Material Models to Study the Effect of Fines in Sandy Soils Based on Experimental and Numerical Results

Alnmr, Ammar

doi:10.14513/actatechjaur.00625

Cited by 10 publications

(12 citation statements)

References 21 publications

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“…Balancing the sand fraction is critical in engineering applications because it improves soil performance and reduces the negative impacts of cracking in expansive soil [19]. The addition of sand to expansive soils has been extensively investigated by many researchers to determine its effect on the soil parameters [10,[20][21][22][23][24][25][26]. Previous studies have confirmed that increasing the percentage of sand increases the unconfined compressive strength (UCS) of expansive soils.…”

Section: Sand As An Additive Materials and Its Impact On Soil Parametersmentioning

confidence: 99%

Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

Alnmr,

Hosamo,

Lyu

et al. 2024

Applied Sciences

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This paper presents a novel application of machine learning models to clarify the intricate behaviors of expansive soils, focusing on the impact of sand content, saturation level, and dry density. Departing from conventional methods, this research utilizes a data-centric approach, employing a suite of sophisticated machine learning models to predict soil properties with remarkable precision. The inclusion of a 30% sand mixture is identified as a critical threshold for optimizing soil strength and stiffness, a finding that underscores the transformative potential of sand amendment in soil engineering. In a significant advancement, the study benchmarks the predictive power of several models including extreme gradient boosting (XGBoost), gradient boosting regression (GBR), random forest regression (RFR), decision tree regression (DTR), support vector regression (SVR), symbolic regression (SR), and artificial neural networks (ANNs and proposed ANN-GMDH). Symbolic regression equations have been developed to predict the elasticity modulus and unconfined compressive strength of the investigated expansive soil. Despite the complex behaviors of expansive soil, the trained models allow for optimally predicting the values of unconfined compressive parameters. As a result, this paper provides for the first time a reliable and simply applicable approach for estimating the unconfined compressive parameters of expansive soils. The proposed ANN-GMDH model emerges as the pre-eminent model, demonstrating exceptional accuracy with the best metrics. These results not only highlight the ANN’s superior performance but also mark this study as a groundbreaking endeavor in the application of machine learning to soil behavior prediction, setting a new benchmark in the field.

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Section: Sand As An Additive Materials and Its Impact On Soil Parametersmentioning

confidence: 99%

Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

Alnmr,

Hosamo,

Lyu

et al. 2024

Applied Sciences

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“…The HS model provides a wide range of defining parameters, which can be based on laboratory testing or back-calculated from field experience. It demonstrated efficiency in simulating soil behavior, encompassing both soft and stiff soils, and was extensively utilized in various research studies [61][62][63][64].…”

Section: Plaxis Validationmentioning

confidence: 99%

“…The set of parameters entered in the HS model allows the user to distinguish between loading and unloading-reloading stiffnesses. This capability enhances the model's ability to capture the soil's response more accurately to various loading conditions and its complex behavior under different stress states [64].…”

mentioning

confidence: 98%

Comparative Analysis of Helical Piles and Granular Anchor Piles for Foundation Stabilization in Expansive Soil: A 3D Numerical Study

2023

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This study investigates the performance of granular anchor piles and helical piles in expansive soils. Expansive soils pose challenges for engineering due to their significant swelling and shrinkage characteristics. Special considerations are required for constructing foundations on expansive soil to mitigate volumetric changes. While helical piles provide uplift resistance in light structures, they may not fully stabilize foundations in expansive soils. In contrast, granular anchor piles offer a simpler alternative for resisting uplift forces. A numerical study was conducted to analyze the pullout loads, compressive loads, and heave behavior of these anchor techniques. The results demonstrate that granular anchor piles outperform helical piles in terms of pullout and compressive performance, with improvements ranging from 17% to 22.5% in pullout capacity and 0.5% to 19% in compressive capacity, depending on specific pile lengths and diameters examined. However, both techniques show similar effectiveness in reducing heave, achieving reductions of over 90% when specific conditions are met. Additionally, the use of high-rise cap piles contributes to significant heave reduction, effectively minimizing heave to nearly negligible levels compared to low-rise cap piles. It is found that the relative density of the granular material has a more pronounced effect on the pullout load compared to the compressive load, and its impact varies depending on the length of the pile. Therefore, it is recommended to avoid high relative density when the pile is entirely within the expansive soil while utilizing higher relative density is beneficial when the pile penetrates and settles in the stable zone.

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“…The first crucial step in geotechnical numerical modeling is selecting and calibrating a robust and accurate soil material model (i.e., constitutive model). A less precise model may yield poor results and make estimating the proper behavior in the field more complex [24].…”

Section: Appropriate Constitutive Modelmentioning

confidence: 99%

Two-Dimensional Numerical Analysis for TBM Tunneling-Induced Structure Settlement: A Proposed Modeling Method and Parametric Study

2023

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The construction of tunnels in densely populated urban areas poses a significant challenge in terms of anticipating the settlement that may result from tunnel excavation. This paper presents a new and more realistic modeling method for tunnel excavation using a Tunnel Boring Machine (TBM). This method is compared with other reference modeling methods using a validated model of a subsurface tunnel excavated by a TBM with a slurry shield. A parametric study is conducted to investigate the impact of key parameters, including structure width, foundation depth, eccentricity, load on the structure, overburden depth, and tunnel diameter, on tunnel–soil–structure interaction and the resulting structure settlements. The results reveal that the tunnel diameter, eccentricity, and overburden depth have a significant impact on structure settlements, with values of 22.5%, 17%, and 7.1%, respectively. Finally, the paper proposes an equation for predicting the maximum settlement of a structure, considering the critical parameters. The validity of the equation is evaluated by comparing its results with the outputs from various case studies, including a newly validated model, two real-life case studies, and centrifuge tests. The results indicate a high level of consistency between the calculated and measured settlements.

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Material Models to Study the Effect of Fines in Sandy Soils Based on Experimental and Numerical Results

Cited by 10 publications

References 21 publications

Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

Novel Insights in Soil Mechanics: Integrating Experimental Investigation with Machine Learning for Unconfined Compression Parameter Prediction of Expansive Soil

Comparative Analysis of Helical Piles and Granular Anchor Piles for Foundation Stabilization in Expansive Soil: A 3D Numerical Study

Two-Dimensional Numerical Analysis for TBM Tunneling-Induced Structure Settlement: A Proposed Modeling Method and Parametric Study

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