An Assessment of Soil Parameters Governing Soil Strength Increases with Chemical Additives

Hussey, N. L.; Cerato, Amy B.; Grasmick, J. G.; Holderby, Eric; Miller, Gerald A.; Tabet, Wassim E.

doi:10.1061/41095(365)274

Cited by 10 publications

(2 citation statements)

References 9 publications

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“…These findings indicate a significant boost in UCS values of 339% and 435%, respectively, for 3% and 6% lime content. Several other researchers [55,[59][60][61][62], have consistently reported this trend. Figure 9 also illustrates that the resistance of soils treated with lime tends to rise until it peaks at a lime content of 6%.…”

Section: Parametric Studymentioning

confidence: 52%

Strength Prediction of Fiber-Reinforced Clay Soils Stabilized with Lime Using XGBoost Machine Learning

Sari-Ahmed,

Benzaamia,

Ghrici

et al. 2024

Civil and Environmental Engineering Reports

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This article proposes a predictive model for the compressive strength (UCS) of lime-stabilized clay soils reinforced with polypropylene fibers (PPF) using the extreme gradient boosting (XGBoost) algorithm. The research indicates that the developed model is highly effective and can serve as a reliable tool for anticipating the UCS of these specific soils. A comparison between experimental data and model predictions suggests that it can effectively elucidate the impact of the combined effect of lime and PPF on the compressive strength of clay soils, thus avoiding the need for new experiments to formulate new compositions. Furthermore, a parametric analysis reveals the benefits of fiber incorporation, particularly at an optimum lime content of 6% dosage. The results also show that an optimal fiber content of 1.25% and a length of 18 mm are essential for achieving satisfactory results. These findings have significant implications for the planning and implementing fibre treatments, allowing for considerably enhancing soil strength. They provide a solid foundation for more precise and effective interventions in the lime stabilization of clay soils, thus paving the way for more efficient practices in this area of research.

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Section: Parametric Studymentioning

confidence: 52%

Strength Prediction of Fiber-Reinforced Clay Soils Stabilized with Lime Using XGBoost Machine Learning

Sari-Ahmed,

Benzaamia,

Ghrici

et al. 2024

Civil and Environmental Engineering Reports

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“…This can be achieved through physical or chemical soil stabilisation methods. Physical soil stabilisation methods include compaction, consolidation, mixing soils of different grain size to obtain a well-graded soil for construction purposes, and finally removal of existing soil and replacing it with a natural soil deposit suitable for construction [1,5,6]. Soil chemical stabilisation methods have been used in recent decades [4,[7][8][9][10][11] to improve slopes, foundation structures, increase slope stability, prevent liquefaction, and stabilise contaminated soils, among others [9,12,13].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Elastic Modulus for Fibre-Reinforced Soil-Cement Mixtures: A Machine Learning Approach

Owusu-Ansah

Tinoco²,

Correia³

et al. 2022

Applied Sciences

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Soil-cement mixtures reinforced with fibres are an alternative method of chemical soil stabilisation in which the inherent disadvantage of low or no tensile or flexural strength is overcome by incorporating fibres. These mixtures require a significant amount of time and resources for comprehensive laboratory characterisation, because a considerable number of parameters are involved. Therefore, the implementation of a Machine Learning (ML) approach provides an alternative way to predict the mechanical properties of soil-cement mixtures reinforced with fibres. In this study, Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), Random Forest (RF), and Multiple Regression (MR) algorithms were trained for predicting the elastic modulus of soil-cement mixtures reinforced with fibres. For ML algorithms training, a dataset of 121 records was used, comprising 16 properties of the composite material (soil, binder, and fibres). ANN and RF showed a promising determination coefficient (R2 ≥ 0.93) on elastic modulus prediction. Moreover, the results of the proposed models are consistent with the findings that the fibre and binder content have a significant effect on the elastic modulus.

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The Utilisation of Colloidal Silica Grout in Soil Stabilisation and Liquefaction Mitigation: A State of the Art Review

Krishnan

Shukla

2021

Geotech Geol Eng

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An Assessment of Soil Parameters Governing Soil Strength Increases with Chemical Additives

Cited by 10 publications

References 9 publications

Strength Prediction of Fiber-Reinforced Clay Soils Stabilized with Lime Using XGBoost Machine Learning

Strength Prediction of Fiber-Reinforced Clay Soils Stabilized with Lime Using XGBoost Machine Learning

Prediction of Elastic Modulus for Fibre-Reinforced Soil-Cement Mixtures: A Machine Learning Approach

The Utilisation of Colloidal Silica Grout in Soil Stabilisation and Liquefaction Mitigation: A State of the Art Review

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