Knowledge Based Prediction of Standard Penetration Resistance of Soil Using Geotechnical Database
Vol. 7 (2021): Special Issue "Innovative Strategies in Civil Engineering Grand Challenges"
Special Issue "Innovative Strategies in Civil Engineering Grand Challenges"
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Doi: 10.28991/CEJ-SP2021-07-01
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Arshid, M. U. (2021). Knowledge Based Prediction of Standard Penetration Resistance of Soil Using Geotechnical Database. Civil Engineering Journal, 7, 1–12. https://doi.org/10.28991/CEJ-SP2021-07-01
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[1] Davidson, J. L., Maultsby, J. P., & Spoor, K. B. (1999). Standard Penetration Test Energy Calibrations. Final Report and Appendices. Available online: https://trid.trb.org/view/497242 (accessed on January 2021).
[2] Aggour, M. S., & Radding, W. R. (2001). Standard penetration test (SPT) correction. Report No. MD02-007B48, Maryland State Highway Administration, Baltimore, USA.
[3] Durgunoğlu, H. T., & Toğrol, E. (1974). Penetration testing in Turkey: State-of-the-art report. In Proceedings of the European Symposium on Penetration Testing (137 Page), Stockholm, Sweden.
[4] Horn, H. M. (1979). North American Experience in Sampling and Laboratory Dynamic Testing. In Geotechnical Testing Journal (Vol. 2, Issue 2, pp. 84–97). doi:10.1520/gtj10434j.
[5] Mori, H. (1979). Review of Japanese Subsurface Investigation Techniques. In Geotechnical Engineering, 10(2), 1-25.
[6] Ulugergerli, E. U., & Uyanik, O. (2007). Statistical correlations between seismic wave velocities and SPT blow counts and the relative density of soils. Journal of Testing and Evaluation, 35(2), 187–191. doi:10.1520/jte100159.
[7] Arshid, M. U., & Kamal, M. A. (2020). Regional geotechnical mapping employing kriging on electronic geodatabase. Applied Sciences (Switzerland), 10(21), 1–15. doi:10.3390/app10217625.
[8] Kim, H. J., Dinoy, P. R. T., Choi, H. S., Lee, K. B., & Mission, J. L. C. (2019). Spatial interpolation of SPT data and prediction of consolidation of clay by ANN method. Coupled Systems Mechanics, 8(6), 523–535. doi:10.12989/csm.2019.8.6.523.
[9] Narimani, S., Chakeri, H., & Davarpanah, S. M. (2018). Simple and non-linear regression techniques used in sandy-clayey soils to predict the pressuremeter modulus and limit pressure: A case study of Tabriz subway. In Periodica Polytechnica Civil Engineering 62(3), 825–839. doi:10.3311/PPci.12063.
[10] Sil, A., & Sitharam, T. G. (2014). Dynamic Site Characterization and Correlation of Shear Wave Velocity with Standard Penetration Test' N' Values for the City of Agartala, Tripura State, India. Pure and Applied Geophysics, 171(8), 1859–1876. doi:10.1007/s00024-013-0754-y.
[11] Behpoor, L., & Ghahramani, A. (1990). Correlation of SPT to strength and modulus of elasticity of cohesive soils. Proc. 12th International Conference on Soil Mechanics and Foundation Engineering, Rio de Janeiro, 1989. Vol. 1, 175–178. doi:10.1016/0148-9062(91)93492-o.
[12] Arshid, M. U., & Kamal, M. A. (2020). Appraisal of bearing capacity and modulus of subgrade reaction of refilled soils. Civil Engineering Journal (Iran), 6(11), 2120–2130. doi:10.28991/cej-2020-03091606.
[13] Wrzesiński, G., Sulewska, M. J., & Lechowicz, Z. (2018). Evaluation of the change in undrained shear strength in cohesive soils due to principal stress rotation using an artificial neural network. In Applied Sciences (Switzerland) 8(5), 781-794. doi:10.3390/app8050781.
[14] Lee, H., & Oh, J. (2018). Establishing an ANN-based risk model for ground subsidence along railways. Applied Sciences (Switzerland), 8(10). doi:10.3390/app8101936.
[15] Baziar, M. H., Saeedi Azizkandi, A., & Kashkooli, A. (2015). Prediction of pile settlement based on cone penetration test results: An ANN approach. In KSCE Journal of Civil Engineering, 19(1), 98–106. doi:10.1007/s12205-012-0628-3.
