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Advanced soft computing techniques for predicting soil compression coefficient in engineering project: a comparative study

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Abstract

Heterogeneous nature of soil consists of various chemical and physical attributes that make the prediction of soil parameters very tedious and challenging. Moreover, it becomes more difficult when we have more number of variables. This study investigates the feasibility of principal component analysis as dimensionality reduction technique to select the input variables in terms of principal components (PCs), which helps in reducing the complexity and multicollinearity problem. The soil attributes, namely depth of the sample, sand percentage, silt percentage, clay percentage, moisture content, dry density, wet density, void ratio, liquid limit, plastic limit, liquid index, and plastic index, have been employed as influencing factors to estimate the coefficient of compression of soil. Furthermore, the extracted variance-based PCs were used as predictor to build the minimax probability machine regression (MPMR), multivariate adaptive regression splines (MARS), and genetic programming regression (GPR). The predictive accuracy of the models has been assessed via five statistical fitness parameters. In the training phase, the PCA-MARS model has shown good outcomes in terms of fitness measurement parameters (RMSE= 0.004, r = 0.981 and NSE = 0.963). During testing phase, PCA-MARS has outperformed (RMSE= 0.006, r = 0.963 and NSE = 0.912) followed by PCA-GPR and PCA-MPMR. The finding of this research concludes that PCA-based MARS model can be used as new and reliable data-driven approach for estimation of soil parameters. Furthermore, this new tool can help to save the time and capital spent on estimation of different parameter of soil.

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Acknowledgements

This research was supported by the Geographic Information Science Research group, Ton Duc Thang University, Ho Chi Minh city, Vietnam. We would like to thank the Nam Cuong Ha Noi Corporation Joint Stock Company and TOMAS JSC for providing the data for this research.

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Authors and Affiliations

  1. Department of Geological-Geotechnical Engineering, Hanoi University of Mining and Geology, No. 18 Pho Vien, Duc Thang, Bac Tu Liem, Hanoi, Vietnam

    Viet-Ha Nhu

  2. Geographic Information Science Research Group, Ton Duc Thang University, Ho Chi Minh, Vietnam

    Pijush Samui

  3. Faculty of Environment and Labour Safety,  Ton Duc Thang University, Ho Chi Minh, Vietnam

    Pijush Samui

  4. Department of Civil Engineering, National Institute of Technology Patna, Ashok Raj Path, Patna, 800005, India

    Deepak Kumar & Anshuman Singh

  5. Faculty of Civil Engineering, Institute of Research and Development, Duy Tan University, P809-03, Quang Trung, Da Nang, 550000, Vietnam

    Nhat-Duc Hoang

  6. Geographic Information System Group, Department of Business and IT, University of South-Eastern Norway, Gullbringvegen 36, 3800, Bø i Telemark, Norway

    Dieu Tien Bui

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  1. Viet-Ha Nhu
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Correspondence to Deepak Kumar.

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Nhu, VH., Samui, P., Kumar, D. et al. Advanced soft computing techniques for predicting soil compression coefficient in engineering project: a comparative study. Engineering with Computers 36, 1405–1416 (2020). https://doi.org/10.1007/s00366-019-00772-7

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