1. Samui, P., D. Kim and C. Ghosh, 2018. Integrating Disaster Science and Management Global Case Studies in Mitigation and Recovery. Elsevier, Netherlands.
2. Roy, S.S., P. Samui, R. Deo and S. Ntalampiras, 2018. Big Data in Engineering Applications. Springer, Netherlands.
3. Kim, D., S.S. Roy, T. Lansivaara, R. Deo and P. Samui, 2018. Handbook of Research on Predictive Modeling and Optimization Methods in Science and Engineering. IGI Global, USA.
4. Dutta, S., P. Samui and D. Kim, 2018. Comparison of machine learning techniques to predict compressive strength of concrete. Comput. Concrete, 21: 463-470.
5. Samui, P., S.S. Roy and V.E. Balas, 2017. Handbook of Neural Computation. Elsevier, Netherlands.
6. Samui, P. and D. Kim, 2017. Minimax probability machine regression and extreme learning machine applied to compression index of marine clay. Indian J. Geo-Marine Sci., 46: 2350-2356.
7. Roy, S.S., P. Kulshrestha and P. Samui, 2017. Classifying Images of Drought-Affected Area Using Deep Belief Network, kNN and Random Forest Learning Techniques. In: Deep Learning Innovations and Their Convergence With Big Data, Karthik, S., A. Paul and N. Karthikeyan (Eds.)., IGI Global, USA., pp: 102-119.
8. Roshni, T., M.K. Sajid and P. Samui, 2017. Potential of regression models in projecting sea level variability due to climate change at Haldia Port, India. Ocean Syst. Eng. Int. J., 7: 319-328.
9. Kumar, R., P. Samui and S. Kumari, 2017. Reliability analysis of infinite slope using metamodels. Geotechnical Geol. Eng., 35: 1221-1230.
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10. Jayabalan, J., S.S. Roy, P. Samui and P. Kurup, 2017. Intelligent Models Applied to Elastic Modulus of Jointed Rock Mass. In: Handbook of Research on Trends and Digital Advances in Engineering Geology, Ceryan, N. (Rd.)., IGI Global, USA., pp: 1-30.
11. Dutta, S., R. Murthy, D. Kim and P. Samui, 2017. Prediction of compressive strength of self-compacting concrete using intelligent computational modeling. Comput. Mater. Continua, 53: 167-185.
12. Deo, R.C. and P. Samui, 2017. Forecasting evaporative loss by least-square support-vector regression and evaluation with genetic programming, Gaussian process and minimax probability machine regression: Case study of Brisbane City. J. Hydrologic Eng., Vol. 22. 10.1061/(ASCE)HE.1943-5584.0001506.
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13. Viswanathan, R. and P. Samui, 2016. Determination of rock depth using artificial intelligence techniques. Geosci. Frontiers, 7: 61-66.
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14. Subburaman, D., J. Jagan, Y. Dalkilic and P. Samui, 2016. Reliability Analysis of Slope Using MPMR, GRNN and GPR. In: Handbook of Research on Computational Simulation and Modeling in Engineering, Miranda, F. and C. Abreu (Eds.). IGI Global, USA., pp: 208-224.
15. Samui, P., S.S. Roy, P. Kurup and Y. Dalkilic, 2016. Modeling of Seismic Liquefaction Data Using Extreme Learning Machine. In: Earthquakes: Monitoring Technology, Disaster Management and Impact Assessment, Coleman, W. (Ed.)., Nova Science Publishers, UK., pp: 61-70.
16. Samui, P., S. Chakraborty and D. Kim, 2016. Modeling and Simulation Techniques in Structural Engineering. IGI Global, USA.
17. Samui, P., P. Kurup, S. Dhivya and J. Jagan, 2016. Reliability analysis of quick sand condition. Geotechnical Geol. Eng., 34: 579-584.
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18. Samui, P., J. Jagan and R. Hariharan, 2016. An alternative method for determination of liquefaction susceptibility of soil. Geotechnical Geol. Eng., 34: 735-738.
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19. Samui, P. and D. Kim, 2016. Determination of electrical resistivity of soil based on thermal resistivity using RVM and MPMR. Periodica Polytechnica Civil Eng., 60: 511-515.
