An intelligent approach for predicting the strength of geosynthetic-reinforced subgrade soil

References

• Aamir, M., et al., 2020. Performance analysis of multi-spindle drilling of Al2024 with TiN and TiCN coated drills using experimental and artificial neural networks technique. Applied Sciences (Switzerland), 10 (23), 1–20.
   Web of Science ®Google Scholar
• Abu-Farsakh, M., et al., 2016. Performance of geosynthetic reinforced/stabilized paved roads built over soft soil under cyclic plate loads. Geotextiles and Geomembranes, 44 (6), 845–853.
   Web of Science ®Google Scholar
• Alawi, M.H., and Rajab, M.I, 2013. Prediction of California bearing ratio of subbase layer using multiple linear regression models. Road Materials and Pavement Design, 14 (1), 211–219.
   Web of Science ®Google Scholar
• Arulrajah, A., et al., 2013. Resilient moduli response of recycled construction and demolition materials in pavement subbase applications. Journal of Materials in Civil Engineering, 25 (12), 1920–1928.
   Web of Science ®Google Scholar
• AS 1289.6.1.1, 2014. Methods of testing soils for engineering purposes Soil strength and consolidation tests – Determination of the California Bearing Ratio of a soil – Standard laboratory method for a remoulded specimen.
   Google Scholar
• ASTM, D., 2016. Standard test method for California bearing ratio (CBR) of laboratory-compacted. West Conshohocken, PA: ASTM International, 1–9.
   Google Scholar
• Bishop, Christopher, 2006. Pattern Recognition and machine learning. New York: Springer-Verlag New York.
   Google Scholar
• Breiman, L, 2001. Random forests. Machine Learning, 45 (1), 5–32.
   Web of Science ®Google Scholar
• Carlos, D.M., Pinho-Lopes, M., and Lopes, M.L, 2016. Effect of geosynthetic reinforcement inclusion on the strength parameters and bearing ratio of a fine soil. Procedia Engineering, 143, 34–41.
   Google Scholar
• Chen, Q., et al., 2018. Performance evaluation of full-scale geosynthetic reinforced flexible pavement. Geosynthetics International, 25 (1), 26–36.
   Web of Science ®Google Scholar
• Choudhary, A., et al., 2012. Improvement in CBR of the expansive soil subgrades with a single reinforcement layer. Proceedings of Indian Geotechnical Conference, 289–292.
   Google Scholar
• Choudhary, A.K., Jha, J.N., and Gill, K.S., 2011. Improvement in CBR values of expansive soil-subgrades using geosynthetics. Punjab: Guru Nanak Dev Engineering College Ludhiana, 155.
   Google Scholar
• Cleary, J.G., and Trigg, L.E, 1995. K*: an instance-based learner using an entropic distance measure. Machine Learning Proceedings, 1995, 108–114.
   Google Scholar
• Cuelho, E. V., Perkins, S.W., and Ganeshan, S.K., 2005. Determining geosynthetic material properties pertinent to reinforced pavement design. Geotechnical Special Publication, 130–142, 241–252.
   Google Scholar
• Daneshvar, D. and Behnood, A, 2020. Estimation of the dynamic modulus of asphalt concretes using random forests algorithm. International Journal of Pavement Engineering.1-11. https://doi.org/10.1080/10298436.2020.1741587
   Google Scholar
• de Souza, W.M., Ribeiro, A.J.A., and da Silva, C.A.U., 2020. Use of ANN and visual-manual classification for prediction of soil properties for paving purposes. International Journal of Pavement Engineering.1- 9. https://doi.org/10.1080/10298436.2020.1807546
   Google Scholar
• Duncan-Williams, E., and Attoh-Okine, N.O, 2008. Effect of geogrid in granular base strength – An experimental investigation. Construction and Building Materials, 22 (11), 2180–2184.
   Web of Science ®Google Scholar
• Erzin, Y., and Turkoz, D, 2016. Use of neural networks for the prediction of the CBR value of some Aegean sands. Neural Computing and Applications, 27 (5), 1415–1426.
   Web of Science ®Google Scholar
• Frank, E., Mayo, M., and Kramer, S., 2015. Alternating model trees. In: Proceedings of the ACM Symposium on Applied Computing, 871–878.
