California Bearing Ratio Of Soil Research Articles

Enhancing California Bearing Ratio through Optimized Compaction Efforts: A Study on Alluvial Soils in North India

The quantity of soil required for embankment construction is enormous. Often the soil is brought to the site in dumpers in multiple rounds. This to and from movement of the wagons leads to the deterioration of the existing flexible pavement. In the absence of good earth nearby, using existing soils to act as a subgrade material upon meeting the desired strength and stability characteristics is the need of the hour. The California Bearing Ratio (CBR) is an indicator of soil's strength, which further depends on the degree of compaction of soil. In this study, an effort has been made to find the required compaction efforts to achieve maximum CBR value finding its applicability for both conventional flexible and perpetual pavements. Three different types of alluvial soils in North India were tested for CBR with varying compaction efforts using the modified Proctor test. The effect of mixing different soils was also considered to find the effectiveness of mechanically mixing soils on the required compaction efforts. It was observed that there is a constant upsurge in the CBR of soils upon an increase in the percentage compactive effort up to a certain limit. In the case of clayey soils, the rate of increase is less as compared to the well-graded sands. This study also highlights the effect of mechanical mixing of such soils with sandy soils in desired proportions giving rise to amplified values of CBR ratio of the soil blend. The regression analysis was also conducted to examine a correlation between the compactive effort and CBR values and it was best represented by exponential function for different types of soils.

Application of Machine-Learning Algorithms for Predicting California Bearing Ratio of Soil

Application of Machine-Learning Algorithms for Predicting California Bearing Ratio of Soil

COMPACTION CHARACTERISTICS AND WORKABILITY OF THE LATERITIC SOIL-IRON ORE TAILINGS IN PAVEMENT CONSTRUCTION

The mining waste deposit such as iron ore tailings (IOT) in Nigeria is a menace to the environment by constituting a nuisance to the mining industry, and its effect on lateritic soil as a compaction and plasticity reduction material for road pavement is considered in this study. The used natural lateritic soil was classified as A-7-6(11) or CL by the American Association of State Highway and Transportation Officials (AASHTO) and Unified Soil Classification System (USCS) respectively. Up to 16% of the soil’s dry weight was modified with iron ore tailings (IOT). Studying the effects of IOT on the altered soil focused on its cation exchange capacity, plasticity, compaction properties and California bearing ratio (CBR). British Standard Light (BSL) energy was used for the compaction process. However, the results of regression analysis showed that the optimum moisture content had a substantial impact on the soil CBR values. The results of the tests show that as the IOT content increased, plasticity and compaction characteristic values increased. Although an optimal 10% IOT treatment of lateritic soil significantly improved its strength properties, the plasticity characteristics recorded exceeded the requirement specified by the Nigerian General Specifications for direct use as a subbase or base material. Thus, reusing 10% iron ore tailings as filling materials required additional percentages of other pozzolanic materials to lessen the environmental problems associated with their deposition.

Optimization of the California Bearing Ratio of Lateritic Soil Stabilized with Rice Husk Ash using Scheffe’s Method

In a bid to minimize the adverse effects of agricultural waste materials in the environment and also encourage for its optimal usage, this study aims at developing model constants for optimizing the California Bearing Ratio (CBR) of Lateritic Soils stabilized with Rice Husk Ash (RHA) using Scheffe’s method. The study practically involved sampling of the materials, laboratory testing and model formulation. The natural soil was subjected to standardized laboratory testing to determine its index and Engineering properties. The scheffe’s method provided mix ratios for the three mix components (lateritic soil, RHA and water) involved in the mix design. The method also employs the use of second-degree polynomials to generate the model constants. The CBR of the soil was the primary property of the soil considered in the study therefore, a CBR test was conducted on the natural soil and modified soil. The optimization technique carried out gave an optimal mix observed to be of mix ratio 1: 1.70: 0.25 for lateritic soil, RHA and water with a CBR value of 30%. The model formulated was further subjected to validation using the F-test and T-test statistical method and it was found adequate at a confidence level of 95%. Hence, the null hypothesis (H0 ) was adopted. The formulated model being adequate can be used exclusively to predict the CBR of Lateritic soil Stabilized with Rice Husk Ash within the same locality.

