Gradient boosting trees with Bayesian optimization to predict activity from other geotechnical parameters

References

• Al-Rawas, A. A. 1999. The Factors Controlling the Expansive Nature of the Soils and Rocks of Northern Oman. Engineering Geology 53 (3–4): 327–350. https://doi.org/10.1016/S0013-7952(98)00085-4
   Web of Science ®Google Scholar
• Arifin, Y. F. 2008. Thermo-hydro-mechanical behavior of compacted bentonite-sand mixtures: An experimental study. https://nbn-resolving.org/urn:nbn:de:gbv:wim2-20081006-14288. https://doi.org/10.25643/bauhaus-universitaet.852
   Google Scholar
• Breiman, L. I., J. H. Friedman, R. A. Olshen, and C. J. Stone. 1984. Classification and Regression Trees (Cart). Encyclopedia of Ecology 40: 582–588.
   Google Scholar
• Cerato, A. 2001. Influence of Specific Surface Area on Geotechnical Characteristics of Fine-Grained Soils. Unpublished MSc Thesis, Department of Civil and Environmental Engineering, University of Massachusetts.
   Google Scholar
• Cerato, A. B., and A. J. Lutenegger. 2002. Determination of Surface Area of Fine-grained Soils by the Ethylene Glycol Monoethyl Ether (EGME) Method. Geotechnical Testing Journal 25: 315–321.
   Google Scholar
• Cerato, A. B., and A. J. Lutenegger. 2004. Determining Intrinsic Compressibility of Fine-Grained Soils. Journal of Geotechnical and Geoenvironmental Engineering 130 (8): 872–877. https://doi.org/10.1061/(ASCE)1090-0241(2004)130:8(872)
   Web of Science ®Google Scholar
• Cerato, A. B., and A. J. Lutenegger. 2005. Activity, Relative Activity and Specific Surface Area of Fine-Grained Soils. Proceedings of the International Conference on Soil Mechanics and Geotechnical Engineering. AA Balkema Publishers, 325.
   Google Scholar
• Chittoori, B., and A. J. Puppala. 2011. Quantitative Estimation of Clay Mineralogy in Fine-Grained Soils. Journal of Geotechnical and Geoenvironmental Engineering 137 (11): 997–1008. https://doi.org/10.1061/(ASCE)GT.1943-5606.0000521
   Web of Science ®Google Scholar
• Christidis, G. E. 1998. Physical and Chemical Properties of Some Bentonite Deposits of Kimolos Island, Greece. Applied Clay Science 13 (2): 79–98. https://doi.org/10.1016/S0169-1317(98)00023-4
   Web of Science ®Google Scholar
• Devore, J. L., N. R. Farnum, and J. A. Doi. 2013. Applied Statistics for Engineers and Scientists. Cengage Learning.
   Google Scholar
• Díaz, E., and R. Tomás. 2021. Upgrading the Prediction of Jet Grouting Column Diameter Using Deep Learning with an Emphasis on High Energies. Acta Geotechnica 16 (5): 1627–1633. https://doi.org/10.1007/s11440-020-01091-8
   Web of Science ®Google Scholar
• Díaz, E., J. Pastor, Á. Rabat, and R. Tomás. 2021. Machine Learning Techniques for Relating Liquid Limit Obtained by Casagrande Cup and Fall Cone Test in Low-Medium Plasticity Fine Grained Soils. Engineering Geology 294: 106381. https://doi.org/10.1016/j.enggeo.2021.106381
   Web of Science ®Google Scholar
• Díaz, E., V. Brotons, and R. Tomás. 2018. Use of Artificial Neural Networks to Predict 3-D Elastic Settlement of Foundations on Soils with Inclined Bedrock. Soils and Foundations 58 (6): 1414–1422. https://doi.org/10.1016/j.sandf.2018.08.001
   Web of Science ®Google Scholar
• Eriksson, D., M. Pearce, J. Gardner, R. D. Turner, and M. Poloczek. 2019. Scalable Global Optimization via Local Bayesian Optimization. Advances in Neural Information Processing Systems 32.
   Google Scholar
• Freund, Y. 1995. Boosting a Weak Learning Algorithm by Majority. Information and Computation 121 (2): 256–285. https://doi.org/10.1006/inco.1995.1136
   Web of Science ®Google Scholar
• Freund, Y., and R. E. Schapire. 1996. Experiments with a New Boosting Algorithm. ICML Citeseer 96: 148–156.
   Google Scholar
• Friedman, JHJAos. 2001. Greedy Function Approximation: A Gradient Boosting Machine. 1189–1232.
   Google Scholar
• Gelman, A., and J. Hill. 2006. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge, UK: Cambridge University Press.
   Google Scholar
• Head, T., M. Kumar, H. Nahrstaedt, G. Louppe, and I. Shcherbatyi. 2020. Scikit-optimize/scikit-optimize: V 0. 8.1. Zenodo.
