State-of-the-art XGBoost, RF and DNN based soft-computing models for PGPN piles

References

• Al‐Atroush, M.E., et al. 2020. Behavior of a large diameter bored pile in drained and undrained conditions: comparative analysis. Geosciences, 10 (7), 261. doi:10.3390/GEOSCIENCES10070261.
   Web of Science ®Google Scholar
• Alkoot, F.M., Alkhedher, H.M., and Alkoot, Z.F., 2023. Experimental analysis of machine learning methods to detect Covid-19 from x-rays. Journal of Engineering Research, 11 (2), 100063. doi:10.1016/J.JER.2023.100063.
   Web of Science ®Google Scholar
• Alkroosh, I.S., et al. 2015. Regressive approach for predicting bearing capacity of bored piles from cone penetration test data. Journal of Rock Mechanics and Geotechnical Engineering, 7 (5), 584–592. doi:10.1016/J.JRMGE.2015.06.011.
   Google Scholar
• Amjad, M., et al. 2022. Prediction of pile bearing capacity using XGBoost Algorithm: modeling and performance evaluation. Applied Sciences (Switzerland), 12 (4), 2126. doi:10.3390/app12042126.
   Web of Science ®Google Scholar
• Asteris, P.G., et al. 2021a. Prediction of cement-based mortars compressive strength using machine learning techniques. Neural Computing and Applications, 33 (19), 13089–13121. doi:10.1007/s00521-021-06004-8.
   Web of Science ®Google Scholar
• Asteris, P.G., et al. 2021b. Soft computing-based models for the prediction of masonry compressive strength. Engineering Structures, 248, 113276. doi:10.1016/J.ENGSTRUCT.2021.113276
   Web of Science ®Google Scholar
• Bak, E., 2013. Numerical modeling of pile load tests. Pollack Periodica, 8 (2), 131–140. doi:10.1556/Pollack.8.2013.2.14.
   Google Scholar
• Bazaraa, A.R. and Kurkur, M.M., 1986. N-Values used to predict settlements of piles in Egypt. Use of in situ Tests in Geotechnical Engineering, 462–474.
   Google Scholar
• Biswas, R., Rai, B., and Samui, P. 2021. Compressive strength prediction model of high-strength concrete with silica fume by destructive and non-destructive technique. Innovative Infrastructure Solutions, 6 (2), 1–14. doi:10.1007/s41062-020-00447-z.
   Web of Science ®Google Scholar
• Breiman, L., 2001. Random forests. Machine Learning, 45 (1), 5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Chen, T. and Guestrin, C. (2016). Xgboost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-Augu, 785–794. 10.1145/2939672.2939785
   Google Scholar
• Fatehnia, M. and Amirinia, G. 2018. A review of genetic programming and artificial neural network applications in pile foundations. International Journal of Geo-Engineering, 9 (1), 1–20. doi:10.1186/s40703-017-0067-6.
   Google Scholar
• Fishman, K.L., Richards, R., Jr., and Yao, D. 2003. Inclination factors for seismic bearing capacity. Journal of Geotechnical and Geoenvironmental Engineering, 129 (9), 861–865. doi:10.1061/(ASCE)1090-0241(2003)129:9(861).
   Web of Science ®Google Scholar
• He, S., et al. 2022. Pile group response induced by adjacent shield tunnelling in clay: Scale model test and numerical simulation. Tunnelling and Underground Space Technology, 120, 104039. doi:10.1016/j.tust.2021.104039
   Web of Science ®Google Scholar
• Heidarie Golafzani, S., Jamshidi Chenari, R., and Eslami, A. 2019. Reliability based assessment of axial pile bearing capacity: static analysis, SPT and CPT-based methods. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 14 (3), 216–230. doi:10.1080/17499518.2019.1628281.
   Google Scholar
• Hong, H., et al. 2017. Comparing the performance of a logistic regression and a random forest model in landslide susceptibility assessments. The Case of Wuyaun Area, China. In: Advancing culture of living with landslides. Springer International Publishing, 1043–1050. doi:10.1007/978-3-319-53498-5_118.
   Google Scholar
• Huynh, V.-H., et al. 2022. A novel direct SPT method to accurately estimate ultimate axial bearing capacity of bored PHC nodular piles with 81 case studies in Vietnam. Soils & Foundations, 62 (4), 101163. doi:10.1016/j.sandf.2022.101163.