The Use of Plastic Sack Fiber and Rice Husk Ash in Increasing the California Bearing Ratio (CBR) Value on the Girisubo Road Section Gunung Kidul Regency

Soil conditions in the Girisubo area, Gunung Kidul Regency, have low soil bearing capacity, so road infrastructure is bumpy and landslides. Therefore, the soil in the area needs to be stabilized and looking for bearing capacity of the soil before use for road subgrades. Clay can be improved by various soil improvement methods, including testing, by mixing plastic sack fiber and rice husk ash. These materials are used based on the high availability of plastic waste and rice husk waste, which are piling up every year. The additional material used was plastic sack fiber with variations of 0.1%, 0.2%, and 0.3% in rice husk ash variations of 0%, 2%, 4%, 6%, and 8% of the dry weight of the soil with the number of test objects 15 pieces. Based on the results, it found the highest CBR value change was obtained in the composition of a mixture of 0.3% plastic sack fiber and 4% rice husk ash of 18.49% and able to increase the percentage of the soil CBR value by 201.50% from the original soil value of 6,13%. Using plastic sack fiber and rice husk ash with the appropriate composition can increase the CBR value and have good quality.

A hybrid approach of ANN and improved PSO for estimating soaked CBR of subgrade soils of heavy-haul railway corridor

ABSTRACT The determination of subgrade/subsoil strength is one of the most important pavement design factors in transportation engineering, particularly for railways, roadways, and airport runways. The California bearing ratio (CBR) is often used to measure the strength and stiffness modulus of subgrade materials. This study presents a novel machine learning solution as an alternate approach for estimating soil CBR in soaked conditions. The present approach is an integration of an artificial neural network (ANN) and improved particle swarm optimisation (IPSO). According to experimental results during the testing phase, the proposed hybrid model, ANN-IPSO has achieved the highest predictive precision with root mean square error, RMSE = 0.0711 and mean absolute error, MAE = 0.0546. The findings of the proposed model are far superior to those of employed models including the conventional ANN, support vector machine, and group method of data handling. Six additional hybrid models of ANN and standard PSO (SPSO), PSO with time-varying accelerator coefficients, modified PSO, Harris hawks optimisation, slime mould algorithm, and colony predation algorithm were also constructed for a detailed comparison. Based on the outcomes, the newly created ANN-IPSO has the potential to be a new tool to estimate soaked CBR of fine-grained soils in civil engineering projects.

Use of Machine Learning to Predict California Bearing Ratio of Soils

CBR is a crucial metric used to assess the durability of base course materials and subgrade soils in various types of pavements. In this research, the machine learning (ML) approach has been implemented using random forest (RF), decision tree (DT), linear regression (LR), and artificial neural network (ANN) models to estimate CBR (California bearing ratio) values of the soil based on seven predictors such as maximum dry density, soil classification, optimum moisture content, liquid limit, plastic limit, plastic index, and swell, which can be easily determined from the laboratory. AASHTO M 145 was used to categorize 252 soil samples that formed the basis of an experimental data set. In this model study, the data were split into 20% test data and 80% training data. Standard statistical measures including coefficient of determination, correlations, and errors were used to assess the effectiveness of the models such as MSE (mean squared error), MAE (mean absolute error), and RMSE (root mean square error). From these evaluation metrics, the random forest algorithm gets a smaller error and larger relative error (R2) value to compare with other algorithms. Therefore, it can be concluded that a random forest algorithm based on the analysis findings can accurately forecast the soil’s CBR.

Effect of steel slag on the mechanical behavior of surficial yellow marl of Tabriz

Fine-grained soils usually have low shear strength and bearing capacity and high swelling potential in the wet state, therefore, they have often to be stabilized by additives. The main objective of this study is to determine the possible effects of ground-granulated blast-furnace slag on the physical and mechanical properties of fine-grained soil. For this purpose, a number of Atterberg, compaction, California bearing ratio (CBR), unconfined compression and freeze-thaw tests were conducted on fine-grained soil. Steel slag (SS) inclusion reduced plasticity index of soil from 44% to 20% when slag content increased from 0% to 55.0%. Moreover, the slag addition improved soil CBR with maximum improvement rate in the sample consists to 55% slag. This increment in CBR was about 140% and 154% for 2.54 mm and 5.08 mm penetration respectively. In addition, slag inclusion raised soil strength with a maximum increment of 132% for clay mixed with 55% slag. Water content and volume changes in freeze-thaw cycles also decreased with increasing percentage of SS, therefore mixtures durability increased.