   Google Scholar
• Hollander, M., D. A. Wolfe, and E. Chicken. 2015. Nonparametric Statistical Methods. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Jones, D. E., Jr, and W. G. Holtz. 1973. Expansive Soils-the Hidden Disaster. Civil Engineering 43: 49–51.
   Web of Science ®Google Scholar
• Jones, D. R. 2001. A Taxonomy of Global Optimization Methods Based on Response Surfaces. Journal of Global Optimization 21 (4): 345–383. https://doi.org/10.1023/A:1012771025575
   Web of Science ®Google Scholar
• Kar, K. K. 2021. Handbook of Fly Ash. Butterworth-Heinemann.
   Google Scholar
• Lambe, T. W. 1960. The Character and Identification of Expansive Soils: A Report Completed for the Technical Studies Program of the Federal Housing Administration. Federal Housing Administration
   Google Scholar
• Li, D., Z. Liu, P. Xiao, J. Zhou, and D. Jahed Armaghani. 2022. Intelligent Rockburst Prediction Model with Sample Category Balance Using Feedforward Neural Network and Bayesian Optimization. Underground Space 7 (5): 833–846. https://doi.org/10.1016/j.undsp.2021.12.009
   Web of Science ®Google Scholar
• Low, P. F. 1987. Structural Component of the Swelling Pressure of Clays. Langmuir 3 (1): 18–25. https://doi.org/10.1021/la00073a004
   Web of Science ®Google Scholar
• Marcial, D. 2013. Measuring Water Retention Properties of a Series of Bentonite Clays a Wide Range of Suctions. Advances in Unsaturated Soils, 135–140. London: Taylor Francis Group.
   Google Scholar
• Mishra, A. K., M. Ohtsubo, L. Y. Li, and T. Higashi. 2012. Influence of Various Factors on the Difference in the Liquid Limit Values Determined by Casagrande’s and Fall Cone Method. Environmental Earth Sciences 65 (1): 21–27. https://doi.org/10.1007/s12665-011-1061-5
   Web of Science ®Google Scholar
• Mitchell, J. K., and K. Soga. 2005. Fundamentals of Soil Behavior. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Moore, D. S., and G. P. McCabe. 1989. Introduction to the Practice of Statistics. New York, USA: WH Freeman/Times Books/Henry Holt & Co.
   Google Scholar
• Muhunthan, B. 1991. Liquid Limit and Surface Area of Clays. Géotechnique 41 (1): 135–138. https://doi.org/10.1680/geot.1991.41.1.135
   Web of Science ®Google Scholar
• Nelson, J. D., K. C. Chao, D. D. Overton, and E. J. Nelson. 2015. Foundation Engineering for Expansive Soils. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Peck, R. B., W. E. Hanson, and T. H. Thornburn. 1974. Foundation Engineering. Hoboken, NJ: John Wiley & Sons.
   Google Scholar
• Pedregosa, F., G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, and V. Dubourg. 2011. Scikit-Learn: Machine Learning in Python. Journal of Machine Learning Research 12: 2825–2830.
   Web of Science ®Google Scholar
• Phoon, K.-K., and W. Zhang. 2023. Future of Machine Learning in Geotechnics. Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards 17 (1): 7–22. https://doi.org/10.1080/17499518.2022.2087884
   Google Scholar
• Polidori, E. 2009. Reappraisal of the Activity of Clays. Activity Chart. Soils and Foundations 49 (3): 431–441. https://doi.org/10.3208/sandf.49.431
   Web of Science ®Google Scholar
• Salvatore, E., G. Modoni, G. Spagnoli, M. Arciero, M. C. Mascolo, and M. Ochmański. 2022. Conditioning Clayey Soils with a Dispersant Agent for Deep Soil Mixing Application: Laboratory Experiments and Artificial Neural Network Interpretation. Acta Geotechnica 17 (11): 5073–5087. https://doi.org/10.1007/s11440-022-01505-9
   Web of Science ®Google Scholar
• Santhoshkumar, T., B. Abraham, A. Sridharan, and B. Jose. 2016. Role of Bentonite in Improving the Efficiency of Cement Grouting in Coarse Sand. Geotechnical Engineering Journal of the SEAGS & AGSSEA 47: 1–8.
   Google Scholar
• Schapire, R. E. 1990. The Strength of Weak Learnability. Machine Learning 5 (2): 197–227. https://doi.org/10.1007/BF00116037
   Web of Science ®Google Scholar
• Schwing, M., Z. Chen, A. Scheuermann, and N. Wagner. 2013. Dielectric Properties of a Clay Soil Determined in the Frequency Range from 1 MHz to 40 GHz. Proc 10th Int. Conf. Electromagn. Wave Interact. Water and Moist Substances, 242–250.
