Assessing the effects of influencing parameters on field strength of soft coastal soil stabilized by deep mixing method

Mahabub, Md Shakil; Hasan, Md Raqibul; Khatti, Jitendra; Hossain, A. T. M. Shakhawat

doi:10.1007/s10064-023-03502-y

Bull Eng Geol Environ

2023

DOI: 10.1007/s10064-023-03502-y

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Assessing the effects of influencing parameters on field strength of soft coastal soil stabilized by deep mixing method

Md Shakil Mahabub,

Md Raqibul Hasan,

Jitendra Khatti

et al.

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2024

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Cited by 8 publications

References 47 publications

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Determination of Compaction Parameters of Cement‐Lime Soils: Boosting‐Based Ensemble Models

Tilahun,

Qinghua,

Ashongo

et al. 2024

Num Anal Meth Geomechanics

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This study investigates the application of artificial intelligence (AI) models to predict soil compaction characteristics, specifically maximum dry density (MDD) and optimum moisture content (OMC), which are critical for stabilizing construction foundations. Traditional methods for determining MDD and OMC are labor‐intensive and often influenced by factors such as soil type, plasticity, and compaction energy (E). The research employed AI models, including random forest regression (RF‐R), gradient boosting regression (GB‐R), XGBoosting regressor (XGB‐R), and multilinear regression (ML‐R), trained on a comprehensive dataset of soil properties. For the first time, compaction energy has been used as an input variable to predict soil cement lime stabilized compaction parameters. Among the models, GB‐R demonstrated the highest prediction accuracy for MDD and OMC, outperforming RF‐R, XGB‐R, and ML‐R. The performance of built‐in models has been measured by three new index performance metrics: the a20‐index, the index of scatter (IS), and the index of agreement (IA), in addition to four common metrics. Taylor diagrams confirmed the robustness of these predictions during lab testing. A sensitivity analysis revealed that MDD and OMC were most influenced by plastic limit (PL), compaction energy (E), liquid limit (LL), and plasticity index (PI). Additionally, curve‐fitting techniques were applied to model the relationship between MDD, OMC, and these key factors. The results indicated that the GB‐R model, particularly when focused on essential features, provided superior accuracy compared to traditional regression methods, offering a reliable tool for soil stabilization assessments in construction.

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Determination of Compaction Parameters of Cement‐Lime Soils: Boosting‐Based Ensemble Models

Tilahun,

Qinghua,

Ashongo

et al. 2024

Num Anal Meth Geomechanics

View full text Add to dashboard Cite

show abstract

Laboratory Investigation of Deep Soil Mixing for the Improvement of Salt-Cemented Soils

Hammad,

Mohamedzein,

Al-Aghbari

et al. 2024

Geotech Geol Eng

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An investigation of nonmagnetic iron slag effect on engineering parameters of expansive soil and its statistical validation

Jangid,

Grover

2024

Multiscale and Multidiscip. Model. Exp. and Des.

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Assessing the effects of influencing parameters on field strength of soft coastal soil stabilized by deep mixing method

Cited by 8 publications

References 47 publications

Determination of Compaction Parameters of Cement‐Lime Soils: Boosting‐Based Ensemble Models

Determination of Compaction Parameters of Cement‐Lime Soils: Boosting‐Based Ensemble Models

Laboratory Investigation of Deep Soil Mixing for the Improvement of Salt-Cemented Soils

An investigation of nonmagnetic iron slag effect on engineering parameters of expansive soil and its statistical validation

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