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20. Roy, S., J. Jagan and P. Samui, 2016. Determination of Work Zone Capacity Using ELM, MPMR and GPR. In: Using Decision Support Systems for Transportation Planning Efficiency, Ocalir-Akunal, E.V. (Ed.)., IGI Global, USA., pp: 93-111.
21. Kumar, M., P. Samui and A.K. Naithani, 2016. Determination of stability of epimetamorphic rock slope using minimax probability machine. Geomatics Nat. Hazards Risk, 7: 186-193.
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22. Jagan, J., Y. Dalkilic and P. Samui, 2016. Utilization of SVM, LSSVM and GP for Predicting the Medical Waste Generation. In: Smart Cities as a Solution for Reducing Urban Waste and Pollution, Hua, G.B. (Ed.)., IGI Global, USA., pp: 224-251.
23. Jagan, J., P. Samui and B. Dixon, 2016. Determination of Rate of Medical Waste Generation Using RVM, MARS and MPMR. In: Handbook of Research on Waste Management Techniques for Sustainability, Akkucuk, U. (Ed.)., IGI Global, USA., pp: 1-18.
24. Jagan, J., G. Meghana and P. Samui, 2016. Determination of Stability Number of Layered Slope Using ANFIS, GPR, RVM and ELM. In: Soft Computing: Developments, Methods and Applications, Casey, A. (Ed.)., Nova Science Publishers, UK., pp: 39-68.
25. Deo, R.C., P. Samui and D. Kim, 2016. Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models. Stochastic Environ. Res. Risk Assess., 30: 1769-1784.
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26. Viswanathan, R., P. Kurup and P. Samui, 2015. Examining efficacy of metamodels in predicting ground water table. Int. J. Performability Eng., 11: 275-281.
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27. Viswanathan, R., J. Jagan, P. Samui and P. Porchelvan, 2015. Spatial variability of rock depth using simple kriging, ordinary Kriging, RVM and MPMR. Geotechnical Geol. Eng., 33: 69-78.
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28. Shah, V.S., H.R. Shah and P. Samui, 2015. Application of Meta-Models (MPMR and ELM) for Determining OMC, MDD and Soaked CBR Value of Soil. In: Advanced Research on Hybrid Intelligent Techniques and Applications, Bhattacharyya, S., P. Banerjee, D. Majumdar and P. Dutta (Eds.)., IGI Global, USA., pp: 454-482.
29. Samui, P., Y.H. Dalkilic, H. Rajadurai and J. Jagan, 2015. Minimax Probability Machine: A New Tool for Modeling Seismic Liquefaction Data. In: Handbook of Research on Swarm Intelligence in Engineering, Bhattacharyya, S. and P. Dutta (Eds.)., IGI Global, USA., pp: 182-210.
30. Samui, P., D. Kim and R. Viswanathan, 2015. Spatial variability of rock depth using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Multivariate Adaptive Regression Spline (MARS). Environ. Earth Sci., 73: 4265-4272.
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31. Samui, P., D. Kim and R. Hariharan, 2015. Determination of seismic liquefaction potential of soil based on strain energy concept. Environ. Earth Sci., 74: 5581-5585.
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32. Samui, P., D. Kim and B.G. Aiyer, 2015. Pullout capacity of small ground anchor: A least square support vector machine approach. J. Zhejiang Univ. Sci. A, 16: 295-301.
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33. Samui, P., 2015. Prediction of fracture parameters of concrete by relevance vector machine. Int. J. Eng. Res. Afr., 17: 1-7.
34. Samui, P., 2015. Handbook of Research on Advanced Computational Techniques for Simulation-Based Engineering. IGI Global, USA.
35. Samui, P. and D. Kim, 2015. Determination of the angle of shearing resistance of soils using multivariate adaptive regression spline. Marine Georesour. Geotechnol., 33: 542-545.
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36. Praseeda, E., B. John, C. Srinivasan, Y. Singh, K.S. Divyalakshmi and P. Samui, 2015. Thenmala fault system, southern India: Implication to neotectonics. J. Geol. Soc. India, 86: 391-398.