Development of Empirical Models for the Estimation of CBR Value of Soil from Their Index Properties: A Case Study of the Ogbia-Nembe Road in Niger Delta Region of Nigeria

This study developed empirical-mathematical models to predict the California Bearing Ratio (CBR) using soil index properties in Ogbia-Nembe road in the Niger Delta region of Nigeria. The determination of CBR of soil is a laborious operation that requires a longer time and materials leading to increased cost and schedule; this can be reduced by adopting an empirical-mathematical model that can predict the CBR using other simpler soil index properties such as Plastic Limit (PL), the Liquid Limit (LL), the Plasticity Index (PI) and the Moisture Content (MC), which are less laborious and take lesser time to obtain. Thirteen models were developed to understand the relationship between these soil index properties: the independent variable and the California Bearing Ratio (CBR): the dependent variable; Six linear, Six quadratic and One multiple linear regression models were developed for this relationship. Analysis of variance (ANOVA) on the thirteen models showed that the Optimum Moisture Content (OMC) and the Maximum Dry Density (MDD) are better independent variables for the prediction of the CBR value of Ogbia-Nembe soil generating a quadratic model and a multiple linear regression model having a better coefficient of determination R2 = 0.96 and 0.94 respectively, mean square error (MSE) of 0.74 and 1.152 respectively with Root mean square errors of 0.861 and 1.073 accordingly. These models were used to predict the CBR of the soil. The CBR values predicted by the model were further compared with those of the actual experimental test and found to be relatively consistent with minimal variance. This establishes that CBR of any soil can be predicted from the Index Property of the soil and this is more economical and takes lesser time and can be universally adopted for soil investigation.

Estimation of California Bearing Ratio of Soils Using Random Forest based Machine Learning

California Bearing Ratio (CBR) is an essential parameter utilized to evaluate the strength of the soil subgrades and base course materials of different types of pavements. In this study, the Machine Learning (ML) approach has been adopted using Random Forest (RF) model to estimate the CBR of the soil based on 10 input parameters such as Plasticity Index (PI), Liquid Limit (LL), Silt Clay content (SC), Fine Sand content (FS), Coarse sand content (CS), Optimum Water Content (OWC), Organic content (O), Plastic Limit (PL), Gravel content (G), and Maximum Dry Density (MDD), which can be easily determined in the laboratory. An experimental database was collected from 214 soil samples, which were classified according to AASHTO M 145(clayey, gravel, sand, silty and clayey soils). The data was divided into 70% training and 30% test data in the model study. Model performance was evaluated using standard statistical measures such as coefficient of determination, correlations, and errors (relative error, MAE, and RMSE). Based on the analysis results shows the RF model is capable of correct prediction of the CBR of the Soil.

Estimation of California Bearing Ratio of Soils Using Random Forest based Machine Learning

California Bearing Ratio (CBR) is an essential parameter utilized to evaluate the strength of the soil subgrades and base course materials of different types of pavements. In this study, the Machine Learning (ML) approach has been adopted using Random Forest (RF) model to estimate the CBR of the soil based on 10 input parameters such as Plasticity Index (PI), Liquid Limit (LL), Silt Clay content (SC), Fine Sand content (FS), Coarse sand content (CS), Optimum Water Content (OWC), Organic content (O), Plastic Limit (PL), Gravel content (G), and Maximum Dry Density (MDD), which can be easily determined in the laboratory. An experimental database was collected from 214 soil samples, which were classified according to AASHTO M 145(clayey, gravel, sand, silty and clayey soils). The data was divided into 70% training and 30% test data in the model study. Model performance was evaluated using standard statistical measures such as coefficient of determination, correlations, and errors (relative error, MAE, and RMSE). Based on the analysis results shows the RF model is capable of correct prediction of the CBR of the Soil.

Estimation of California Bearing Ratio of Soils Using Random Forest based Machine Learning

California Bearing Ratio (CBR) is an essential parameter utilized to evaluate the strength of the soil subgrades and base course materials of different types of pavements. In this study, the Machine Learning (ML) approach has been adopted using Random Forest (RF) model to estimate the CBR of the soil based on 10 input parameters such as Plasticity Index (PI), Liquid Limit (LL), Silt Clay content (SC), Fine Sand content (FS), Coarse sand content (CS), Optimum Water Content (OWC), Organic content (O), Plastic Limit (PL), Gravel content (G), and Maximum Dry Density (MDD), which can be easily determined in the laboratory. An experimental database was collected from 214 soil samples, which were classified according to AASHTO M 145(clayey, gravel, sand, silty and clayey soils). The data was divided into 70% training and 30% test data in the model study. Model performance was evaluated using standard statistical measures such as coefficient of determination, correlations, and errors (relative error, MAE, and RMSE). Based on the analysis results shows the RF model is capable of correct prediction of the CBR of the Soil.