[16] Kurup, P. U., & Griffin, E. P. (2006). Prediction of Soil Composition from CPT Data Using General Regression Neural Network. Journal of Computing in Civil Engineering, 20(4), 281–289. doi:10.1061/(asce)0887-3801(2006)20:4(281).
[17] Adeli, H. (2001). Neural networks in civil engineering: 1989-2000. Computer-Aided Civil and Infrastructure Engineering, 16(2), 126–142. doi:10.1111/0885-9507.00219.
[18] Penumadu, D., & Zhao, R. (1999). Triaxial compression behavior of sand and gravel using artificial neural networks (ANN). Computers and Geotechnics, 24(3), 207–230. doi:10.1016/S0266-352X(99)00002-6.
[19] Penumadu, D., & Zhao, R. (2000). Modeling drained triaxial compression behavior of sand using ANN. In Proceedings of Sessions of Geo-Denver 2000 - Numerical Methods in Geotechnical Engineering, GSP 96 (Vol. 284, pp. 71–87). doi:10.1061/40502(284)6.
[20] Grimaldi, M., Visintainer, R., & Jurman, G. (2011). Regnann: Reverse engineering gene networks using artificial neural networks. In PLoS ONE (Vol. 6, Issue 12, p. 28646). doi:10.1371/journal.pone.0028646.
[21] Ayoubi, S., Pilehvar, A., Mokhtari, P., & L., K. (2011). Application of Artificial Neural Network (ANN) to Predict Soil Organic Matter Using Remote Sensing Data in Two Ecosystems. Biomass and Remote Sensing of Biomass, 181–196. doi:10.5772/18956.
[22] Park, H. (2011). Study for Application of Artificial Neural Networks in Geotechnical Problems. In Artificial Neural Networks - Application. Artificial Neural Networks-Application. doi:10.5772/15011.
[23] Arshid, M. U., Shabbir, F., Hussain, J., Ahmed, A., & Tahir, I. (2013). Assessment of variation in soil parameters, for design of lightly loaded structural foundations. Life Science Journal, 12(SPL.ISSUE), 217–220.
[24] Š½lender, B., & JeluŠ¡iÄ, P. (2016). Predicting Geotechnical Investigation Using the Knowledge Based System. Advances in Fuzzy Systems, 2016, 1–10. doi:10.1155/2016/4867498.
[25] Zhang, G., Fu, P., & Liang, F. (2013). Mathematical and numerical modeling in geotechnical engineering. Journal of Applied Mathematics, 2013. doi:10.1155/2013/123485.
[26] Asmawisham Alel, M. N., Anak Upom, M. R., Abdullah, R. A., & Zainal Abidin, M. H. (2018). Estimating SPT-N Value Based on Soil Resistivity using Hybrid ANN-PSO Algorithm. Journal of Physics: Conference Series, 995(1). doi:10.1088/1742-6596/995/1/012035.
[27] Ramasamy, M., Hannan, M. A., Ahmed, Y. A., & Dev, A. K. (2021). Ann-based decision making in station keeping for geotechnical drilling vessel. Journal of Marine Science and Engineering, 9(6), 596. doi:10.3390/jmse9060596.
[28] Sarkar, G., Siddiqua, S., Banik, R., & Rokonuzzaman, M. (2015). Prediction of soil type and standard penetration test (SPT) value in Khulna City, Bangladesh using general regression neural network. Quarterly Journal of Engineering Geology and Hydrogeology, 48(3–4), 190–203. doi:10.1144/qjegh2014-108.
[29] Fernando, H., Nugroho, S. A., Suryanita, R., & Kikumoto, M. (2021). Prediction of SPT value based on CPT data and soil properties using ANN with and without normalization. International Journal of Artificial Intelligence Research, 5(2), 123–131. doi:10.29099/ijair.v5i2.208.
[30] Ateş, A., Keskin, I., Totiç, E., & Yeşil, B. (2014). Investigation of soil liquefaction potential around efteni lake in Duzce Turkey: Using empirical relationships between shear wave velocity and SPT blow count (N). Advances in Materials Science and Engineering, 2014. doi:10.1155/2014/290858.
[31] Hassoun, M. H., Watta, P. B., & Shringarpure, R. (1995). Cross-validation without a validation set in BP-trained neural nets. IEEE International Conference on Neural Networks - Conference Proceedings, 1, 369–372. doi:10.1109/icnn.1995.488127.
