Prediction of California Bearing Ratio Using Particle Swarm Optimization

Nagaraju, T. Vamsi; Prasad, Ch. Durga; Raju, M. Jagapathi

doi:10.1007/978-981-15-0035-0_65

Cited by 10 publications

(6 citation statements)

References 7 publications

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“…The strength of the subgrade soil is routinely assessed in terms of its California Bearing Ratio (CBR). The California Bearing Ratio (CBR) of soil is a static strength and bearing capacity index, which may be obtained from either laboratory or in situ measurements [3,4]. The CBR is an important input parameter predicting the stiffness modulus of the soil subgrade, which is a key pavement design index considering the effect of cyclic loading on the soil's stiffness [5][6][7].…”

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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“…The subgrade soil's strength is commonly measured by its California Bearing Ratio (CBR). CBR is a static strength and bearing capacity index that can be measured in the laboratory or in situ 2 , 3 . The CBR is an important input parameter for predicting the stiffness modulus of the subgrade soil, which is an essential pavement design index when cyclic loading is considered 4 , 5 .…”

Section: Introductionmentioning

confidence: 99%

Predicting California bearing ratio of HARHA-treated expansive soils using Gaussian process regression

Ahmad,

Al-Zubi,

Kubińska-Jabcoń

et al. 2023

Sci Rep

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The California bearing ratio (CBR) is one of the basic subgrade strength characterization properties in road pavement design for evaluating the bearing capacity of pavement subgrade materials. In this research, a new model based on the Gaussian process regression (GPR) computing technique was trained and developed to predict CBR value of hydrated lime-activated rice husk ash (HARHA) treated soil. An experimental database containing 121 data points have been used. The dataset contains input parameters namely HARHA—a hybrid geometrical binder, liquid limit, plastic limit, plastic index, optimum moisture content, activity and maximum dry density while the output parameter for the model is CBR. The performance of the GPR model is assessed using statistical parameters, including the coefficient of determination (R2), mean absolute error (MAE), root mean square error (RMSE), Relative Root Mean Square Error (RRMSE), and performance indicator (ρ). The obtained results through GPR model yield higher accuracy as compare to recently establish artificial neural network (ANN) and gene expression programming (GEP) models in the literature. The analysis of the R2 together with MAE, RMSE, RRMSE, and ρ values for the CBR demonstrates that the GPR achieved a better prediction performance in training phase with (R2 = 0.9999, MAE = 0.0920, RMSE = 0.13907, RRMSE = 0.0078 and ρ = 0.00391) succeeded by the ANN model with (R2 = 0.9998, MAE = 0.0962, RMSE = 4.98, RRMSE = 0.20, and ρ = 0.100) and GEP model with (R2 = 0.9972, MAE = 0.5, RMSE = 4.94, RRMSE = 0.202, and ρ = 0.101). Furthermore, the sensitivity analysis result shows that HARHA was the key parameter affecting the CBR.

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“…Alawi and Rajab (2013) utilised multiple linear regression (MLR) analysis to predict the CBR of the unreinforced subbase soil layer. Recently, similar applications of advanced ML techniques for the prediction of CBR of different unreinforced soils were also conducted by other researchers (Erzin and Turkoz 2016, González Farias et al 2018, de Souza et al 2020, Nagaraju et al 2020, Tenpe and Patel 2020. Similarly, several studies were also carried out to evaluate permanent deformation and resilient modulus of recycled demolition wastes in pavements using ML algorithms (Arulrajah et al 2013, Ullah et al 2020, Ghorbani et al 2020a.…”

Section: Introductionmentioning

confidence: 94%

An intelligent approach for predicting the strength of geosynthetic-reinforced subgrade soil

Raja

Shukla

Khan

2021

International Journal of Pavement Engineering

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In the recent times, the use of geosynthetic-reinforced soil (GRS) technology has become popular for constructing safe and sustainable pavement structures. The strength of the subgrade soil is routinely assessed in terms of its California bearing ratio (CBR). However, in the past, no effort was made to develop a method for evaluating the CBR of the reinforced subgrade soil. The main aim of this paper is to explore and appraise the competency of the several intelligent models such as artificial neural network (ANN), least median of squares regression, Gaussian processes regression, elastic net regularisation regression, lazy K-star, M-5 model trees, alternating model trees and random forest in estimating the CBR of reinforced soil. For this, all the models were calibrated and validated using the reliable pertinent historical data. The prognostic veracity of all the tools mentioned supra were assessed using the well-established traditional statistical indices, external model evaluation technique, multi-criteria assessment approach and independent experimental dataset. Due to the overall excellent performance of ANN, the model was converted into a trackable functional relationship to estimate the CBR of reinforced soil. Finally, the sensitivity analysis was performed to find the strength and relationship of the used parameters on the CBR value.

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Prediction of California Bearing Ratio Using Particle Swarm Optimization

Cited by 10 publications

References 7 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

Predicting California bearing ratio of HARHA-treated expansive soils using Gaussian process regression

An intelligent approach for predicting the strength of geosynthetic-reinforced subgrade soil

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