Stabilisasi Tanah Gambut Dengan Kapur Terhadap Nilai California Beraring Ratio (CBR)

The sontang-duri cross road is a causeway that is often traversed by large trucks. The problem that occurs is that the road construction has been destroyed, because this road has a subgrade of peat, where peat soil is considered to be less profitable soil if used as the basis of a road construction. Peat soil has low soil bearing capacity, high water content and high compressibility. The purpose of this study was to determine the percentage value of the California Bearing Ratio (CBR) peat soil stabilized using lime using variations of 5%, 10%, and 15%. The method used for this problem is to carry out chemical stabilization with the mixture of lime. From the results of the tests that have been carried out, the original soil CBR value of 95% maximum dry density was obtained at 1.40%. The addition of lime to peat soil with variations of 5%, 10%, and 15% increased the CBR value . The increase that reaches the requirements to be used as a road subgrade is in the variation of 15%, with a CBR value of 8%. The requirement to be used as a road subgrade is based on the bina marga specification of 6% so it can be concluded that the addition of lime for peat soil stabilization can increase the CBR value.

Pemanfaatan Limbah Penambangan Bukit Kapur Untuk Stabilisasi Tanah Lempung ( Clay )

Soil stabilization by adding new materials is one of the alternatives to soil repair that can improve the properties of the soil, namely by mixing new materials and soil that is then compacted. Waste left over limestone hill mining,among others in the form of materials with various sizes of granules. This study aims to review the effect of the addition of limestone hill mining waste material on the density and value of the California Bearing Ratio (CBR) of clay soil. Tests conducted only on a laboratory scale with variations in the increase in limestone hill mining waste levels are 0%, 5%, 10%, and 15% to the total weight of the mixture. Laboratory CBR testing is performed under optimum water content conditions. The results showed that the stabilization of soft soils using limestone hill mining waste, can increase the density and value of soil CBR. The most optimal level of use of limestone hill mining waste and which provides the highest CBR value is for the addition of 5%of limestone hill mining waste with a CBR value of 26.5%.

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

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.

Efficient computational techniques for predicting the California bearing ratio of soil in soaked conditions

California bearing ratio (CBR) is one of the important parameters that is used to express the strength of the pavement subgrade of railways, roadways, and airport runways. CBR is usually determined in the laboratory in soaked conditions, which is an exhaustive and time-consuming process. Therefore, to sidestep the operation of conducting actual laboratory tests, this study presents the development of four efficient soft computing techniques, namely multivariate adaptive regression splines with piecewise linear models (MARS-L), multivariate adaptive regression splines with piecewise cubic models (MARS-C), Gaussian process regression, and genetic programming. For this purpose, a wide range of experimental results of soaked CBR was collected from an ongoing railway project of Indian Railways. Three explicit expressions are proposed to estimate the CBR of soils in soaked conditions. Separate laboratory experiments were performed to evaluate the generalization capabilities of the developed models. Furthermore, simulated datasets were used to validate the feasibility of the best-performing model. Experimental results reveal that the proposed MARS-L model attained the most accurate prediction (R2 = 0.9686 and RMSE = 0.0359 against separate laboratory experiments) in predicting the soaked CBR at all stages. Based on the accuracies attained, the proposed MARS-L model is very potential to be an alternate solution to estimate the CBR value in different phases of civil engineering projects.

ELM-based adaptive neuro swarm intelligence techniques for predicting the California bearing ratio of soils in soaked conditions

This study proposes novel integration of extreme learning machine (ELM) and adaptive neuro swarm intelligence (ANSI) techniques for the determination of California bearing ratio (CBR) of soils for the subgrade layers of railway tracks, a critical real-time problem of geotechnical engineering. Particle swarm optimization (PSO) with adaptive and time-varying acceleration coefficients (TAC) was employed to optimize the learning parameters of ELM. Three novel ELM-based ANSI models, namely ELM coupled-modified PSO (ELM-MPSO), ELM coupled-TAC PSO (ELM-TPSO), and ELM coupled-improved PSO (ELM-IPSO) were developed for predicting the CBR of soils in soaked conditions. Compared to standard PSO (SPSO), the modified and improved version of PSO are capable of converging to a high-quality solution at early iterations. A detailed comparison was made between the proposed models and other conventional soft computing techniques, such as conventional ELM, artificial neural network, genetic programming, support vector machine, group method of data handling, and three ELM-based swarm intelligence optimized models (ELM-based grey wolf optimization, ELM-based slime mould algorithm, and ELM-based Harris hawks optimization). Experimental results reveal that the proposed ELM-based ANSI models can attain the most accurate prediction and confirm the dominance of MPSO over SPSO. Considering the consequences and robustness of the proposed models, it can be concluded that the newly constructed ELM-based ANSI models, especially ELM-MPSO, can solve the difficulties in tuning the acceleration coefficients of SPSO by the trial-and-error method for predicting the CBR of soils and be further applied to other real-time problems of geotechnical engineering.