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37. Jagan, J., G. Prabhakar and P. Samui, 2015. Utilization of Classification Techniques for the Determination of Liquefaction Susceptibility of Soils. In: Advanced Research on Hybrid Intelligent Techniques and Applications, Bhattacharyya, S., P. Banerjee, D. Majumdar and P. Dutta (Eds.)., IGI Global, USA., pp: 124-160.
38. Yuvaraj, P., A.R. Murthy, N.R. Iyer, P. Samui and S.K. Sekar, 2014. Prediction of fracture characteristics of high strength and ultra high strength concrete beams based on relevance vector machine. Int. J. Damage Mech., 23: 979-1004.
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39. Yuvaraj, P., A.R. Murthy, N.R. Iyer, P. Samui and S.K. Sekar, 2014. Prediction of critical stress intensity factor for high strength and ultra high strength concrete beams using support vector regression. J. Struct. Eng., 40: 245-253.
40. Yuvaraj, P., A.R. Murthy, N.R. Iyer, S.K. Sekar and P. Samui, 2014. ANN model to predict fracture characteristics of high strength and ultra high strength concrete beams. Comput. Mater. Continua, 43: 193-213.
41. Shah, V.S., H.R. Shah, P. Samui, A.R. Murthy and P.A. Merono et al., 2014. Prediction of fracture parameters of high strength and ultra-high strength concrete beams using minimax probability machine regression and extreme learning machine. Comput. Mater. Continua, 44: 73-84.
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42. Samui, P., R. Hariharan and J. Karthikeyan, 2014. Determination of stability of slope using minimax probability machine. Georisk: Assess. Manage. Risk Eng. Syst. Geohazards, 8: 147-151.
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43. Samui, P., D. Choubisa and A. Sharda, 2014. Application of Artificial Neural Network and Genetic Programming in Civil Engineering. In: Biologically-Inspired Techniques for Knowledge Discovery and Data Mining, Alam, S., G. Dobbie, Y.S. Koh and Saeed ur Rehman (Eds.)., IGI Global, USA., pp: 204-220.
44. Samui, P., 2014. Vector machine techniques for modeling of seismic liquefaction data. Ain Shams Eng. J., 5: 355-360.
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45. Samui, P., 2014. Utilization of gaussian process regression for determination of soil electrical resistivity. Geotechnical Geol. Eng., 32: 191-195.
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46. Samui, P., 2014. Use of minimax probability machine regression for modelling of settlement of shallow foundations on cohesionless soil. Int. J. Performability Eng., 10: 325-328.
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47. Samui, P., 2014. Determination of surface and hole quality in drilling of AISI D2 cold work tool steel using MPMR, MARS and LSSVM. J. Adv. Manuf. Syst., 13: 237-246.
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48. Samui, P., 2014. Determination of Pull Out Capacity of Small Ground Anchor Using Data Mining Techniques. In: Data Mining and Analysis in Engineering Field, Bhatnagar, V. (Ed.)., IGI Global, USA., pp: 80-88.
49. Samui, P. and Y. Dalkilic, 2014. Modeling of Wind Speed Profile Using Soft Computing Techniques. In: Soft Computing Applications for Renewable Energy and Energy Efficiency, Cascales, M.D.S.G., J.M.S. Lozano, A.D.M. Arredondo and C.C. Corona (Eds.)., IGI Global, USA., pp: 252-273.
50. Samui, P. and R. Hariharan, 2014. Modeling of SPT seismic liquefaction data using minimax probability machine. Geotechnical Geol. Eng., 32: 699-703.
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51. Samui, P. and R. Hariharan, 2014. A unified classification model for prediction of seismic liquefaction potential of soil. J. Adv. Res., 6: 587-592.
52. Samui, P. and J. Karthikeyan, 2014. The use of a relevance vector machine in predicting liquefaction potential. Indian Geotechnical J., 44: 458-467.
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53. Samui, P. and H.Y. Dalkilic, 2014. GPR and RVM-Based Predictions of Surface and Hole Quality in Drilling of AISI D2 Cold Work Tool Steel. In: Handbook of Research on Artificial Intelligence Techniques and Algorithms, Vasant, P. (Ed.)., IGI Global, USA., pp: 736-762.
54. Samui, P. and D. Kim, 2014. Applicability of artificial intelligence to reservoir induced earthquakes. Acta Geophys., 62: 608-619.
