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

Baghbani, Abolfazl; Nguyen, Minh Duc; Alnedawi, Ali; Milne, Nick; Baumgartl, Thomas; Abuel-Naga, Hossam

doi:10.3390/app13084934

Cited by 14 publications

(4 citation statements)

References 80 publications

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“…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

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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.

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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

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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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“…A method that was successfully used in various fields of geotechnical engineering and has shown acceptable results is artificial intelligence [45]. Over the past two decades, artificial intelligence methods have been applied successfully in a wide range of geotechnical engineering disciplines, including soil dynamics [46][47][48], slope stability [49][50][51][52], fracture mechanics [53][54][55][56], rock and tunnel mechanics [57][58][59][60][61][62], among others. However, no studies have been published on the application of artificial intelligence methods to predict the friction angle of sand-rubber mixtures.…”

Section: Introductionmentioning

confidence: 99%

Internal Friction Angle of Cohesionless Binary Mixture Sand–Granular Rubber Using Experimental Study and Machine Learning

et al. 2023

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This study aimed to examine the shear strength characteristics of sand–granular rubber mixtures in direct shear tests. Two different sizes of rubber and one of sand were used in the experiment, with the sand being mixed with various percentages of rubber (0%, 10%, 20%, 30%, and 50%). The mixtures were prepared at three different densities (loose, slightly dense, and dense), and shear stress was tested at four normal stresses (30, 55, 105, and 200 kPa). The results of 80 direct shear tests were used to calculate the peak and residual internal friction angles of the mixtures, and it was found that the normal stress had a significant effect on the internal friction angle, with an increase in normal stress leading to a decrease in the internal friction angle. These results indicated that the Mohr–Coulomb theory, which applies to rigid particles only, is not applicable in sand–rubber mixtures, where stiff particles (sand) and soft particles (rubber) are mixed. The shear strength of the mixtures was also influenced by multiple factors, including particle morphology (size ratio, shape, and gradation), mixture density, and normal stress. For the first time in the literature, genetic programming, classification and regression random forests, and multiple linear regression were used to predict the peak and residual internal friction angles. The genetic programming resulted in the creation of two new equations based on mixture unit weight, normal stress, and rubber content. Both artificial intelligence models were found to be capable of accurately predicting the peak and residual internal friction angles of sand–rubber mixtures.

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

Cited by 14 publications

References 80 publications

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

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

Internal Friction Angle of Cohesionless Binary Mixture Sand–Granular Rubber Using Experimental Study and Machine Learning

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