Influence and correlation of maximum dry density on soaked & unsoaked CBR of soil

Preparation of subgrade plays an significant role in construction of roads as it acts as the foundation to the flexible pavement. The soil subgrade can be utilized to its full extent only if it is well compacted, the subgrade strength is related to the CBR of soil. Determination of stiffness modulus and shear strength of the subgrade using California bearing ratio (CBR) test. The CBR values be contingent on the nature & other properties of soil. CBR test is very laborious, time overwhelming & always be performed on moulded soil samples which makes it difficult to get accurate values and also to implement in reality. In the present study the authors have made an attempt to calculate the CBR values and the index properties of the soil and to develop a relationship between them so that one can obtain reliable CBR values in less time. The authors have collected the samples from Chickballapur district, India and correlated unsoaked and soaked CBR value with maximum dry density, optimum moisture content, percentage fines, liquid limit and unconfined compressive strength etc. Experimental outcomes indicates that the soil properties influence the CBR value of soil. Correlations are developed between the California bearing ratio and maximum dry density with satisfactory R2 values with which CBR values can be predicted.

Evaluation Of Correlation Equations Of CBR Of Soils

California Bearing Ratio (CBR) is extensively used parameter in design and construction of flexible pavements. Sub grade strength along the alignment of flexible pavements is required to be determined in terms of soaked CBR of soils. Also, CBR is used as design and quality control parameter of sub base and base layer materials. Considering significance of CBR determination and to cut down time delay in its determination, researchers have developed correlation equations for CBR of different soils in terms of easily determinable index and engineering properties. Correlation equations are developed in terms of single, two and multiple variables through linear and non linear regression analysis. The existing equations applicability to different soils require thorough evaluation of significant factors influencing CBR of soils and the number of influencing variables used in developing the equations. The present paper is aimed at evaluating the correlation equations for Clays and sandy soils from literature based on properties of 10 selected soil properties. The effectiveness of the correlation equations in more precise estimation of CBR is assessed in terms of number of variables used in the equations, selected variable(s) and method of regression analysis adopted.The study indicated that multiple variable linear regression equations with highest degree yielded better equations compared to non linear regression analysis. The study also revealed that the accuracy of CBR value increased with increase in degree of correlation equation.

Variation of groundwater depth and cation concentrations with CBRs of residual soils: Case study of three lithologic terrains from North-central Nigeria

Twenty nine (29) water samples and 24 soil samples were collected from three lithologic terrains. The waters were subjected to Atomic Absorption Spectrophotometer (AAS). The soils were subjected to California Bearing Ratio (CBR) tests. The AAS revealed that water from gneiss/schist terrain have mean Ca2+, Na+, Mg2+ and K+ concentrations of 25.09, 13.14, 10.92 and 3.90 mg/l respectively; water from granite terrain have mean Ca2+, Na+, Mg2+ and K+ concentrations of 13.68, 6.39, 5.45 and 2.78 mg/l respectively while water from sandstone terrain have mean Ca2+, Na+, Mg2+ and K+ concentration of 7.82, 1.61, 1.41 and 2.16 mg/l respectively. The CBR tests revealed that the soils from gneiss/schist terrain have mean soaked and unsoaked CBRs of 12.37% and 13.75% respectively; soils from granite terrain have mean soaked and unsoaked CBRs of 35.25% and 65.53% respectively; while soils from sandstone terrain have mean soaked and unsoaked CBRs of 78.13% and 104.13% respectively. Results revealed that terrains of shallow groundwater depth are characterized by low CBRs and vice versa. This work has shown that the variation existing between the groundwater cation concentrations and overlying residual soil CBRs is based on the intrinsic properties of the lithology that formed the soil and that is hosting the groundwater.