Applied machine learning and predictive modeling techniques for soil profiling. Practical CPTU application
DOI:
https://doi.org/10.14195/2184-8394_152_19Keywords:
Machine learning, Soil characterization, CPTUAbstract
This article addresses some concepts and principles of machine learning applied to soil profiling and geotechnical characterization based on cone penetration testing (CPTU). Some practical outcomes from a survey at a Spanish Port are discussed. Ultimately, the article aims to provide a mathematical approach that assist designers to produce, if possible, a more objectively assessable repeatable and precise soil profile models.
Downloads
References
Abe, S. (2010). Support Vector Machines for Pattern Classification. 2a Edicion. Springer.
Cetin, K.; Seed, R.; Kayen, R.; Moss, R.; Bilge, T.; Ilgaç, M.; Chowdhury, K. (2018). Dataset on SPT-based seismic soil liquefaction. Data in Brief. 20. 10.1016/j.dib.2018.08.043.
Duda, R. O.; Hart, P. E.; Stork, D. G. (2000). Pattern Classification, 2a edición, John Wiley & Sons Inc EN 1997-2 (2007): Eurocode 7: Geotechnical design - Part 2: Ground investigation and testing. CEN, Brussels, Belgium.
Hartigan, J.A.; Wong, M. (1979). A k-means clustering algorithm. Applied Statistics 28, 100-108.
Hastie, T.; Tibshirani, R.; Friedman, J. (2008). The elements of statistical learning: Data mining, inference and prediction. 2a edición. Springer.
Hegazy, Y.A.; Mayne, P.W. (2002). Objective site characterization using clustering of piezocone data, Journal of Geotechnical and Geoenvironmental Engineering, 128(12): 986-996.
Khun, M.; Johnson, K. (2016). Applied predictive modelling. 5a Edición. Springer.
Kolmogorov, A. N. (1957). On the representation of continuous functions of many variables by superposition of continuous functions of one variable and addition. Dokl. Akad. Nauk SSSR, 114:5 (1957), 953-956.
Krizhevsky, A.; Sutskever, I.; Hinton E.H. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Neural Information Processing Systems. 25. 10.1145/3065386
Lloyd, S.P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory 28, 129-137.
López Mántaras, R. (2020). El traje nuevo de la inteligencia artificial. Investigación y Ciencia, 526, pp 52-59.
MacQueen, J.B. (1967). Some methods for classification and analysis of multivariate observations. In: Le cam, L.M., Neyman, J. (Eds.), Proceedings of 5th Symposium on Mathematical Statistics and Probability, vol. 1. University of California Press, Berkeley, pp. 281-297.
Mlynarek, Z.; Wierzbicki, J.; Wolynski, W. (2005). Use of cluster method for in situ tests. Studia Geotechnica et Mechanica, XXVII(3-4): 15-27.
Murphy, K. P. (2012). Machine Learning: A probabilistic perspective. Massachusetts Institute of Technology.
Nadim, F. (2007). Tools and Strategies for Dealing with Uncertainty in Geotechnics. In: Griffiths D.V., Fenton G.A. (eds). Probabilistic Methods in Geotechnical Engineering. Springer.
Peña, D. (2004). Análisis de datos multivariantes. McGraw-Hill Interamericana de España S.L.
Robertson, P.K. (2016). Cone penetration test (CPT)-Based soil behaviour type (SBT) Classification system- an update. Canadian geotechnical journal, 53, pp. 1910-1927.
Vapnik, V. N. (1999). The nature of statistical learning theory. 2a edición. Springer.
Wierzbicki J.; Smaga A.; Stefaniak K.; Wołyński W. (2016). 3D mapping of organic layers by means of CPTU and statistical data analysis. Geotechnical and Geophysical Site Characterisation 5 - Lehane, Acosta-Martínez & Kelly (Eds.)
Wierzchon, S. T.; Klopotek, M. A. (2018). Modern algorithms of cluster analysis. Springer.