   Google Scholar
• Shahriari, B., K. Swersky, Z. Wang, R. P. Adams, and N. De Freitas. 2016. Taking the Human out of the Loop: A Review of Bayesian Optimization. Proceedings of the IEEE 104 (1): 148–175. https://doi.org/10.1109/JPROC.2015.2494218
   Web of Science ®Google Scholar
• Sivapullaiah, P. V., B. G. Prasad, and M. M. Allam. 2008. Methylene Blue Surface Area Method to Correlate with Specific Soil Properties. Geotechnical Testing Journal 31 (6): 503–512.
   Google Scholar
• Skempton, A. 1953. The Colloidal Activity of Clays. Selected Papers on Soil Mechanics 1: 57–61.
   Google Scholar
• Snoek, J., H. Larochelle, and R. P. Adams. 2012. Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems 25: 2951–2959.
   PubMedGoogle Scholar
• Spagnoli, G., and S. Shimobe. 2019. A Statistical Reappraisal of the Relationship between Liquid Limit and Specific Surface Area, Cation Exchange Capacity and Activity of Clays. Journal of Rock Mechanics and Geotechnical Engineering 11 (4): 874–881. https://doi.org/10.1016/j.jrmge.2018.11.007
   Web of Science ®Google Scholar
• Spagnoli, G., M. Feinendegen, and D. Rubinos. 2013. Modification of Clay Adhesion to Improve Tunnelling Excavation. Proceedings of the Institution of Civil Engineers – Ground Improvement 166 (1): 21–31. https://doi.org/10.1680/grim.11.00017
   Google Scholar
• Stephens, M. A. 1974. EDF Statistics for Goodness of Fit and Some Comparisons. Journal of the American Statistical Association 69 (347): 730–737. https://doi.org/10.1080/01621459.1974.10480196
   Web of Science ®Google Scholar
• Stone, M. 1974. Cross‐Validatory Choice and Assessment of Statistical Predictions. Journal of the Royal Statistical Society: Series B (Methodological) 36 (2): 111–133. https://doi.org/10.1111/j.2517-6161.1974.tb00994.x
   Web of Science ®Google Scholar
• Wackerly, D., W. Mendenhall, and R. L. Scheaffer. 2014. Mathematical Statistics with Applications. Cengage Learning.
   Google Scholar
• Wang, L., C. Wu, L. Tang, W. Zhang, S. Lacasse, H. Liu, and L. Gao. 2020. Efficient Reliability Analysis of Earth Dam Slope Stability Using Extreme Gradient Boosting Method. Acta Geotechnica 15 (11): 3135–3150. https://doi.org/10.1007/s11440-020-00962-4
   Web of Science ®Google Scholar
• Warkentin, B. 1972. Use of the Liquid Limit in Characterizing Clay Soils. Canadian Journal of Soil Science 52 (3): 457–464. https://doi.org/10.4141/cjss72-057
   Web of Science ®Google Scholar
• Yao, H-l., Y. Yang, and P. Cheng. 2004. Standard Moisture Absorption Water Content of Soil and Its Testing Standard. Rock and Soil Mechanics-WUHAN 25: 856–859.
   Google Scholar
• Zapata, C., S. Houston, W. Houston, and H. Dye. 2006. Expansion Index and Its Relationship with Other Index Properties. Unsaturated Soils 2006: 2133–2137. https://doi.org/10.1061/40802(189)180
   Google Scholar
• Zhang, W., C. Wu, H. Zhong, Y. Li, and L. Wang. 2021. Prediction of Undrained Shear Strength Using Extreme Gradient Boosting and Random Forest Based on Bayesian Optimization. Geoscience Frontiers 12 (1): 469–477. https://doi.org/10.1016/j.gsf.2020.03.007
   Web of Science ®Google Scholar
• Zhang, W., R. Zhang, C. Wu, A. T. Goh, and L. Wang. 2022b. Assessment of Basal Heave Stability for Braced Excavations in Anisotropic Clay Using Extreme Gradient Boosting and Random Forest Regression. Underground Space 7 (2): 233–241. https://doi.org/10.1016/j.undsp.2020.03.001
   Web of Science ®Google Scholar
• Zhang, W., X. Gu, L. Tang, Y. Yin, D. Liu, and Y. Zhang. 2022a. Application of Machine Learning, Deep Learning and Optimization Algorithms in Geoengineering and Geoscience: Comprehensive Review and Future Challenge. Gondwana Research 109: 1–17. https://doi.org/10.1016/j.gr.2022.03.015
   Web of Science ®Google Scholar
• Zhang, W., Y. He, L. Wang, S. Liu, and X. Meng. 2023. Landslide Susceptibility Mapping Using Random Forest and Extreme Gradient Boosting: A Case Study of Fengjie, Chongqing. Geological Journal 58 (6): 2372–2387. https://doi.org/10.1002/gj.4683
   Web of Science ®Google Scholar
• Zhang, Y., Y. Qu, G. Liu, and S. Wu. 2003. Engineering Geological Properties of Miocene Hard Clays along the Middle Line of the North–South Diversion Water Project in China. Bulletin of Engineering Geology and the Environment 62 (3): 213–219. https://doi.org/10.1007/s10064-003-0195-4
   Google Scholar