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55. Samui, P. and B. Dixon, 2014. Determination of contaminatated wells to No_₃-N: A novel vulnerability assessment tool. J. Urban Environ. Eng., 8: 243-249.
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56. Parab, S., S. Srivastava, P. Samui and A.R. Murthy, 2014. Prediction of fracture parameters of high strength and ultra-high strength concrete beams using gaussian process regression and least squares support vector machine. Comput. Modeling Eng. Sci., 101: 139-158.
57. Okkan, U., Z.A. Serbes and P. Samui, 2014. Relevance vector machines approach for long-term flow prediction. Neural Comput. Applic., 25: 1393-1405.
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58. Kumar, M., B.G. Aiyer and P. Samui, 2014. Machine learning techniques applied to uniaxial compressive strength of oporto granite. Int. J. Performability Eng., 10: 189-195.
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59. Karthikeyan, J. and P. Samui, 2014. Application of statistical learning algorithms for prediction of liquefaction susceptibility of soil based on shear wave velocity. Geomatics Nat. Hazards Risk, 5: 7-25.
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60. Aiyer, B.G., D. Kim, N. Karingattikkal, P. Samui and P.R. Rao, 2014. Prediction of compressive strength of self-compacting concrete using least square support vector machine and relevance vector machine. KSCE J. Civil Eng., 18: 1753-1758.
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61. Yuvaraj, P., A.R. Murthy, N.R. Iyer, S.K. Sekar and P. Samui, 2013. Support vector regression based models to predict fracture characteristics of high strength and ultra high strength concrete beams. Eng. Fracture Mech., 98: 29-43.
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62. Samuk, P. and M. Kumar, 2013. Analysis of epimetamorphic rock slopes using soft computing. J. Shanghai Jiaotong Univ., 19: 274-278.
    Direct Link  |  
63. Samui, P., T. Lansivaara and M.R. Bhatt, 2013. Least square support vector machine applied to slope reliability analysis. Geotechnical Geol. Eng., 31: 1329-1334.
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64. Samui, P., 2013. Support vector classifier analysis of slope. Geomatics Nat. Hazards Risk, 4: 1-12.
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65. Samui, P., 2013. Multivariate adaptive regression spline (Mars) for prediction of elastic modulus of jointed rock mass. Geotechnical Geol. Eng., 31: 249-253.
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66. Samui, P., 2013. Liquefaction prediction using support vector machine model based on cone penetration data. Frontiers Struct. Civil Eng., 7: 72-82.
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67. Samui, P., 2013. Determination of compressive strength of concrete by statistical learning algorithms. Eng. J., 17: 111-120.
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68. Samui, P. and J. Karthikeyan, 2013. Determination of liquefaction susceptibility of soil: A least square support vector machine approach. Int. J. Numer. Anal. Methods Geomechan., 37: 1154-1161.
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69. Samui, P. and J. Jagan, 2013. Determination of effective stress parameter of unsaturated soils: A Gaussian process regression approach. Frontiers Struct. Civil Eng., 7: 133-136.
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70. Samui, P. and D. Kim, 2013. Least square support vector machine and multivariate adaptive regression spline for modeling lateral load capacity of piles. Neural Comput. Applic., 23: 1123-1127.
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71. Samui, P. and D. Kim, 2013. Least square support vector machine and multivariate adaptive regression spline for modeling lateral load capacity of piles. Neural Comput. Applic., 23: 1123-1127.
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72. Samui, P. and D. Kim, 2013. Determination of reservoir induced earthquake using support vector machine and gaussian process regression. Applied Geophys., 10: 229-234.
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73. Muduli, P.K., M.R. Das, P. Samui and S.K. Das, 2013. Uplift capacity of suction caisson in clay using artificial intelligence techniques. Marine Georesour. Geotechnol., 31: 375-390.
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74. Kumar, M., P. Samui and A.K. Naithani, 2013. Determination of uniaxial compressive strength and modulus of elasticity of travertine using machine learning techniques. Int. J. Adv. Soft Comput. Applic., 5: 1-10.