   Google Scholar
• Gandomi, A.H., Yun, G.J., and Alavi, A.H, 2013. An evolutionary approach for modeling of shear strength of RC deep beams. Materials and Structures/Materiaux et Constructions, 46 (12), 2109–2119.
   Web of Science ®Google Scholar
• Gao, W., et al., 2019. Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms. Applied Soft Computing Journal, 84. https://doi.org/10.1016/j.asoc.2019.105748
   Google Scholar
• Ghorbani, B., et al., 2020a. Experimental investigation and modelling the deformation properties of demolition wastes subjected to freeze–thaw cycles using ANN and SVR. Construction and Building Materials, 258. https://doi.org/10.1016/j.conbuildmat.2020.119688
   Google Scholar
• Ghorbani, B., et al., 2020b. Experimental and ANN analysis of temperature effects on the permanent deformation properties of demolition wastes. Transportation Geotechnics, 24. https://doi.org/10.1016/j.trgeo.2020.100365
   Google Scholar
• Ghorbani, B., et al., 2020c. Development of genetic-based models for predicting the resilient modulus of cohesive pavement subgrade soils. Soils Found, 60, 398–412. doi:10.1016/j.sandf.2020.02.010.
   Web of Science ®Google Scholar
• Ghorbani, B., et al., 2021. Shakedown analysis of PET blends with demolition waste as pavement base/subbase materials using experimental and neural network methods. Transportation Geotechnics, 27. https://doi.org/10.1016/j.trgeo.2020.100481
   Google Scholar
• Ghosh Mondal, P. and Kuna, K., 2020. An automated technique for characterising foamed bitumen using ultrasonic sensor system. International Journal of Pavement Engineering. https://doi.org/10.1080/10298436.2020.1849689
   Google Scholar
• Golbraikh, A., et al., 2003. Rational selection of training and test sets for the development of validated QSAR models. Journal of Computer-Aided Molecular Design, 17 (2–4), 241–253.
   PubMed Web of Science ®Google Scholar
• González Farias, I., Araujo, W., and Ruiz, G., 2018. Prediction of California bearing ratio from index properties of soils using parametric and non-parametric models. Geotechnical and Geological Engineering, 36 (6), 3485–3498.
   Web of Science ®Google Scholar
• Han, C., et al., 2020. Intelligent decision model of road maintenance based on improved weight random forest algorithm. International Journal of Pavement Engineering. https://doi.org/10.1080/10298436.2020.1784418
   Google Scholar
• Han, J., and Moraga, C, 1995. The influence of the sigmoid function parameters on the speed of backpropagation learning. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 930, 195–201.
   Google Scholar
• Hasanzadehshooiili, H., Lakirouhani, A., and Medzvieckas, J, 2012. Superiority of artificial neural networks over statistical methods in prediction of the optimal length of rock bolts. Journal of Civil Engineering and Management, 18 (5), 655–661.
   Web of Science ®Google Scholar
• Ho, T.K, 1995. Random decision forests. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, 278–282.
   Google Scholar
• Khorrami, R., Derakhshani, A., and Moayedi, H, 2020. New explicit formulation for shallow foundations’ ultimate bearing capacity rested on granular soil using M-5’ model tree. Measurement, 108032.
   Google Scholar
• Kuity, A., and Roy, T.K, 2013. Utilization of geogrid mesh for improving the soft subgrade layer with waste material mix compositions. Procedia – Social and Behavioral Sciences, 104, 255–263.
   Google Scholar
• Massart, D.L., et al., 1986. Least median of squares: a robust method for outlier and model error detection in regression and calibration. Analytica Chimica Acta, 187 (C), 171–179.
   Google Scholar
• Mittal, A., and Shukla, S, 2018. Influence of geotextile and geogrid reinforcement on strength behaviour of soft silty soil. Applied Mechanics and Materials, 877, 264–269.
   Google Scholar
• Mittal, A., and Shukla, S, 2019. Strength improvement of poor subgrade soil reinforced with polyester biaxial geogrid. Jordan Journal of Civil Engineering, 13 (2), 214–225.
   Web of Science ®Google Scholar
• Miura, N., et al., 1990. Polymer grid reinforced pavement on soft clay grounds. Geotextiles and Geomembranes, 9 (1), 99–123.
