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    Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach

    Access Status
    Fulltext not available
    Authors
    Samaei, Masoud
    Alinejad Omran, Morteza
    Keramati, Mohsen
    Naderi, Reza
    Shirani Faradonbeh, Roohollah
    Date
    2024
    Type
    Journal Article
    
    Metadata
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    Citation
    Samaei, M. and Alinejad Omran, M. and Keramati, M. and Naderi, R. and Shirani Faradonbeh, R. 2024. Assessing the shear strength of sandy soil reinforced with polyethylene-terephthalate: an AI-based approach. Earth Science Informatics.
    Source Title
    Earth Science Informatics
    DOI
    10.1007/s12145-024-01398-0
    ISSN
    1865-0473
    Faculty
    Faculty of Science and Engineering
    School
    WASM: Minerals, Energy and Chemical Engineering
    URI
    http://hdl.handle.net/20.500.11937/95502
    Collection
    • Curtin Research Publications
    Abstract

    This research aimed to investigate the effectiveness of Polyethylene-Terephthalate (PET) as a reinforcement material for sandy soils in enhancing the shear strength. To achieve this, different concentrations of PET were tested, and 118 sets of data were collected. Parameters such as relative density, normal stress in direct shear strength test, and types of PET elements (1 × 1, 1 × 5, and fiber) were also recorded. Subsequently, four decision tree-oriented machine learning (ML) methods—decision tree (DT), random forest (RF), AdaBoost, and XGBoost—were applied to construct models capable of forecasting enhancements in shear strength. The evaluation of these models' effectiveness was conducted using four established statistical metrics: R2, RMSE, VAF, and A-10. The results showed that AdaBoost results in the highest prediction accuracy among other algorithms, representing the high modelling performance of the algorithm in dealing with complex nonlinear problems. The conducted sensitivity analysis also revealed that relative density is the most crucial parameter for all the algorithms in predicting the output, followed by PET percentage and normal stress. Furthermore, to make the developed model in this study more practical and easy to use, a Graphical User Interface (GUI) was created, enabling the engineers and researchers to perform the analysis straightforwardly.

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