Soil stabilization is commonly accepted for amending undesired soil geotechnical characteristics to be suitable for various structures foundations. Various additives namely iron ore (UIR), used automobile oil (UAO), shale rock powder (ShP), a mixture of hydrated lime, gypsum, and cement (LGC), waste plastic pieces (WPP), rice husk (RH), and sandstone powder (SSP) were used with different percentages. The experimental results reveal that additives namely SSP, UIR, and ShP showed an improvement in the unconfined compressive strength (UCS) of the soil, while the rest of the additives showed that the higher the percentage of the additive, the lower the unconfined compressive strength. Neural networks can capture the complex relationship that links several parameters together and provides the possibility to study the effect of each input variable on the desired output. Thus, this study was implemented to examine the applicability of Artificial Neural Networks (ANNs) to perform an effective prediction model for some geotechnical characteristics correlation of stabilized clayey soils utilizing various stabilization agents. Four input variables including liquid limit (LL), plastic limit (PL), linear shrinkage limit (SL), and additives type while the output is unconfined compressive strength (UCS) of the stabilized clayey samples were utilized. The selected geotechnical parameters can be determined in a laboratory on soil samples; however, these tests are time-consuming and expensive. Therefore, one model was developed to estimate the UCS from the consistency parameters. The ANNs models were developed and verified using a database of 74 cases of actual measured parameters of stabilized clayey soils. As the result of the study, the proposed ANNs model has good accuracy for the prediction of the UCS of clayey soils.


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    Title :

    Using Artificial Neural Networks to predict the Unconfined Compressive Strength of Clayey Soils Stabilized by Various Stabilization Agents


    Additional title:

    KSCE J Civ Eng



    Published in:

    Publication date :

    2023-09-01


    Size :

    9 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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