The strength and stability of pavement subgrades are heavily influenced by the geotechnical properties of underlying soils, which can exhibit high variability in regions with complex soil compositions, such as lateritic–lithomargic subgrades. This study introduces a novel approach by employing machine learning (ML) models to predict the California bearing ratio (CBR) of lateritic–lithomargic subgrade soils found in Karavali Karnataka regions of southern peninsular India. The primary novelty of this research lies in leveraging ML techniques to analyze soil characteristics and develop predictive ML models for CBR, reducing the reliance on extensive and time-consuming laboratory testing. Soil samples for this study were collected from 20 different locations at the low-volume road junctions along the stretch of KAR-SH-1 to ensure diversity and representativeness of the soil and were subjected to a series of tests to determine basic geotechnical properties (gradation, plasticity index, specific gravity, and compaction) and soaked CBR value. The data pertaining to tests performed on soil samples for 80% of the locations in the region were used to develop the ML models, while data on the remaining 20% locations were used in validating the same. Finally, the models with acceptable level of statistical results were given higher weightage, where the regressors gave the best performance metrics to the independent models with lower error rate compared to the other models. This study applies multiple ML algorithms, including multiple linear regression (MLR), decision tree (DT), random forest (RF), support vector machine (SVM), AdaBoost, and gradient boosting regressor (GBR), to identify the relationships between these soil parameters and CBR values. The modeling pipeline incorporated standardized pre-processing, k-fold cross-validation, and advanced performance metrics including MAE, MSE, RMSE, R2, CV-Mean a20-index, a10-index, performance index (PI), improvement assessment (IA), and objective function (OBJ). The key results show that the AdaBoost and GBR models outperformed others in prediction accuracy, with AdaBoost achieving the lowest RMSE (0.378284) and the highest R2 score (0.952), with satisfactory other key-results, indicating a robust model for practical use in pavement applications. The ML models successfully identified key soil properties that correlate with CBR, facilitating more efficient and accurate pavement subgrade evaluation. Overall, this study highlights the potential of ML-based approaches to streamline the design of pavements and embankments, particularly in regions with lateritic–lithomargic soils, by providing sustainable, rapid, cost-effective, and data-driven solutions for the highway construction industry. Graphical Abstract

Machine-Learning-Based Prediction of California Bearing Ratio (CBR) for Lateritic–Lithomargic Pavement Subgrades through the Major Index Properties

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2025年7月5日

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Shriram Marathe,
Akhila Sheshadri

摘要

The strength and stability of pavement subgrades are heavily influenced by the geotechnical properties of underlying soils, which can exhibit high variability in regions with complex soil compositions, such as lateritic–lithomargic subgrades. This study introduces a novel approach by employing machine learning (ML) models to predict the California bearing ratio (CBR) of lateritic–lithomargic subgrade soils found in Karavali Karnataka regions of southern peninsular India. The primary novelty of this research lies in leveraging ML techniques to analyze soil characteristics and develop predictive ML models for CBR, reducing the reliance on extensive and time-consuming laboratory testing. Soil samples for this study were collected from 20 different locations at the low-volume road junctions along the stretch of KAR-SH-1 to ensure diversity and representativeness of the soil and were subjected to a series of tests to determine basic geotechnical properties (gradation, plasticity index, specific gravity, and compaction) and soaked CBR value. The data pertaining to tests performed on soil samples for 80% of the locations in the region were used to develop the ML models, while data on the remaining 20% locations were used in validating the same. Finally, the models with acceptable level of statistical results were given higher weightage, where the regressors gave the best performance metrics to the independent models with lower error rate compared to the other models. This study applies multiple ML algorithms, including multiple linear regression (MLR), decision tree (DT), random forest (RF), support vector machine (SVM), AdaBoost, and gradient boosting regressor (GBR), to identify the relationships between these soil parameters and CBR values. The modeling pipeline incorporated standardized pre-processing, k-fold cross-validation, and advanced performance metrics including MAE, MSE, RMSE, R², CV-Mean a20-index, a10-index, performance index (PI), improvement assessment (IA), and objective function (OBJ). The key results show that the AdaBoost and GBR models outperformed others in prediction accuracy, with AdaBoost achieving the lowest RMSE (0.378284) and the highest R² score (0.952), with satisfactory other key-results, indicating a robust model for practical use in pavement applications. The ML models successfully identified key soil properties that correlate with CBR, facilitating more efficient and accurate pavement subgrade evaluation. Overall, this study highlights the potential of ML-based approaches to streamline the design of pavements and embankments, particularly in regions with lateritic–lithomargic soils, by providing sustainable, rapid, cost-effective, and data-driven solutions for the highway construction industry.

Graphical Abstract