Prediction of California Bearing Ratio from Index Properties of Soils Using Parametric and Non-parametric Models

Farias, Isabel González; Araujo, William; Ruiz, Gaby

doi:10.1007/s10706-018-0548-1

Cited by 32 publications

(3 citation statements)

References 6 publications

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“…The CBR test was originally introduced by the California Highway Department, during World War-II, and was subsequently adopted widely as a standard method for soil strength and bearing capacity evaluation [8], obtained using either ASTM Standard D-1883-05 or BS 1377 [9]. Laboratory tests are performed on compacted soil samples with OMC in un-soaked and soaked conditions, and they can also be carried out on natural soils.…”

Section: Introductionmentioning

confidence: 99%

On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils

et al. 2021

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The California Bearing Ratio (CBR) is an important index for evaluating the bearing capacity of pavement subgrade materials. In this research, random subspace optimization-based hybrid computing models were trained and developed for the prediction of the CBR of soil. Three models were developed, namely reduced error pruning trees (REPTs), random subsurface-based REPT (RSS-REPT), and RSS-based extra tree (RSS-ET). An experimental database was compiled from a total of 214 soil samples, which were classified according to AASHTO M 145, and included 26 samples of A-2-6 (clayey gravel and sand soil), 3 samples of A-4 (silty soil), 89 samples of A-6 (clayey soil), and 96 samples of A-7-6 (clayey soil). All CBR tests were performed in soaked conditions. The input parameters of the models included the particle size distribution, gravel content (G), coarse sand content (CS), fine sand content (FS), silt clay content (SC), organic content (O), liquid limit (LL), plastic limit (PL), plasticity index (PI), optimum moisture content (OMC), and maximum dry density (MDD). The accuracy of the developed models was assessed using numerous performance indexes, such as the coefficient of determination, relative error, MAE, and RMSE. The results show that the highest prediction accuracy was obtained using the RSS-based extra tree optimization technique.

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Section: Introductionmentioning

confidence: 99%

On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils

et al. 2021

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“…Over the last decade, many scholars have used statistical methods and proposed simple and multiple regressions to estimate CBR values. (Rehman et al, 2017;González Farias et al, 2018;Katte et al, 2019;Haupt et al, 2021). Black (1962) predicted CBR from the Plastic and Liquid Index.…”

Section: Introductionmentioning

confidence: 99%

Application of hybrid modeling to predict California bearing ratio of soil

Thi Thanh Ngo,

Anh Thi Bui,

Nguyen Van

et al. 2024

Vietnam J. Earth Sci.

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California Bearing Ratio (CBR) is used to assess bearing capacity, deformation characteristics of roadbed soil, and base layer material in pavement structure. In general, CBR is often determined by laboratory or in-situ tests. However, it is time- and cost-consuming to conduct this experiment because this test requires cumbersome equipment such as a compressor. In this study, two Artificial Intelligence models are developed, including a simple model (Decision Tree Regression, DT) and a hybrid model (AdaBoost - Decision Tree, AB-DT). Using 214 data samples from Van Don - Mong Cai expressway, Vietnam, 10 input variables of the model were considered namely particle composition (content of gravel (X1), coarse sand (X2), fine sand (X3), silt clay (X4), organic (X5)), Atterberg limits (Liquid limit (X6), Plastic limit (X7), Plastic index (X8)), and compaction curve (optimum water content (X9) and maximum dry density (X10)). The developed models were evaluated by using a variety of statistical indicators, including coefficient of determination (R2), Root mean square error (RMSE), and Mean absolute error (MAE). The results show that AB-DT model has higher accuracy than the DT model. Moreover, the SHAP value analysis shows that the variable X10 influences the CBR value the most. Thus, it implies that applying the AB-DT model to effectively predict the CBR of the roadbed soil saves time and money for experiments.

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“…They concluded that multivariate adaptive regression splines with piecewise linear models achieved the highest level of accuracy with a coefficient of determination (R 2 ) value of 0.969. On the other hand, this study analysed most of works where ML techniques were applied to predict the CBR value [11,[21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. The study concluded that the works conducted with small datasets (consisting of fewer than 160 data points) exhibited higher predictive accuracies, with R 2 values between 0.81 and 1.00, in contrast to the studies conducted with larger datasets ranging from 358 to 389 soil test results [21,22].…”

Section: Introductionmentioning

confidence: 99%

Natural Gradient Boosting for Probabilistic Prediction of Soaked CBR Values Using an Explainable Artificial Intelligence Approach

Díaz,

Spagnoli

2024

Buildings

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The California bearing ratio (CBR) value of subgrade is the most used parameter for dimensioning flexible and rigid pavements. The test for determining the CBR value is typically conducted under soaked conditions and is costly, labour-intensive, and time-consuming. Machine learning (ML) techniques have been recently implemented in engineering practice to predict the CBR value from the soil index properties with satisfactory results. However, they provide only deterministic predictions, which do not account for the aleatoric uncertainty linked to input variables and the epistemic uncertainty inherent in the model itself. This work addresses this limitation by introducing an ML model based on the natural gradient boosting (NGBoost) algorithm, becoming the first study to estimate the soaked CBR value from this probabilistic perspective. A database of 2130 soaked CBR tests was compiled for this study. The NGBoost model showcased robust predictive performance, establishing itself as a reliable and effective algorithm for predicting the soaked CBR value. Furthermore, it produced probabilistic CBR predictions as probability density functions, facilitating the establishment of reliable confidence intervals, representing a notable improvement compared to conventional deterministic models. Finally, the Shapley additive explanations method was implemented to investigate the interpretability of the proposed model.

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Prediction of California Bearing Ratio from Index Properties of Soils Using Parametric and Non-parametric Models

Cited by 32 publications

References 6 publications

On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils

On Random Subspace Optimization-Based Hybrid Computing Models Predicting the California Bearing Ratio of Soils

Application of hybrid modeling to predict California bearing ratio of soil

Natural Gradient Boosting for Probabilistic Prediction of Soaked CBR Values Using an Explainable Artificial Intelligence Approach

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