   Web of Science ®Google Scholar
• Jamaleddyn, I., Elayachi, R., and Biniz, M. 2023. An improved approach to Arabic news classification based on hyperparameter tuning of machine learning algorithms. Journal of Engineering Research, 11 (2), 100061. doi:10.1016/J.JER.2023.100061.
   Web of Science ®Google Scholar
• Jianbin, Z., Jiewen, T., and Yongqiang, S., 2010. An ANN model for predicting level ultimate bearing capacity of PHC Pipe Pile. Earth and Space, 2010, 3168–3176. doi:10.1061/41096(366)302
   Google Scholar
• Jiang, F., et al. 2020. Comparison of ultimate bearing capacity based on empirical method, interpretation of loading pile test and finite element. IOP Conference Series: Materials Science & Engineering, 930 (1), 012036. doi:10.1088/1742-6596/1693/1/012036.
   Google Scholar
• Józefiak, K., et al. 2015. Numerical modelling and bearing capacity analysis of pile foundation. Procedia Engineering, 111, 356–363. doi:10.1016/j.proeng.2015.07.101
   Google Scholar
• Kalinli, A., Acar, M.C., and Gündüz, Z., 2011. New approaches to determine the ultimate bearing capacity of shallow foundations based on artificial neural networks and ant colony optimization. Engineering Geology, 117 (1–2), 29–38. doi:10.1016/J.ENGGEO.2010.10.002.
   Web of Science ®Google Scholar
• Karim, M.A. 2016. Evaluation of measured and interpreted failure loads of bored piles in alluvial soil deposits. International Journal of Geomate, 10 (1), 1636–1643. doi:10.21660/2016.19.201111.
   Google Scholar
• Khan, E.A. and Shambour, M.K., 2023. An optimized solution for the transportation scheduling of pilgrims in Hajj using harmony search algorithm. Journal of Engineering Research, 11 (2), 100038. doi:10.1016/J.JER.2023.100038.
   Web of Science ®Google Scholar
• Khatti, J. and Grover, K.S. 2023a. Prediction of compaction parameters for fine-grained soil: Critical comparison of the deep learning and standalone models. Journal of Rock Mechanics and Geotechnical Engineering, 15 (11), 3010–3038. doi:10.1016/J.JRMGE.2022.12.034.
   Web of Science ®Google Scholar
• Khatti, J. and Grover, K.S. 2023b. Prediction of compaction parameters of compacted soil using LSSVM, LSTM, LSBoostRF, and ANN. Innovative Infrastructure Solutions, 8 (2), 1–34. doi:10.1007/s41062-023-01048-2.
   Web of Science ®Google Scholar
• Krasiński, A. and Wiszniewski, M., 2017. Static load test on instrumented pile – Field data and numerical simulations. Studia Geotechnica et Mechanica, 39 (3), 17–25. doi:10.1515/sgem-2017-0026.
   Google Scholar
• Kumar, D.R., Samui, P., and Burman, A. 2022. Prediction of probability of liquefaction using hybrid ANN with optimization techniques. Arabian Journal of Geosciences, 15 (20), 1–21. doi:10.1007/S12517-022-10855-3.
   Google Scholar
• Kumar, M., et al. 2022a. Metaheuristic models for the prediction of bearing capacity of pile foundation. Geomechanics and Engineering, 31 (2), 129–147. doi:10.12989/GAE.2022.31.2.129.
   Web of Science ®Google Scholar
• Kumar, M., et al. 2022b. Hybrid ELM and MARS-Based prediction model for bearing capacity of shallow foundation. Processes, 10 (5), 1013. doi:10.3390/PR10051013.
   Web of Science ®Google Scholar
• Kumar, M. and Samui, P. 2020. Reliability analysis of settlement of pile group in clay using LSSVM, GMDH, GPR. Geotechnical and Geological Engineering, 38 (6), 6717–6730. doi:10.1007/s10706-020-01464-6.
