Experimental study on cyclic simple shear behaviour of predominantly dilative silica sand

Baghbani, Abolfazl; Costa, Susanga; O’Kelly, Brendan C.; Soltani, Amin; Barzegar, Milad

doi:10.1080/19386362.2022.2135226

Cited by 12 publications

(10 citation statements)

References 37 publications

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“…According to Wichtmann et al [24], the damping ratio of sand decreases with increasing uniformity ratio. In general, several factors can affect the damping ratio of sands, including particle shape, grain size, confining pressure, moisture content, loading frequency, and particle distribution [25][26][27][28][29][30]. The complex relationship between these parameters and the damping ratio of sand requires further research.…”

Section: Introductionmentioning

confidence: 99%

Experimental-AI Investigation of the Effect of Particle Shape on the Damping Ratio of Dry Sand under Simple Shear Test Loading

Baghbani¹,

Costa²,

Faradonbeh³

et al. 2023

Preprint

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This paper reports on a series of dynamic simple shear tests conducted to investigate the influence of particle shape on the damping ratio of dry sand. The tests were conducted on sand samples subjected to simple cyclic shear tests to evaluate their cyclic behavior. The particle shape was quantified using three shape parameters: roundness, sphericity, and regularity. The sand samples were subjected to twelve different scenarios with varying vertical stresses and cyclic stress ratios (CSR), in both constant and controlled stress states. Each scenario involved five cyclic tests, using the same sand that was reconstructed from its previous cyclic test. After each cyclic test, hysteresis loops were created to determine the damping ratio. The results showed that the shape of the sand particles changed during cyclic loading, becoming more rounded and spherical, which resulted in an increase in damping ratio. Moreover, the paper presents two artificial intelligence models, an artificial neural network (ANN) and a support vector machine (SVM), which were developed to predict the effect of grain shape on the damping ratio. The models were found to be effective in predicting the damping ratio based on the shape of the grain, vertical stress, CSR, and number of loading cycles. Furthermore, a parameter analysis was conducted to identify the most important shape parameter, which was found to be vertical stress and regularity, while parameter CSR was the least important. Overall, this study contributes to a better understanding of the relationship between particle shape and damping ratio, which could have practical implications for geotechnical engineering applications.

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Section: Introductionmentioning

confidence: 99%

Experimental-AI Investigation of the Effect of Particle Shape on the Damping Ratio of Dry Sand under Simple Shear Test Loading

Baghbani¹,

Costa²,

Faradonbeh³

et al. 2023

Preprint

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show abstract

“…Machine learning has been widely applied in many fields of science and engineering, including geotechnical engineering. Several studies have demonstrated the effectiveness of machine learning in predicting various properties in geotechnics, such as soil dynamics [36][37][38][39][40][41][42][43][44], slope stability, and soil cracking [45][46][47][48][49][50][51][52]. A comprehensive study has not yet been presented on the use of artificial intelligence models to predict the thermal conductivity of sand.…”

Section: Introductionmentioning

confidence: 99%

Accurately Predicting Quartz Sand Thermal Conductivity Using Machine Learning and Grey-Box AI Models

2023

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The thermal conductivity of materials is a crucial property with diverse applications, particularly in engineering. Understanding soil thermal conductivity is crucial for designing efficient geothermal systems, predicting soil temperatures, and assessing soil contamination. This paper aimed to predict quartz sand thermal conductivity by using four mathematical models: multiple linear regression (MLR), artificial neural network (ANN), classification and regression random forest (CRRF), and genetic programming (GP). A grey-box AI method, GP, was used for the first time in this topic. Seven inputs affecting thermal conductivity were evaluated in the study, including sand porosity, degree of saturation, coefficient of uniformity, coefficient of curvature, mean particle size, and minimum and maximum void ratios. In predicting thermal conductivity, the MLR model performed poorly, with a coefficient of determination R2 = 0.737 and a mean absolute error MAE = 0.300. Both ANN models using the Levenberg–Marquardt algorithm and the Bayesian Regularization (BR) algorithm outperformed the MLR model with an accuracy of R2 = 0.916 and an error of MAE = 0.151. In addition, the CRRF model had the best accuracy of R2 = 0.993 and MAE = 0.045. In addition, GP showed acceptable performance in predicting sand thermal conductivity. The R2 and MAE values of GP were 0.986 and 0.063, respectively. This paper presents the best GP equation for evaluating other databases. Additionally, the porosity and saturation of the sand were found to have the greatest impact on the model results, while coefficients of curvature and uniformity had the least influence. Overall, the results of this study demonstrate that grey-box artificial intelligence models can be used to accurately predict quartz sand thermal conductivity.

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“…Using artificial intelligence techniques, it is possible to determine the relationship between different parameters with a high degree of accuracy, without prior knowledge. Various topics in geotechnical engineering, such as slope stability [25][26][27], tunneling [28][29][30], pavement and road construction [31,32], soil cracking [33][34][35], rock mechanics [36,37], soil dynamics [38][39][40][41], and soil stabilizers [42][43][44] have been addressed using artificial intelligence methods [45]. Nevertheless, only two studies have used artificial intelligence to predict the properties arising from mixing sludge with soil [46,47].…”

Section: Introductionmentioning

confidence: 99%

Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio

et al. 2023

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Alum sludge is a byproduct of water treatment plants, and its use as a soil stabilizer has gained increasing attention due to its economic and environmental benefits. Its application has been shown to improve the strength and stability of soil, making it suitable for various engineering applications. However, to go beyond just measuring the effects of alum sludge as a soil stabilizer, this study investigates the potential of artificial intelligence (AI) methods for predicting the California bearing ratio (CBR) of soils stabilized with alum sludge. Three AI methods, including two black box methods (artificial neural network and support vector machines) and one grey box method (genetic programming), were used to predict CBR, based on a database with nine input parameters. The results demonstrate the effectiveness of AI methods in predicting CBR with good accuracy (R2 values ranging from 0.94 to 0.99 and MAE values ranging from 0.30 to 0.51). Moreover, a novel approach, using genetic programming, produced an equation that accurately estimated CBR, incorporating seven inputs. The analysis of parameter sensitivity and importance, revealed that the number of hammer blows for compaction was the most important parameter, while the parameters for maximum dry density of soil and mixture were the least important. This study highlights the potential of AI methods as a useful tool for predicting the performance of alum sludge as a soil stabilizer.

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Experimental study on cyclic simple shear behaviour of predominantly dilative silica sand

Cited by 12 publications

References 37 publications

Experimental-AI Investigation of the Effect of Particle Shape on the Damping Ratio of Dry Sand under Simple Shear Test Loading

Experimental-AI Investigation of the Effect of Particle Shape on the Damping Ratio of Dry Sand under Simple Shear Test Loading

Accurately Predicting Quartz Sand Thermal Conductivity Using Machine Learning and Grey-Box AI Models

Improving Soil Stability with Alum Sludge: An AI-Enabled Approach for Accurate Prediction of California Bearing Ratio

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