   Web of Science ®Google Scholar
• Moayedi, H., et al., 2020. Feature validity during machine learning paradigms for predicting biodiesel purity. Fuel, 262. https://doi.org/10.1016/j.fuel.2019.116498
   Google Scholar
• Moayedi, H., and Hayati, S, 2018. Applicability of a CPT-based neural network solution in predicting load-settlement responses of bored pile. International Journal of Geomechanics, 18 (6), 06018009.
   Web of Science ®Google Scholar
• Naeini, S.A., and Ziaie-Moayed, R, 2009. Effect of plasticity index and reinforcement on the CBR value of soft clay. International Journal of Civil Engineering, 7 (2), 124–130.
   Web of Science ®Google Scholar
• Nagaraju, T.V., Prasad, C.D., and Raju, M.J, 2020. Prediction of California bearing ratio using particle swarm optimization. Advances in Intelligent Systems and Computing, 1048, 795–803.
   Google Scholar
• Nair, A.M., and Latha, G.M, 2011. Bearing resistance of reinforced soil-aggregate systems. Proceedings of the Institution of Civil Engineers: Ground Improvement, 164 (2), 83–95.
   Google Scholar
• Naser, M., and Alavi, A., 2020. Insights into performance fitness and error metrics for machine learning, 25.
   Google Scholar
• Nazari, S., et al., 2020. A proper model to predict energy efficiency, exergy efficiency, and water productivity of a solar still via optimized neural network. Journal of Cleaner Production, 277. https://doi.org/10.1016/j.jclepro.2020.123232
   Google Scholar
• Nazemi, M., and Heidaripanah, A, 2016. Support vector machine to predict the indirect tensile strength of foamed bitumen-stabilised base course materials. Road Materials and Pavement Design, 17 (3), 768–778.
   Web of Science ®Google Scholar
• Negi, M.S. and Singh, S.K, 2019. Experimental and numerical studies on geotextile reinforced subgrade soil. International Journal of Geotechnical Engineering. 1-12. https://doi.org/10.1080/19386362.2019.1684654
   Google Scholar
• Nguyen, H., et al., 2020. Optimizing ANN models with PSO for predicting short building seismic response. Engineering with Computers, 36 (3), 823–837.
   Web of Science ®Google Scholar
• Ogutu, J.O., Schulz-Streeck, T., and Piepho, H.P, 2012. Genomic selection using regularized linear regression models: ridge regression, lasso, elastic net and their extensions. BMC Proceedings, 6 (Suppl. 2). No. 2, pp. 1-6
   PubMedGoogle Scholar
• Olowosulu, A.T., et al., 2020. Investigating surface condition classification of flexible road pavement using data mining techniques. International Journal of Pavement Engineering. 1-12. https://doi.org/10.1080/10298436.2020.1847285
   Web of Science ®Google Scholar
• Oztoprak, S., and Bolton, M.D, 2013. Stiffness of sands through a laboratory test database. Geotechnique, 63 (1), 54–70.
   Web of Science ®Google Scholar
• Perkins, S.W, 1999. Mechanical response of geosynthetic-reinforced flexible pavements. Geosynthetics International, 6 (5), 347–382.
   Web of Science ®Google Scholar
• Quinlan, J.R., 1992. Learning with continuous classes. In: Australian Joint Conference on Artificial Intelligence, 343–348.
   Google Scholar
• Raja, M.N.A. and Shukla, S.K, 2020. An extreme learning machine model for geosynthetic-reinforced sandy soil foundations. Proceedings of the Institution of Civil Engineers – Geotechnical Engineering, 1–42. https://doi.org/10.1680/jgeen.19.00297
   Google Scholar
• Raja, M.N.A. and Shukla, S.K, 2021. Multivariate adaptive regression splines model for reinforced soil foundations. Geosynthetics International, 1–23. https://doi.org/10.1680/jgein.20.00049
   Google Scholar
• Rajesh, U., Sajja, S., and Chakravarthi, V.K, 2016. Studies on engineering performance of geogrid reinforced soft subgrade. Transportation Research Procedia, 17, 164–173.
   Google Scholar
• Rasmussen, C.E., 2006. Gaussian Processes in machine learning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics).
   Google Scholar
• Rousseeuw, P.J, 1984. Least median of squares regression. Journal of the American Statistical Association, 79 (388), 871–880.
