Effects of particle shape on shear modulus of sand using dynamic simple shear testing

Baghbani, Abolfazl; Costa, Susanga; Lu, Youqian; Soltani, Amin; Abuel-Naga, Hossam; Samui, Pijush

doi:10.1007/s12517-023-11524-9

Cited by 7 publications

(4 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By employing these realistic particle shapes, the simulation offers improved accuracy compared with traditional spherical particles. Baghbani et al [29] investigated the effects of particle shape on the secant shear modulus of dry sand through dynamic simple shear testing and indicated that the sand had a dilative behavior, and successive cyclic loading negatively affected the shape of the sand particles. This paper does not delve extensively into the effects of rubber particle and sand particle shapes on the simulation outcomes, because the rubber particles employed for road filling predominantly comprise discarded tire byproducts and, as such, do not possess tailored shapes.…”

Section: Generation Of Subgrade Sample Of Gravels and Rubber Mixturesmentioning

confidence: 99%

A Dynamic Assessment of Rubber–Sand Mixtures as Subgrade Materials during Vibratory Roller Compaction through DEM Simulation in 2D

Sun,

Xie,

et al. 2023

Sustainability

View full text Add to dashboard Cite

The accumulation of discarded tire rubber poses significant challenges in terms of land usage and environmental hazards. To address this issue, this article explores the potential reuse of rubber in roadbed engineering. This study conducts a comprehensive examination of the vibration compaction process involving a vibratory roller and rubber–sand mixtures, utilizing the discrete element method (DEM) in a two-dimensional (2D) framework to investigate the impact of dynamic vibration compaction on sand mixtures with varying rubber contents under different roller working conditions, while also evaluating the associated energy consumption. The results reveal that both the rubber content and operational parameters of the roller significantly influence compaction vibration effects. Notably, optimal rolling frequency, velocity, and rolling mass show correlations with the rubber content. Furthermore, this research provides a microscopic understanding of the compaction process, offering detailed insights into displacement fields, velocity fields, and contact forces.

show abstract

Section: Generation Of Subgrade Sample Of Gravels and Rubber Mixturesmentioning

confidence: 99%

A Dynamic Assessment of Rubber–Sand Mixtures as Subgrade Materials during Vibratory Roller Compaction through DEM Simulation in 2D

Sun,

Xie,

et al. 2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…The realm of geotechnical engineering, while somewhat reserved in its adoption of AI, has begun to witness the application of AI-based techniques in addressing complex challenges. AI methods such as artificial neural networks (ANNs), fuzzy inference systems (FISs), adaptive neuro-fuzzy inference systems (ANFISs), and others have shown remarkable potential in deciphering intricate relationships within complex datasets across diverse domains such as soil dynamics [15][16][17][18][19][20], deep foundations [21][22][23][24], soil cracking [25][26][27], recycled materials [28][29][30][31][32][33][34][35][36], soil mechanics [37,38], tunnelling and rock mechanics [39][40][41] and other fields [42][43][44][45][46][47][48][49][50][51]. The beauty of these techniques lies in their capacity to capture nonlinear interactions between a myriad of variables, even when the underlying relationships are not fully understood.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Ultimate Bearing Capacity Prediction of Cohesionless Soils Beneath Shallow Foundations with Grey Box and Hybrid AI Models

Kiany,

Baghbani,

Abuel-Naga

et al. 2023

Algorithms

Self Cite

View full text Add to dashboard Cite

This study examines the potential of the soft computing technique, namely, multiple linear regression (MLR), genetic programming (GP), classification and regression trees (CART) and GA-ENN (genetic algorithm-emotional neuron network), to predict the ultimate bearing capacity (UBC) of cohesionless soils beneath shallow foundations. For the first time, two grey-box AI models, GP and CART, and one hybrid AI model, GA-ENN, were used in the literature to predict UBC. The inputs of the model are the width of footing (B), depth of footing (D), footing geometry (ratio of length to width, L/B), unit weight of sand (γd or γ′), and internal friction angle (ϕ). The results of the present model were compared with those obtained via two theoretical approaches and one AI approach reported in the literature. The statistical evaluation of results shows that the presently applied paradigm is better than the theoretical approaches and is competing well for the prediction of qu. This study shows that the developed AI models are a robust model for the qu prediction of shallow foundations on cohesionless soil. Sensitivity analysis was also carried out to determine the effect of each input parameter. The findings showed that the width and depth of the foundation and unit weight of soil (γd or γ′) played the most significant roles, while the internal friction angle and L/B showed less importance in predicting qu.

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Effects of particle shape on shear modulus of sand using dynamic simple shear testing

Cited by 7 publications

References 48 publications

A Dynamic Assessment of Rubber–Sand Mixtures as Subgrade Materials during Vibratory Roller Compaction through DEM Simulation in 2D

A Dynamic Assessment of Rubber–Sand Mixtures as Subgrade Materials during Vibratory Roller Compaction through DEM Simulation in 2D

Enhancing Ultimate Bearing Capacity Prediction of Cohesionless Soils Beneath Shallow Foundations with Grey Box and Hybrid AI Models

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

Contact Info

Product

Resources

About