75. Kumar, M., M. Mittal and P. Samui, 2013. Performance assessment of genetic programming (GP) and minimax probability machine regression (MPMR) for prediction of seismic ultrasonic attenuation. Earthquake Sci., 26: 147-150.
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76. Karthikeyan, J. and P. Samui, 2013. Determination of strain energy for triggering liquefaction based on Gaussian process regression. Eng. J., 17: 71-78.
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77. Gopinath, K.G.S., S. Pal, P. Samui and B.K. Sarkar, 2013. Support vector machine and relevance vector machine for prediction of alumina and pore volume fraction in bioceramics. Int. J. Applied Ceramic Technol., 10: E240-E246.
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78. Ceryan, N., U. Okkan, P. Samui and S. Ceryan, 2013. Modeling of tensile strength of rocks materials based on support vector machines approaches. Int. J. Numerical Anal. Methods Geomechanics, 37: 2655-2670.
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79. Samui, P., T. Edison, C. Harikumar and C.S. Pillai, 2012. Site characterization of (IGCAR) Kalpakkam using soft computing technices. Int. J. Adv. Soft Comput. Applic., 4: 1-14.
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80. Samui, P., S. Bhattacharya and T.G. Sitharam, 2012. Support vector classifiers for prediction of pile foundation performance in liquefied ground during earthquakes. Int. J. Geotech. Earthquake Eng., 3: 42-59.
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81. Samui, P., P.H. Gowda, T. Oommen, T.A. Howell, T.H. Marek and D.O. Porter, 2012. Statistical learning algorithms for identifying contrasting tillage practices with Landsat Thematic Mapper data. Int. J. Remote Sens., 33: 5732-5745.
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82. Samui, P., D. Kim, S. Das and G.L. Yoon, 2012. Determination of compression index for marine clay: A relevance vector machine approach. Mar. Georesour. Geotechnol., 30: 263-273.
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83. Samui, P., 2012. Three-dimensional site characterization model of bangalore using support vector machine. ISRN Soil Sci., Vol. 2012. 10.5402/2012/346439.
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84. Samui, P., 2012. Slope Stability Analysis Using Multivariate Adaptive Regression Spline. In: Metaheuristics in Water, Geotechnical and Transport Engineering, Yang, X.S., A.H. Gandomi, S. Talatahari and A.H. Alavi (Eds.)., Elsevier, London, pp: 327-344.
85. Samui, P., 2012. Determination of ultimate capacity of driven piles in cohesionless soil: A multivariate adaptive regression spline approach. Int. J. Numer. Anal. Methods Geomech., 36: 1434-1439.
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86. Samui, P., 2012. Application of support vector machine in P-wave attenuation in sandatones. Int. J. Geotechnics Environ., 4: 1-13.
87. Samui, P., 2012. Application of statistical learning algorithms to ultimate bearing capacity of shallow foundation on cohesionless soil. Int. J. Numer. Anal. Methods Geomech., 36: 100-110.
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88. Samui, P., 2012. Application of relevance vector machine for prediction of ultimate capacity of driven piles in cohesionless soils. Geotechnical Geol. Eng., 30: 1261-1270.
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89. Samui, P., 2012. Applicability of data mining techniques for predicting electrical resistivity of soils based on thermal resistivity. Int. J. Geomechanics, 13: 692-697.
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90. Samui, P., 2012. A study of slope stability prediction using least square support vector machine. J. Applied Mech. Eng., 17: 279-287.
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91. Samui, P. and P. Kurup, 2012. Multivariate adaptive regression spline and least square support vector machine for prediction of undrained shear strength of clay. Int. J. Applied Metaheuristic Comput., 3: 33-42.
92. Samui, P. and P. Kurup, 2012. Multivariate adaptive regression spline (MARS) and least squares support vector machine (LSSVM) for OCR prediction. Soft Comput., 16: 1347-1351.
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93. Samui, P. and D.P. Kothari, 2012. Artificial Intelligence in Civil Engineering. VDM Publishing House Ltd., Germany.
94. Samui, P. and D. Kim, 2012. Utilization of support vector machine for prediction of fracture parameters of concrete. Comput. Concrete, 9: 215-226.
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95. Samui, P. and D. Kim, 2012. Modelling of reservoir-induced earthquakes: A multivariate adaptive regression spline. J. Geophys. Eng., Vol. 9. 10.1088/1742-2132/9/5/494.
