Abstract
Cone Penetration Test (CPT) provides us with subsurface information with high resolution and good accuracy, which does not confirm the types of in-situ geomaterials directly. The engineering experience-driven classification charts or tables are usually used when CPT data is applied for soil stratification. However, these charts or tables have an inherent limitation that they were derived merely based on the given field experiences, which indicates that these cannot represent the engineering characteristic of all the soils in the world. This study proposes that the development of locally modified CPT-based soil classification methods can be performed with machine learning techniques. The results show that, using the simply trained algorithm with sufficient training data, the locally specified soil stratification is possible with high accuracy.
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References
Bhattacharya, B., and Solomatine, D.P. (2006). Machine learning in soil classification. Neural Networks, 19, pp 186-195.
Ian H.W. (2011). DATA MINING: Practical Machine Learning Tools and Techniques. Elsevier.
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1), pp 81-106.
Quinlan, J.R. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), pp 1-37
Robertson, P.K. (1990). Soil classification using the cone penetration test. Canadian Journal of Geotech, 27(1), pp 151-158.
Robertson, P.K., and Wride, C.E. (1998). Evaluating cyclic liquefaction potential using the cone penetration test. Canadian Journal of Geotech, 35(3), pp 442-459.
Robertson, P.K. (2009). Interpretation of cone penetration tests – a unified approach. Canadian Journal of Geotech, 46(11), pp 1337-1355.
Robertson, P.K. (2016). Cone penetration test – based soil behaviour type classification system – an updated. Canadian Journal of Geotech, 53, pp 1910-1927.
Wang, X., Wang, H., Liang, R.Y., and Liu, Y. (2019). A semi-supervised clustering-based approach for stratification identification using borehole and cone penetration test data. Engineering Geology, 248, pp 102-116.
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Cho, S., Cho, B., Kang, S., Kim, H. (2020). Development of Locally Specified Soil Stratification Method with CPT Data Based on Machine Learning Techniques. In: Duc Long, P., Dung, N. (eds) Geotechnics for Sustainable Infrastructure Development. Lecture Notes in Civil Engineering, vol 62. Springer, Singapore. https://doi.org/10.1007/978-981-15-2184-3_170
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DOI: https://doi.org/10.1007/978-981-15-2184-3_170
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