The Undrained Shear Behavior of Reinforced Clayey Sand

Talamkhani, Saeed; Naeini, Seyed Abolhasan

doi:10.1007/s10706-020-01490-4

Cited by 11 publications

(4 citation statements)

References 46 publications

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“…In order to establish the efectiveness and dependability of the algorithms, a reliability analysis based on the Friedman analysis is performed [17]. According to this method, the models are ranked according to their errors in their predictions, from 1 indicating the least error to z indicating the highest error, for z models.…”

Section: Reliability Analysismentioning

confidence: 99%

“…Parallel algorithms run several learners simultaneously and then calculate the fnal prediction from all independent learners. Among the parallel ensemble methods, random forest is being used more frequently in engineering and geotechnical problems [16][17][18]. On the other hand, a sequential process (also known as boosting) builds base estimators sequentially and attempts to reduce the bias of the combined estimator at each iteration.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning-Based Prediction of Unconfined Compressive Strength of Sands Treated by Microbially-Induced Calcite Precipitation (MICP): A Gradient Boosting Approach and Correlation Analysis

Talamkhani

2023

Advances in Civil Engineering

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The current study applies a soft-computing approach based on the gradient boosting method to predict the unconfined compressive strength (UCS) of sands treated with microbially-induced calcite precipitation (MICP). A 10-fold cross-validation method and hyperparameter tuning are performed to find the optimal architecture of the gradient boosting algorithm. A total of 402 data of unconfined compression tests performed on biocemented sands are utilized in this study. The dataset includes eight input parameters: median sand particle size, uniformity coefficient of sand, initial void ratio, calcium chloride concentration, urea concentration, urease activity, optical density of bacteria, and calcite content. The finding demonstrates that the gradient boosting method outperformed five commonly used machine learning algorithms (artificial neural networks, random forests, k-nearest neighbors, support vector regression, and decision trees) in predicting the UCS of biocemented sands. Using the gradient boosting, the predicted UCS has a strong correlation with the actual values (R2 = 0.95). Moreover, a series of correlation and feature importance analyses are carried out over the dataset. The relationships between unconfined compressive strength, calcite content, and initial void ratio are discussed within the article. Furthermore, some guidelines are provided for assessing the effect of environmental factors on the UCS of biocemented sands. For further study, the limitations of this study regarding the insufficiency of data for correlation and environmental modification are addressed.

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Section: Reliability Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Prediction of Unconfined Compressive Strength of Sands Treated by Microbially-Induced Calcite Precipitation (MICP): A Gradient Boosting Approach and Correlation Analysis

Talamkhani

2023

Advances in Civil Engineering

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“…Talamkhani and Naeini 10 reported that for clay content being less than 10%, the undrained shear strength of reinforced and unreinforced clayey sand decreases with an increase in clay content; the undrained shear strength grows as clay content continues to rise. Belhouari et al 11 indicated that with an increase in fine content, the resistance of the soil weakens and the values of the critical state parameter fall, resulting in a decrease in the critical undrained shear strength of terrigenous silica sands 5, 7, 12, 13 .…”

Section: Introductionmentioning

confidence: 99%

Mechanical properties of coarse-fine particle mixtures: a laboratory investigation of the effect of fine content

Chang

Wang

2023

Preprint

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The impacts of fine content on the microstructure and mechanical behavior of five types of coarse-fine particle mixtures with different fine contents are examined based on laboratory tests. The samples used in these tests all had a void ratio of 0.6. As the fine content decreases, the pore-size distribution of the soil changes from unimodal to bimodal, and the variation rate of the initial elastic modulus with confining pressure becomes great. If the fine content is greater than 71.43%, the pore volume of the mixtures reduces progressively progressively with decreasing the fine content, resulting in the growth of peak deviator stress, cohesiveness, and internal friction angle. If the concentration of the fine grains is less than 71.43%, the pore volume of the mixtures shows an increasing trend, which causes a decrease in peak deviator stress,cohesiveness, and internal friction angle.

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“…Several other applications of the CSSM framework are available in the literature. For instance, studies on effects of frictional angle, particle breakage, and deformation on the CS properties of soils (De Bono, 2018;Kang, 2019;Nie, 2022;Taiba, 2023;Wang, 2020;Yang, 2018;Yu 2017); effects of fabric anisotropy vs isotropy (Kazem, 2022;Kolapalli et al 2023;Papadimitriou, 2019;Rahman & Dafalias 2022); the role of principal stress rotation on the critical state line Kazem, 2022); the influence of polymer on the critical state of sand (Liu, 2020), and the impact of plastic and non-plastic fines on the critical state of sand (e.g., Mahmoudi et al 2018;Papadopoulou & Tika 2008Rahman & Dafalias 2022;Talamkhani & Naeini 2018). In the domain of numerical modeling of soils, the CSSM has also proved to be accurate in predicting a wide range of soil engineering characteristics.…”

Section: Introductionmentioning

confidence: 99%

The critical state characterization of east coast sand (ECS) and calibration for use with an advanced critical state soil constitutive model

Bolarinwa

Kalatehjari

Safuan

2022

Preprint

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This paper outlines the findings of a laboratory-based study to investigate the properties and undrained flow failure behavior of East Coast Sand (ECS). ECS is a commonly encountered coastal deposit from the upper North Island of New Zealand. This work aims to derive soil parameters for an advanced constitutive soil model (NORSAND) to enable predictive analyses of flow failure potential using the critical state framework. The emphasis in the study is placed on the undrained strength behavior under static, triaxial compressive loading conditions at confining pressures in the range of typical engineering interest and for a range of soil densities considered in loosely deposited sands common to nearshore marine and estuarine environments. The research objective of establishing the basic soil properties and intrinsic advanced geomechanical properties of ECS from Auckland was achieved through laboratory experiments and matching simulations for five different aspects of its undrained behavior under static loads. It was shown that loosely deposited ECS within mean effective stresses ranging between 50kPa and 200kPa can be susceptible to expensive flow failures of structures built on or with them. Therefore, the re-engineering of ECS by adequate soil improvement is required when used in earthworks.

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The Undrained Shear Behavior of Reinforced Clayey Sand

Cited by 11 publications

References 46 publications

Machine Learning-Based Prediction of Unconfined Compressive Strength of Sands Treated by Microbially-Induced Calcite Precipitation (MICP): A Gradient Boosting Approach and Correlation Analysis

Machine Learning-Based Prediction of Unconfined Compressive Strength of Sands Treated by Microbially-Induced Calcite Precipitation (MICP): A Gradient Boosting Approach and Correlation Analysis

Mechanical properties of coarse-fine particle mixtures: a laboratory investigation of the effect of fine content

The critical state characterization of east coast sand (ECS) and calibration for use with an advanced critical state soil constitutive model

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