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96. Samui, P. and B. Dixon, 2012. Application of support vector machine and relevance vector machine to determine evaporative losses in reservoirs. Hydrol. Processes, 26: 1361-1369.
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97. Okkan, U. and P. Samui, 2012. Modeling of watershed runoff using discrete wavelet transform and support vector machines. Fresenius Environ. Bull., 21: 3971-3986.
98. Venkatesh, S., P. Samui, D. Kim and S.K. Sekar, 2011. Application of statistical learning algorithm for determination of failure mechanism of interior beam-column joint. Int. J. Earth Sci. Eng., 4: 1111-1117.
99. Samui, P., V.R. Mandla, A. Krishna and T. Teja, 2011. Prediction of rainfall using support vector machine and relevance vector machine. Earth Sci. India, 4: 188-200.
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100. Samui, P., T. Lansivaara and D. Kim, 2011. Utilization relevance vector machine for slope reliability analysis. Applied Soft Comput., 11: 4036-4040.
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101. Samui, P., S.K. Sekar and K. Kulkarni, 2011. Machine Learning in Concrete Technology. VDM Publishing House Ltd., Germany.
102. Samui, P., S. Das and D. Kim, 2011. Uplift capacity of suction caisson in clay using multivariate adaptive regression spline. Ocean Eng., 38: 2123-2127.
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103. Samui, P., L. See, T. Chaipimonplin and P. Kneale, 2011. Advances in data-driven flood forecasting using radar data. J. Flood Eng., 2: 129-145.
104. Samui, P., D. Kim and T.G. Sitharam, 2011. Support vector machine for evaluating seismic-liquefaction potential using shear wave velocity. J. Applied Geophys., 73: 8-15.
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105. Samui, P., 2011. Utilization of statistical learning algorithms for prediction of elastic modulus of jointed rock mass. Recent Trends Civil Eng. Technol., 1: 1-7.
106. Samui, P., 2011. Utilization of relevance vector machine for rock slope stability analysis. Int. J. Geotech. Eng., 5: 351-355.
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107. Samui, P., 2011. Utilization of least square support vector machine (LSSVM) for prediction of liquefaction susceptibility of soil. Int. J. Sens. Comput. Control, 1: 111-116.
     Direct Link  |  
108. Samui, P., 2011. Prediction of pile bearing capacity using support vector machine. Int. J. Geotech. Eng., 5: 95-102.
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109. Samui, P., 2011. Multivariate adaptive regression spline applied to friction capacity of driven piles in clay. Geomech. Eng., 3: 285-290.
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110. Samui, P., 2011. Least square support vector machine applied to elastic modulus of jointed rock mass. J. Rock Mech. Tunneling Technol., 17: 5-12.
111. Samui, P., 2011. Least square support vector machine and relevance vector machine for evaluating seismic liquefaction potential using SPT. Nat. Hazards, 59: 811-822.
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112. Samui, P., 2011. Disaster Mitigation and Management: The Relevance of Artificial Intelligence. In: Rebuilding Sustainable Communities with Vulnerable Populations after the Cameras Have Gone: A Worldwide Study, Awotona, A. (Ed.)., Cambridge Scholars Publishing, UK., pp: 357-402.
113. Samui, P., 2011. Data Driven Models. VDM Publishing House Ltd., Germany.
114. Samui, P., 2011. Application of Least Square Support Vector Machine (LSSVM) for determination of evaporation losses in reservoirs. Engineering, 3: 431-434.
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115. Samui, P. and T.G. Sitharam, 2011. Machine learning modelling for predicting soil liquefaction susceptibility. Nat. Hazards Earth Syst. Sci., 11: 1-9.
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116. Samui, P. and T.G. Sitharam, 2011. Determination of liquefaction susceptability of soil based on field test and artificial intelligence. Int. J. Earth Sci. Eng., 4: 216-222.
117. Samui, P. and T.G. Sitharam, 2011. Application of geostatistical models for estimating spatial variability of rock depth. Engineering, 3: 886-894.
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118. Samui, P. and S. Das, 2011. Site characterization model using support vector machine and ordinary kriging. Int. J. Intell. Syst., 20: 261-278.
