concrete compression strength. In International Conference on Civil Engineering Innovative Development in Engineering Advances (pp. 495-503). Singapore: Springer Nature Singapore. 

76. Kumar, D. R., Samui, P., Wipulanusat, W., Keawsawasvong, S., Sangjinda, K., & Jitchaijaroen, W. (2023). Bearing capacity of eccentrically loaded footings on rock masses using soft computing techniques. Eng Sci, 24(929), 929. 

77. Kumar, D. R., Samui, P., & Burman, A. (2022). Determination of best criteria for evaluation of liquefaction potential of soil. Transportation Infrastructure Geotechnology, 1-20. 

78. Kumar, D. R., Samui, P., & Burman, A. (2022). Prediction of probability of liquefaction using soft computing techniques. Journal of The Institution of Engineers (India): Series A, 103(4), 1195-1208. 

79. Kumar, M., Biswas, R., Kumar, D. R., Pradeep, T., & Samui, P. (2022). Metaheuristic models for the prediction of bearing capacity of pile foundation. Geomechanics and Engineering, 31(2), 129.

80. Kumar, D. R., Samui, P., & Burman, A. (2022). Prediction of probability of liquefaction using hybrid ANN with optimization techniques. Arabian Journal of Geosciences, 15(20), 1587.