[32] Kröse, B., Krose, B., Smagt, P. van der, & Smagt, P. (1993). Buch - An introduction to neural networks. The University of Amsterdam, Netherlands.
[33] Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536. doi:10.1038/323533a0.
[34] Beale, M. H., Hagan, M. T., & Demuth, H. B. (2012). Neural network toolboxâ„¢ user's guide, R2012a, the MathWorks, Inc., 3 Apple Hill Drive Natick, MA 01760"’2098.
[35] Wilson, D. R., & Martinez, T. R. (2003). The general inefficiency of batch training for gradient descent learning. Neural Networks, 16(10), 1429–1451. doi:10.1016/S0893-6080(03)00138-2.
[36] LeCun, Y., Bottou, L., Orr, G. B., & Müller, K.-R. (1998). Efficient BackProp. In Neural networks: Tricks of the trade (pp. 9–50). Springer. doi:10.1007/3-540-49430-8_2.
[37] Simard, P. Y., LeCun, Y. A., Denker, J. S., & Victorri, B. (1998). Transformation invariance in pattern recognition-tangent distance and tangent propagation. In Neural networks: tricks of the trade (pp. 239-274). Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49430-8_13.
[38] Warwick, P. D., & Wardlaw, B. R. (Eds.). (2007). Regional studies of the Potwar plateau area, northern Pakistan (Vol. 2078). US Department of the Interior, US Geological Survey.
[39] Elahi, M. K., & Martin, N. R. (1961). The physiography of the Potwar of West Pakistan. Geological Bulletin of the Punjab University, 1, 5–11, Pakistan.
[40] Warwick, P. D., & Wardlaw, B. R. (1992). Paleocene–Eocene stratigraphy in northern Pakistan: Depositional and structural implications. In Programme and Abstracts, Seventh Himalaya-Karakoram-Tibet Workshop Oxford, United Kingdom, Department of Earth Sciences, Oxford University (pp. 97-98).
[41] ASTM, (2008). Standard test method for standard penetration test (SPT) and split-barrel sampling of soils. American Society of Testing and Materials, West Conshohocken Pennsylvania, USA.
[42] ASTM, D4318-10 (2010). Standard test methods for liquid limit, plastic limit, and plasticity index of soils. American Society of Testing and Materials, West Conshohocken Pennsylvania, USA.
[43] ASTM, D 6913-04. (2009). Standard test methods for particle-size distribution (gradation) of soils using sieve analysis. American Society of Testing and Materials, West Conshohocken Pennsylvania, USA.
[2] Aggour, M. S., & Radding, W. R. (2001). Standard penetration test (SPT) correction. Report No. MD02-007B48, Maryland State Highway Administration, Baltimore, USA.
[3] Durgunoğlu, H. T., & Toğrol, E. (1974). Penetration testing in Turkey: State-of-the-art report. In Proceedings of the European Symposium on Penetration Testing (137 Page), Stockholm, Sweden.
[4] Horn, H. M. (1979). North American Experience in Sampling and Laboratory Dynamic Testing. In Geotechnical Testing Journal (Vol. 2, Issue 2, pp. 84–97). doi:10.1520/gtj10434j.
[5] Mori, H. (1979). Review of Japanese Subsurface Investigation Techniques. In Geotechnical Engineering, 10(2), 1-25.
[6] Ulugergerli, E. U., & Uyanik, O. (2007). Statistical correlations between seismic wave velocities and SPT blow counts and the relative density of soils. Journal of Testing and Evaluation, 35(2), 187–191. doi:10.1520/jte100159.
[7] Arshid, M. U., & Kamal, M. A. (2020). Regional geotechnical mapping employing kriging on electronic geodatabase. Applied Sciences (Switzerland), 10(21), 1–15. doi:10.3390/app10217625.
[8] Kim, H. J., Dinoy, P. R. T., Choi, H. S., Lee, K. B., & Mission, J. L. C. (2019). Spatial interpolation of SPT data and prediction of consolidation of clay by ANN method. Coupled Systems Mechanics, 8(6), 523–535. doi:10.12989/csm.2019.8.6.523.
[9] Narimani, S., Chakeri, H., & Davarpanah, S. M. (2018). Simple and non-linear regression techniques used in sandy-clayey soils to predict the pressuremeter modulus and limit pressure: A case study of Tabriz subway. In Periodica Polytechnica Civil Engineering 62(3), 825–839. doi:10.3311/PPci.12063.