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119. Samui, P. and S. Das, 2011. Relevance vector machine for prediction of soil properties. J. Civil Eng. Res. Pract., 8: 23-33.
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120. Samui, P. and P. Kurup, 2011. Use of the relevance vector machine for prediction of an overconsolidation ratio. Int. J. Geomech., 13: 26-32.
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121. Samui, P. and J. Karthikeyan, 2011. Determination of liquefaction susceptibility of soil based on CPT: A least square support vector machine approach. Int. J. Geotech. Environ., 3: 75-84.
122. Samui, P. and D.P. Kothari, 2011. Utilization of a least square support vector machine (LSSVM) for slope stability analysis. Sci. Iranica, 18: 53-58.
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123. Samui, P. and D.P. Kothari, 2011. Application of multivariate adaptive regression splines to evaporation losses in reservoirs. Earth Sci. India, 4: 15-20.
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124. Mitra, N. and P. Samui, 2011. Prediction of inelastic mechanisms leading to seismic failure of interior reinforced concrete beam-column connections. Pract. Periodical Struct. Des. Construct., 17: 110-118.
125. Kallyan, S.K., P. Samui, D. Kim and S.K. Sekar, 2011. Model of least square support vector machine (LSSVM) for prediction of fracture parameters of concrete. Int. J. Concrete Struct. Mater., 5: 21-25.
126. Das, S.K., P. Samui, D. Kim, N. Sivakugan and R. Biswal, 2011. Lateral displacement of liquefaction induced ground using least square support vector machine. Int. J. Geotech. Earthquake Eng., 2: 29-39.
127. Das, S.K., P. Samui and A.K. Sabat, 2011. Prediction of field hydraulic conductivity of clay liners using an artificial neural network and support vector machine. Int. J. Geomech., 12: 606-611.
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128. Das, S.K., P. Samui and A.K. Sabat, 2011. Application of artificial intelligence to maximum dry density and unconfined compressive strength of cement stabilized soil. Geotech. Geol. Eng., 29: 329-342.
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129. Das, S., P. Samui, S.Z. Khan and N. Sivakugan, 2011. Machine learning techniques applied to prediction of residual strength of clay. Cent. Eur. J. Geosci., 3: 449-461.
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130. Samui, P., S. Das and T.G. Sitharam, 2010. Soft Computing in Geotechnical Engineering. VDM Publishing House Ltd., Germany.
131. Samui, P., 2010. Support vector machine for evaluating seismic liquefaction potential using standard penetration test. Disaster Adv., 3: 20-25.
132. Samui, P., 2010. Slope Stability and Liquefaction. VDM Publishing House Ltd., Germany.
133. Samui, P., 2010. Seismic liquefaction potential assessed by least square support vector machine (LSSVM). Int. J. Eng. Uncertainty: Hazards Assess. Mitigation, 2: 151-155.
134. Samui, P., 2010. Artificial Intelligence in Earthquake Engineering. LAP Lambert Academic Publishing AG & Co. KG., Germany.
135. Samui, P., 2010. Application of support vector machine for rock slope stability analysis. J. Rock Mech. Tunneling Technol., 16: 113-122.
136. Samui, P., 2010. Application of soft computing in disaster mitigation and management. Disaster Adv., 3:: 3-3.
137. Samui, P. and T.G. Sitharam, 2010. Spatial variability of rock depth using artificial intelligence techniques. Earth Sci. India, 3: 195-205.
138. Samui, P. and T.G. Sitharam, 2010. Spatial variability of SPT data using ordinary and disjunctive kriging. Georisk, 4: 22-31.
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139. Samui, P. and T.G. Sitharam, 2010. Site characterization model using least‐square support vector machine and relevance vector machine based on corrected SPT data (N_c). Int. J. Numer. Anal. Methods Geomech., 34: 755-770.
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140. Samui, P. and T.G. Sitharam, 2010. Site characterization model using artificial neural network and kriging. Int. J. Geomech., 10: 171-180.