   Web of Science ®Google Scholar
• Kumar, M. and Samui, P., 2022. Reliability analysis of pile foundation using GMDH, GP and MARS. In: Lecture Notes in Civil Engineering. Singapore: Springer, Vol. 203, 1151–1159. 10.1007/978-981-16-7160-9_117
   Google Scholar
• Kumar, M., et al. 2021. Reliability analysis of settlement of pile group. Innovative Infrastructure Solutions, 6 (1). doi:10.1007/s41062-020-00382-z
   Web of Science ®Google Scholar
• Kumar, D.R., et al. 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. doi:10.1016/j.iswa.2023.200314
   Google Scholar
• Kumar, R., Kumar, A., and Ranjan Kumar, D., 2023. Buckling response of CNT based hybrid FG plates using finite element method and machine learning method. Composite Structures, 319, 117204. doi:10.1016/J.COMPSTRUCT.2023.117204
   Web of Science ®Google Scholar
• Lastiasih, Y. and Sari, P.T.K. 2020. Comparison of ultimate bearing capacity based on empirical method, interpretation of loading pile test and finite element. IOP Conference Series: Materials Science & Engineering, 930 (1), 012036. doi:10.1088/1757-899X/930/1/012036.
   Google Scholar
• Liu, S., et al. 2023. A comprehensive review of machine learning-based methods in landslide susceptibility mapping. Geological Journal, 58 (6), 2283–2301. doi:10.1002/GJ.4666.
   Web of Science ®Google Scholar
• Loganathan, N. and Poulos, H.G., 1998. Analytical prediction for tunneling-induced ground movements in clays. Journal of Geotechnical and Geoenvironmental Engineering, 124 (9), 846–856. doi:10.1061/(ASCE)1090-0241(1998)124:9(846).
   Web of Science ®Google Scholar
• Lopes, F.R. and Laprovitera, H., 1991. Prediction of the bearing capacity of bored piles from dynamic penetration tests. International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts, 28 (1), A48. doi:10.1016/0148-9062(91)93619-H.
   Google Scholar
• Lundberg, S.M., Allen, P.G., and Lee, S.-I., 2017. A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 30. https://github.com/slundberg/shap
   Google Scholar
• Mayerhof, G. 1976. Bearing capacity and settlement of pile foundations. Journal of Geotechnical and Geoenvironmental Engineering, 102 (3), 197–228. doi:10.1061/AJGEB6.0000243.
   Google Scholar
• Moayedi, H. and Hayati, S., 2019. Artificial intelligence design charts for predicting friction capacity of driven pile in clay. Neural Computing and Applications, 31 (11), 7429–7445. doi:10.1007/s00521-018-3555-5.
   Web of Science ®Google Scholar
• Nguyen, T., et al. 2022. Prediction of axial load bearing capacity of PHC nodular pile using Bayesian regularization artificial neural network. Soils and Foundations, 62 (5), 101203. doi:10.1016/j.sandf.2022.101203.
   Web of Science ®Google Scholar
• Pham, T.A., et al. 2020. Prediction of pile axial bearing capacity using artificial neural network and random forest. Applied Sciences, 10 (5), 1871. 10.3390/app10051871
   Google Scholar
• Pham, T.A., Nguyen, D.H., and Duong, H.A.T., 2022. Development of deep learning neural network for estimating pile bearing capacity. Lecture Notes in Civil Engineering, 203, 1815–1823. doi:10.1007/978-981-16-7160-9_183/COVER
   Google Scholar
• Rodriguez-Galiano, V.F., Chica-Olmo, M., and Chica-Rivas, M., 2014. Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain. International Journal of Geographical Information Science, 28 (7), 1336–1354. doi:10.1080/13658816.2014.885527.
   Web of Science ®Google Scholar
• Wengang, Z., et al. 2023. Application of machine learning in slope stability assessment. Springer Nature Singapore. doi:10.1007/978-981-99-2756-2.
   Google Scholar
• Whittle, A.J., 1993. Assessment of an effective stress analysis for predicting the performance of driven piles in clays. Dordrecht: Springer, 607–643. doi:10.1007/978-94-017-2473-9_29.
   Google Scholar
• Yao, W.J., et al. 2019. The analysis model of shaft resistance softening of a new type of static drill rooted super-long nodular pile. Arabian Journal of Geosciences, 12 (5), 1–7. doi:10.1007/S12517-019-4320-5.
   Web of Science ®Google Scholar
• Zhang, W. and Wu, C., 2020. Machine learning predictive models for pile drivability: an evaluation of random forest regression and multivariate adaptive regression splines. Springer Series in Geomechanics and Geoengineering. doi:10.1007/978-3-030-32029-4_21.
   Google Scholar
• Zhang, W., et al. 2021. Assessment of pile drivability using random forest regression and multivariate adaptive regression splines. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 15 (1), 27–40. doi:10.1080/17499518.2019.1674340.
   Google Scholar