Predicting the strength of recycled glass powder-based geopolymers for improving mechanical behavior of clay soils using artificial intelligence

Baghbani, A.

doi:10.21741/9781644902592-66

Cited by 8 publications

(3 citation statements)

References 27 publications

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“…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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Section: Introductionmentioning

confidence: 99%

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

2023

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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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“…Artificial intelligence has been used as a method of solving this problem. A number of fields, including soil mechanics [12][13][14], soil dynamics [15][16], soil cracking [17][18], road construction [19][20], recycled material [21][22][23][24] and slope stability [25] have successfully used artificial intelligence methods [26][27]. In spite of this, no study has yet been conducted to investigate the use of artificial intelligence methods in predicting the resilient modulus of two soil-of polyethylene (PE) bottles and polypropylene (PP) mixtures.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-based prediction of geotechnical impacts of polyethylene bottles and polypropylene on clayey soil

Baghbani¹

2023

Advanced Topics in Mechanics of Materials, Structures and Construction

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Abstract. This study aims to investigate the application of artificial intelligence (AI) methods in predicting the resilient modulus of soil mixtures with polyethylene (PE) bottles and polypropylene (PP). The AI methods used in the study are artificial neural network (ANN) and classification and regression random forest (CRRF), and the modeling was conducted using a database of 160 datasets. The study also evaluated the importance of different input parameters on the accuracy of the models. The results show that the CRRF model is more accurate than the ANN model in predicting the effects of materials PE and PP on soil resilient modulus. Additionally, the study found that the number of hidden layers and neurons in the ANN model should be optimized for the best performance and increasing their number does not always lead to increased accuracy. Finally, the study identified the most and least important input parameters for predicting the effect of PE and PP on the resilient modulus of the mixture using both AI models.

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Predicting the strength of recycled glass powder-based geopolymers for improving mechanical behavior of clay soils using artificial intelligence

Cited by 8 publications

References 27 publications

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

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

Artificial intelligence-based prediction of geotechnical impacts of polyethylene bottles and polypropylene on clayey soil

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