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141. Samui, P. and T.G. Sitharam, 2010. Relevance Vector Machine for Evaluating Seismic Liquefaction Potential Using Shear Wave Velocity. In: Soil Dynamics and Earthquake Engineering, Huang, M., X. Yu and Y. Huang (Eds.). ASCE Publication, Reston, VA., ISBN: 978-0-7844-1102-5, pp: 212-217.
142. Samui, P. and T.G. Sitharam, 2010. Intelligent Models in Geotechnical Engineering. LAP Lambert Academic Publishing AG & Co. KG., Germany.
143. Samui, P. and T.G. Sitharam, 2010. Design of a piezovibrocone and calibration chamber. Geomech. Eng.: Int. J., 2: 177-190.
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144. Samui, P. and T.G. Sitharam, 2010. Correlation between SPT, CPT and MASW. Int. J. Geotech. Eng., 4: 279-288.
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145. Samui, P. and T.G. Sitharam, 2010. Applicability of statistical learning algorithms for spatial variability of rock depth. Math. Geol., 42: 433-446.
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146. Kumar, B. and P. Samui, 2010. Determination of stability numbers for soil slopes following non-associated non-coaxial flow rule. Int. J. Geotechn. Eng., 4: 89-97.
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147. Das, S.K., P. Samui, A.K. Sabat and T.G. Sitharam, 2010. Prediction of swelling pressure of soil using artificial intelligence techniques. Environ. Earth Sci., 61: 393-403.
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148. Samui, P. and T.G. Sitharam, 2009. Pullout capacity of small ground anchors: A relevance vector machine approach. Geomech. Eng.: Int. J., 1: 259-262.
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149. Samui, P. and T.G. Sitharam, 2009. Application of least squares support vector machine in seismic attenuation prediction. ISET J. Earthquake Technol., 46: 147-155.
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150. Sitharam, T.G., P. Samui and A. Panjamani, 2008. Spatial variability of rock depth in Bangalore using geostatistical, neural network and support vector machine models. Geotech. Geol. Eng., 26: 503-517.
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151. Samui, P., T.G. Sitharam and P.U. Kurup, 2008. OCR prediction using support vector machine based on piezocone data. J. Geotech. GeoEnviron. Eng., 134: 894-898.
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152. Samui, P., 2008. Support vector machine applied to settlement of shallow foundations on cohesionless soils. Comput. Goetech., 35: 419-427.
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153. Samui, P., 2008. Slope stability analysis: A support vector machine approach. Environ. Geol., 26: 255-267.
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154. Samui, P., 2008. Relevance vector machine applied to settlement of shallow foundation on cohesionless soils. Goerisk, 2: 41-47.
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155. Samui, P., 2008. Prediction of friction capacity of driven piles in clay using the support vector machine. Can. Geotech. J., 45: 288-295.
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156. Samui, P., 2008. Predicted ultimate capacity of laterally loaded piles in clay using support vector machine. Geomech. Geoeng., 3: 113-120.
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157. Samui, P. and T.G. Sitharam, 2008. Least‐square support vector machine applied to settlement of shallow foundations on cohesionless soils. Int. J. Numer. Anal. Methods Geomech., 32: 2033-2043.
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158. Kumar, J. and P. Samui, 2008. Frequency effect on liquefaction using shake table tests. J. S. Asian Geotech. Soc., 39: 169-173.
159. Sitharam, T.G. and P. Samui, 2007. Geostatistical modelling of spatial and depth variability of SPT data for Bangalore. Geomech. Geoeng., 2: 307-316.
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160. Samui, P., 2007. Seismic liquefaction potential assessment by using relevance vector machine. Earthquake Eng. Eng. Vibration, 6: 331-336.
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161. Samui, P., 2007. Application of relevance vector machine in seismic attenuation prediction. J. Earthquake Tsunami, 1: 299-309.
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162. Kumar, B. and P. Samui, 2007. Application of ANN for predicting pore water pressure response in a shake table test. Int. J. Geotech. Eng., 2: 153-160.
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163. Samui, P. and B. Kumar, 2006. Artificial neural network prediction of stability numbers for two-layered slopes with associated flow rule. Electron. J. Geotech. Eng., 11: 1-44.
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164. Kumar, J. and P. Samui, 2006. Stability determination for layered soil slopes using the upper bound limit analysis. Geotech. Geol. Eng., 24: 1803-1819.
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