   Web of Science ®Google Scholar
• Roy, P.P., and Roy, K, 2008. On some aspects of variable selection for partial least squares regression models. QSAR and Combinatorial Science, 27 (3), 302–313.
   Google Scholar
• Seeger, M, 2004. Gaussian processes for machine learning. International Journal of Neural Systems, 14 (2), 69–106.
   PubMedGoogle Scholar
• Shukla, S.K, 2002. Geosynthetics and their Applications. Geosynthetics and their Applications. Thomas Telford.
   Google Scholar
• Singh, M., Trivedi, A., and Shukla, S.K, 2019. Strength enhancement of the subgrade soil of unpaved road with geosynthetic reinforcement layers. Transportation Geotechnics, 19, 54–60.
   Web of Science ®Google Scholar
• Singh, M., Trivedi, A., and Shukla, S.K., 2020. Fuzzy-based model for predicting strength of geogrid-reinforced subgrade soil with optimal depth of geogrid reinforcement. Transportation Infrastructure Geotechnology.
   Google Scholar
• Soleimanbeigi, A., and Hataf, N, 2006. Prediction of settlement of shallow foundations on reinforced soils using neural networks. Geosynthetics International, 13 (5), 218–218.
   Google Scholar
• Suthar, M, 2020. Applying several machine learning approaches for prediction of unconfined compressive strength of stabilized pond ashes. Neural Computing and Applications, 32 (13), 9019–9028.
   Web of Science ®Google Scholar
• Taskiran, T, 2010. Prediction of California bearing ratio (CBR) of fine grained soils by AI methods. Advances in Engineering Software, 41 (6), 886–892.
   Web of Science ®Google Scholar
• Taylor, K.E, 2001. Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research Atmospheres, 106 (D7), 7183–7192.
   Web of Science ®Google Scholar
• Tenpe, A.R., and Patel, A, 2020. Utilization of support vector models and gene expression programming for soil strength modeling. Arabian Journal for Science and Engineering, 45 (5), 4301–4319.
   Web of Science ®Google Scholar
• Ullah, S., Tanyu, B.F., and Zainab, B, 2020. Development of an artificial neural network (ANN)-based model to predict permanent deformation of base course containing reclaimed asphalt pavement (RAP). Road Materials and Pavement Design, 1–19.
   Web of Science ®Google Scholar
• Üstün, B., Melssen, W.J., and Buydens, L.M.C, 2006. Facilitating the application of support vector regression by using a universal Pearson VII function based kernel. Chemometrics and Intelligent Laboratory Systems, 81 (1), 29–40.
   Web of Science ®Google Scholar
• Vinod, P., and Minu, M, 2010. Use of coir geotextiles in unpaved road construction. Geosynthetics International, 17 (4), 220–227.
   Web of Science ®Google Scholar
• Wang, Y., and Witten, I.H., 1997. Inducing model trees for continuous classes. In: European Conference on Machine Learning (ECML), 1–10.
   Google Scholar
• Webb, G.I., et al., 2011. Lazy learning. Encyclopedia of Machine Learning, 571–572.
   Google Scholar
• Willmott, C.J, 1981. On the validation of models. Physical Geography, 2 (2), 184–194.
   Google Scholar
• Yaseen, Z.M., et al., 2018. Predicting compressive strength of lightweight foamed concrete using extreme learning machine model. Advances in Engineering Software, 115, 112–125.
   Web of Science ®Google Scholar
• Yildirim, B., and Gunaydin, O, 2011. Estimation of California bearing ratio by using soft computing systems. Expert Systems with Applications, 38 (5), 6381–6391.
   Web of Science ®Google Scholar
• Youd, T., 1973. Factors controlling maximum and minimum densities of sands. In evaluation of relative density and its role in geotechnical projects involving cohesionless soils. ASTM International.
   Google Scholar
• Zhang, Z., et al., 2019. Wind speed prediction method using shared weight long short-term memory network and Gaussian process regression. Applied Energy, 247, 270–284.
   Web of Science ®Google Scholar
• Zheng, J., and Hryciw, R.D, 2016. Index void ratios of sands from their intrinsic properties. Journal of Geotechnical and Geoenvironmental Engineering, 142 (12), 06016019.
   Web of Science ®Google Scholar
• Zou, H., and Hastie, T, 2005. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67 (2), 301–320.
   Web of Science ®Google Scholar