[10] Sil, A., & Sitharam, T. G. (2014). Dynamic Site Characterization and Correlation of Shear Wave Velocity with Standard Penetration Test' N' Values for the City of Agartala, Tripura State, India. Pure and Applied Geophysics, 171(8), 1859–1876. doi:10.1007/s00024-013-0754-y.
[11] Behpoor, L., & Ghahramani, A. (1990). Correlation of SPT to strength and modulus of elasticity of cohesive soils. Proc. 12th International Conference on Soil Mechanics and Foundation Engineering, Rio de Janeiro, 1989. Vol. 1, 175–178. doi:10.1016/0148-9062(91)93492-o.
[12] Arshid, M. U., & Kamal, M. A. (2020). Appraisal of bearing capacity and modulus of subgrade reaction of refilled soils. Civil Engineering Journal (Iran), 6(11), 2120–2130. doi:10.28991/cej-2020-03091606.
[13] Wrzesiński, G., Sulewska, M. J., & Lechowicz, Z. (2018). Evaluation of the change in undrained shear strength in cohesive soils due to principal stress rotation using an artificial neural network. In Applied Sciences (Switzerland) 8(5), 781-794. doi:10.3390/app8050781.
[14] Lee, H., & Oh, J. (2018). Establishing an ANN-based risk model for ground subsidence along railways. Applied Sciences (Switzerland), 8(10). doi:10.3390/app8101936.
[15] Baziar, M. H., Saeedi Azizkandi, A., & Kashkooli, A. (2015). Prediction of pile settlement based on cone penetration test results: An ANN approach. In KSCE Journal of Civil Engineering, 19(1), 98–106. doi:10.1007/s12205-012-0628-3.
[16] Kurup, P. U., & Griffin, E. P. (2006). Prediction of Soil Composition from CPT Data Using General Regression Neural Network. Journal of Computing in Civil Engineering, 20(4), 281–289. doi:10.1061/(asce)0887-3801(2006)20:4(281).
[17] Adeli, H. (2001). Neural networks in civil engineering: 1989-2000. Computer-Aided Civil and Infrastructure Engineering, 16(2), 126–142. doi:10.1111/0885-9507.00219.
[18] Penumadu, D., & Zhao, R. (1999). Triaxial compression behavior of sand and gravel using artificial neural networks (ANN). Computers and Geotechnics, 24(3), 207–230. doi:10.1016/S0266-352X(99)00002-6.
[19] Penumadu, D., & Zhao, R. (2000). Modeling drained triaxial compression behavior of sand using ANN. In Proceedings of Sessions of Geo-Denver 2000 - Numerical Methods in Geotechnical Engineering, GSP 96 (Vol. 284, pp. 71–87). doi:10.1061/40502(284)6.
[20] Grimaldi, M., Visintainer, R., & Jurman, G. (2011). Regnann: Reverse engineering gene networks using artificial neural networks. In PLoS ONE (Vol. 6, Issue 12, p. 28646). doi:10.1371/journal.pone.0028646.
[21] Ayoubi, S., Pilehvar, A., Mokhtari, P., & L., K. (2011). Application of Artificial Neural Network (ANN) to Predict Soil Organic Matter Using Remote Sensing Data in Two Ecosystems. Biomass and Remote Sensing of Biomass, 181–196. doi:10.5772/18956.
[22] Park, H. (2011). Study for Application of Artificial Neural Networks in Geotechnical Problems. In Artificial Neural Networks - Application. Artificial Neural Networks-Application. doi:10.5772/15011.
[23] Arshid, M. U., Shabbir, F., Hussain, J., Ahmed, A., & Tahir, I. (2013). Assessment of variation in soil parameters, for design of lightly loaded structural foundations. Life Science Journal, 12(SPL.ISSUE), 217–220.
[24] Š½lender, B., & JeluŠ¡iÄ, P. (2016). Predicting Geotechnical Investigation Using the Knowledge Based System. Advances in Fuzzy Systems, 2016, 1–10. doi:10.1155/2016/4867498.
[25] Zhang, G., Fu, P., & Liang, F. (2013). Mathematical and numerical modeling in geotechnical engineering. Journal of Applied Mathematics, 2013. doi:10.1155/2013/123485.
