Abstract
In this study, Random Forest Regressor, Linear Regression, Generalized Regression Neural Network (GRNN) and Fully connected Neural Network (FCNN) models are leveraged for predicting unconfined compression coefficient with respect to standard penetration test (N-value), depth and soil type. The study is focused on a particular correlation of undrained shear strength of clay (Cu) with the standard penetration strength. The data used is from 14 no. ward in Mymensingh and Rangamati districts which are situated in Bangladesh. By using this data, the study tries to solidify the correlation of SPT (N-value) with Cu. It also tries to check the goodness of the relationship by comparing it with unconfined compression strength values gained from the unconfined compression test calculated from the field by experts.
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Acknowledgment
The authors would like to thank the Department of Urban and Regional Planning from Bangladesh University of Engineering and Technology for providing us with the related datasets from Mymensingh Ward 14.
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Sarwar, A.M. et al. (2020). Soil Analysis and Unconfined Compression Test Study Using Data Mining Techniques. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_4
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DOI: https://doi.org/10.1007/978-3-030-63119-2_4
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