Ultrasonic Characterization of Compacted Salty Kaolin–Sand Mixtures Under Nearly Zero Vertical Stress Using Experimental Study and Machine Learning

Baghbani, Abolfazl; Abuel-Naga, Hossam; Faradonbeh, Roohollah Shirani; Costa, Susanga; Almasoudi, Rayed

doi:10.1007/s10706-023-02441-5

Cited by 13 publications

(6 citation statements)

References 70 publications

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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

Baghbani¹,

Nguyen²,

Alnedawi³

et al. 2023

Preprint

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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 paper explores the use of artificial intelligence (AI) methods to predict 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 showed that all three AI models were able to predict CBR with good accuracy, with coefficient of determination (R2) values ranging from 0.94 to 0.99 and mean absolute error (MAE) values ranging from 0.30 to 0.51. In a novel approach, the genetic programming method was used to produce an equation to estimate CBR, which included seven inputs and accurately predicted CBR. The analysis of sensitivity and importance of parameters showed 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 suggests that AI methods can effectively predict the performance of alum sludge as a soil stabilizer, and the proposed equation using genetic programming can be a useful tool for predicting CBR.

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Section: Introductionmentioning

confidence: 99%

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

Baghbani¹,

Nguyen²,

Alnedawi³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

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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“…Based on AI methods, it is possible to predict the output with high accuracy without knowing the relationship between the parameters in advance [14]. In the last two decades, AI methods were used in geotechnical engineering applications include slope stability [15][16][17], tunnelling [18][19], road construction [20][21], and soil cracking [22][23], soil dynamics [24][25][26] and recycled material [27][28][29][30][31]. There has not yet been an article published on artificial intelligence methods for determining the strength of RGP and soil mixtures based on different input parameters.…”

Section: Introductionmentioning

confidence: 99%

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

Baghbani¹

2023

Advanced Topics in Mechanics of Materials, Structures and Construction

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Abstract. The paper investigates the use of artificial intelligence (AI) methods to predict the strength of recycled glass powder (RGP) and soil mixtures based on different input parameters. The study utilized a database of 57 sets with 5 inputs, including RGP percentage, ordinary Portland cement (OPC) percentage, molar concentration, curing temperature and time, and one output, mixed UCS. There were two artificial intelligence models used in this study, a support vector machines (SVM) and classification and regression random forest (CRRF). The results demonstrate the potential of RGP-based geopolymers to improve the mechanical behavior of clay soils, and the use of AI methods to predict the strength of RGP and soil mixtures with high accuracy. Using SVM model, the testing dataset had a mean absolute error (MAE) and R2 of 0.072 and 0.978, respectively. Also, CRRF had an accurate performance with a MAE of 0.075 and the R2 of 0.979. These results suggest that the AI models fits well with the data. Also, by analyzing the results of the SVM and CRRF models, it is found that curing time is the most important input parameter, while RGP and OPC are the least significant.

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Ultrasonic Characterization of Compacted Salty Kaolin–Sand Mixtures Under Nearly Zero Vertical Stress Using Experimental Study and Machine Learning

Cited by 13 publications

References 70 publications

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

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

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

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

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