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Multivariate Adaptive Regression Splines for Stability Number of Unsupported Conical Slopes in Anisotropic and Heterogeneous Clays

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Computational Intelligence Methods for Green Technology and Sustainable Development (GTSD 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 567))

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Abstract

The novel equation for determining the stability number of unsupported conical slopes in anisotropic and heterogeneous clays is presented in this study. Multivariate Adaptive Regression Splines (MARS) model ~ a machine learning approach, is adopted to build the close–form equation between input variables and output results, and also investigate the coupling effects among input variables by sensitive analysis. The artificial data for MARS model is 576 combinations of four input variables (i.e., the ratio between the height and the radius at the bottom slope, and the inclination angle of slope, the gradient of increasing undrained shear strength, ratio of anisotropic) corresponding to output results of stability number which is based on finite element limit analysis (FELA) results from a previous study. The results of the paper provide a guidance theory and effective tool for practical engineering in determining the stability number of unsupported conical slopes in anisotropic and heterogeneous clays.

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Acknowledgement

We would like to thank Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for the support of time and facilities for this study.

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Authors and Affiliations

  1. Faculty of Civil Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam

    Van Qui Lai

  2. Vietnam National University Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam

    Van Qui Lai

  3. Department of Civil Engineering, Thammasat School of Engineering, Thammasat University, Khlong Nueng, Pathumthani, 12120, Thailand

    Chanachai Thongchom & Suraparb Keawsawasvong

  4. Faculty of Civil Engineering, Ho Chi Minh City University of Technology and Education, No. 1 Vo Van Ngan Street, Linh Chieu Ward, Thu Duc City, Ho Chi Minh City, Vietnam

    Chung Nguyen Van

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  1. Van Qui Lai
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  2. Chanachai Thongchom
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  3. Suraparb Keawsawasvong
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  4. Chung Nguyen Van
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Corresponding author

Correspondence to Van Qui Lai .

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Editors and Affiliations

  1. National Penghu University of Science and Technology, Magong City, Taiwan

    Yo-** Huang

  2. Department of Electrical Engineering, National Central University, Zhongli, Taiwan

    Wen-June Wang

  3. HCMC University of Technology and Education, Ho Chi Minh City, Vietnam

    Hoang An Quoc

  4. HCMC University of Technology and Education, Ho Chi Minh City, Vietnam

    Hieu-Giang Le

  5. Nha Trang University, Nha Trang City, Vietnam

    Hoai-Nam Quach

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Lai, V.Q., Thongchom, C., Keawsawasvong, S., Van, C.N. (2023). Multivariate Adaptive Regression Splines for Stability Number of Unsupported Conical Slopes in Anisotropic and Heterogeneous Clays. In: Huang, YP., Wang, WJ., Quoc, H.A., Le, HG., Quach, HN. (eds) Computational Intelligence Methods for Green Technology and Sustainable Development. GTSD 2022. Lecture Notes in Networks and Systems, vol 567. Springer, Cham. https://doi.org/10.1007/978-3-031-19694-2_15

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