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Exploring the Potential of Machine Learning in Predicting Soil California Bearing Ratio Values

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Titel: Exploring the Potential of Machine Learning in Predicting Soil California Bearing Ratio Values
Autoren: Wu, Xu, Lu, Feng, He, Tao
Quelle: Periodica Polytechnica Civil Engineering; Vol. 69 No. 2 (2025); 551-566 ; 1587-3773 ; 0553-6626
Verlagsinformationen: Budapest University of Technology and Economics
Publikationsjahr: 2025
Bestand: Periodica Polytechnica (Budapest University of Technology and Economics)
Schlagwörter: regression analysis, California bearing ratio, stochastic gradient boosting regression, self-adaptive bonobo optimization algorithm, improved arithmetic optimization algorithm
Beschreibung: Accurately predicting the California Bearing Ratio (CBR) of soil is vital for civil engineering projects as it determines soil strength and stability, crucial for designing safe and durable infrastructure. Conventional methods for calculating CBR values are both expensive and time-consuming, prompting the need for more efficient approaches. This study explores the use of advanced machine learning (ML) techniques to improve workflow and productivity in CBR prediction. Specifically, the Improved Arithmetic Optimization Algorithm (IAOA) and the Bonobo Optimization Algorithm (SBOA) are applied to enhance the Stochastic Gradient Boosting Regression (SGBR) model for predicting CBR values. The SGBR model, known for its ability to handle complex datasets and nonlinear interactions, is optimized to improve predictive accuracy. Performance metrics such as the coefficient of determination (R2), n10-index, and Root Mean Squared Error (RMSE) are used to assess the model's performance. After training, testing, and validation with relevant data, the optimized SGIA model (SGBR enhanced by IAOA) achieves impressive results, including an n10-index of 1.000, a root mean square error of 0.161, and an R2 value of 0.981. These metrics demonstrate the SGIA model's capability to accurately forecast CBR values, offering a reliable, cost-effective alternative to traditional methods for soil evaluation in engineering applications.
Publikationsart: article in journal/newspaper
Dateibeschreibung: application/pdf
Sprache: English
Relation: https://pp.bme.hu/ci/article/view/38678/23781; https://pp.bme.hu/ci/article/view/38678
Verfügbarkeit: https://pp.bme.hu/ci/article/view/38678
Rights: Copyright (c) 2025 Periodica Polytechnica Civil Engineering
Dokumentencode: edsbas.40FE21D3
Datenbank: BASE
Beschreibung
Abstract:Accurately predicting the California Bearing Ratio (CBR) of soil is vital for civil engineering projects as it determines soil strength and stability, crucial for designing safe and durable infrastructure. Conventional methods for calculating CBR values are both expensive and time-consuming, prompting the need for more efficient approaches. This study explores the use of advanced machine learning (ML) techniques to improve workflow and productivity in CBR prediction. Specifically, the Improved Arithmetic Optimization Algorithm (IAOA) and the Bonobo Optimization Algorithm (SBOA) are applied to enhance the Stochastic Gradient Boosting Regression (SGBR) model for predicting CBR values. The SGBR model, known for its ability to handle complex datasets and nonlinear interactions, is optimized to improve predictive accuracy. Performance metrics such as the coefficient of determination (R2), n10-index, and Root Mean Squared Error (RMSE) are used to assess the model's performance. After training, testing, and validation with relevant data, the optimized SGIA model (SGBR enhanced by IAOA) achieves impressive results, including an n10-index of 1.000, a root mean square error of 0.161, and an R2 value of 0.981. These metrics demonstrate the SGIA model's capability to accurately forecast CBR values, offering a reliable, cost-effective alternative to traditional methods for soil evaluation in engineering applications.