[26] Asmawisham Alel, M. N., Anak Upom, M. R., Abdullah, R. A., & Zainal Abidin, M. H. (2018). Estimating SPT-N Value Based on Soil Resistivity using Hybrid ANN-PSO Algorithm. Journal of Physics: Conference Series, 995(1). doi:10.1088/1742-6596/995/1/012035.
[27] Ramasamy, M., Hannan, M. A., Ahmed, Y. A., & Dev, A. K. (2021). Ann-based decision making in station keeping for geotechnical drilling vessel. Journal of Marine Science and Engineering, 9(6), 596. doi:10.3390/jmse9060596.
[28] Sarkar, G., Siddiqua, S., Banik, R., & Rokonuzzaman, M. (2015). Prediction of soil type and standard penetration test (SPT) value in Khulna City, Bangladesh using general regression neural network. Quarterly Journal of Engineering Geology and Hydrogeology, 48(3–4), 190–203. doi:10.1144/qjegh2014-108.
[29] Fernando, H., Nugroho, S. A., Suryanita, R., & Kikumoto, M. (2021). Prediction of SPT value based on CPT data and soil properties using ANN with and without normalization. International Journal of Artificial Intelligence Research, 5(2), 123–131. doi:10.29099/ijair.v5i2.208.
[30] Ateş, A., Keskin, I., Totiç, E., & Yeşil, B. (2014). Investigation of soil liquefaction potential around efteni lake in Duzce Turkey: Using empirical relationships between shear wave velocity and SPT blow count (N). Advances in Materials Science and Engineering, 2014. doi:10.1155/2014/290858.
[31] Hassoun, M. H., Watta, P. B., & Shringarpure, R. (1995). Cross-validation without a validation set in BP-trained neural nets. IEEE International Conference on Neural Networks - Conference Proceedings, 1, 369–372. doi:10.1109/icnn.1995.488127.
[32] Kröse, B., Krose, B., Smagt, P. van der, & Smagt, P. (1993). Buch - An introduction to neural networks. The University of Amsterdam, Netherlands.
[33] Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536. doi:10.1038/323533a0.
[34] Beale, M. H., Hagan, M. T., & Demuth, H. B. (2012). Neural network toolboxâ„¢ user's guide, R2012a, the MathWorks, Inc., 3 Apple Hill Drive Natick, MA 01760"’2098.
[35] Wilson, D. R., & Martinez, T. R. (2003). The general inefficiency of batch training for gradient descent learning. Neural Networks, 16(10), 1429–1451. doi:10.1016/S0893-6080(03)00138-2.
[36] LeCun, Y., Bottou, L., Orr, G. B., & Müller, K.-R. (1998). Efficient BackProp. In Neural networks: Tricks of the trade (pp. 9–50). Springer. doi:10.1007/3-540-49430-8_2.
[37] Simard, P. Y., LeCun, Y. A., Denker, J. S., & Victorri, B. (1998). Transformation invariance in pattern recognition-tangent distance and tangent propagation. In Neural networks: tricks of the trade (pp. 239-274). Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-49430-8_13.
[38] Warwick, P. D., & Wardlaw, B. R. (Eds.). (2007). Regional studies of the Potwar plateau area, northern Pakistan (Vol. 2078). US Department of the Interior, US Geological Survey.
[39] Elahi, M. K., & Martin, N. R. (1961). The physiography of the Potwar of West Pakistan. Geological Bulletin of the Punjab University, 1, 5–11, Pakistan.
[40] Warwick, P. D., & Wardlaw, B. R. (1992). Paleocene–Eocene stratigraphy in northern Pakistan: Depositional and structural implications. In Programme and Abstracts, Seventh Himalaya-Karakoram-Tibet Workshop Oxford, United Kingdom, Department of Earth Sciences, Oxford University (pp. 97-98).
[41] ASTM, (2008). Standard test method for standard penetration test (SPT) and split-barrel sampling of soils. American Society of Testing and Materials, West Conshohocken Pennsylvania, USA.
[42] ASTM, D4318-10 (2010). Standard test methods for liquid limit, plastic limit, and plasticity index of soils. American Society of Testing and Materials, West Conshohocken Pennsylvania, USA.
[43] ASTM, D 6913-04. (2009). Standard test methods for particle-size distribution (gradation) of soils using sieve analysis. American Society of Testing and Materials, West Conshohocken Pennsylvania, USA.
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