Prediction of California Bearing Ratio (CBR) Using Index Soil Properties and Compaction Parameters of Low Plastic Fine-Grained Soil

2021 ◽  
Author(s):  
Jawad Hassan ◽  
Badee Alshameri ◽  
Faizan Iqbal
Keyword(s):  
Soil Properties ◽  
California Bearing Ratio ◽  
Fine Grained ◽  
Fine Grained Soil

scholarly journals PREDICTION OF CALIFORNIA BEARING RATIO OF FINE-GRAINED SOIL STABILIZED WITH ADMIXTURES USING SOFT COMPUTING SYSTEMS

2020 ◽  
Vol 11 (1) ◽  
pp. 28-44
Author(s):  
Md. Rafizul Islam ◽  
Animesh Chandra Roy
Keyword(s):  
Soft Computing ◽  
California Bearing Ratio ◽  
Computing Systems ◽  
Independent Variables ◽  
Fine Grained ◽  
Reasonable Degree ◽  
Optimum Content ◽  
Fine Grained Soil ◽  
Stabilized Soil ◽  
Quarry Dust

The main focus of this study was to predict California bearing ratio (CBR) of stabilized soils with quarry dust (QD) and lime as well as rice husk ash (RHA) and lime. In the laboratory, stabilized soils were prepared at varying mixing proportions of QD as 0, 10, 20, 30, 40 and 50%; lime of 2, 4 and 6% with varying curing periods of 0, 7 and 28 days. Moreover, admixtures of RHA with 0, 4, 8, 12 and 16%; lime of 0, 3, 4 and 5% was used to stabilize soil with RHA and lime. In this study, soft computing systems like SLR, MLR, ANN and SVM were implemented for the prediction of CBR of stabilized soils. The result of ANN reveals QD, lime and OMC were the best independent variables for the stabilization of soil with QD, while, RHA, lime, CP, OMC and MDD for the stabilization of soil with RHA. In addition, SVM proved QD and lime as well as RHA, lime, CP, OMC and MDD were the best independent variables for the stabilization of soil with QD and RHA, respectively. The optimum content of QD was found 40% and lime 4% at varying curing periods to get better CBR of stabilized soil with QD and lime. Moreover, the optimum content of RHA was also found 12% and lime 4% at varying curing periods to get better CBR of stabilized soil with RHA and lime. The observed CBR and selected independent variables can be expressed by a series of developed equations with reasonable degree of accuracy and judgment from SLR and MLR analysis. The model ANN showed comparatively better values of CBR with satisfactory limits of prediction parameters (RMSE, OR, R2 and MAE) as compared to SLR, MLR and SVM. Therefore, model ANN can be considered as best fitted for the prediction of CBR of stabilized soils. Finally, it might be concluded that the selected optimum content of admixtures and newly developed techniques of soft computing systems will further be used of other researchers to stabilize soil easily and then predict CBR of stabilized soils.

scholarly journals Ficus carica L. on terraced agro-brown soils of the Southern Coast of the Crimea

2019 ◽  
pp. 216-224
Author(s):  
A. P. Novitskaya ◽  
N. E. Opanasenko
Keyword(s):  
Soil Properties ◽  
Ficus Carica ◽  
Tree Trunk ◽  
Fine Dust ◽  
Southern Coast ◽  
Fine Grained ◽  
The Crimea ◽  
Fine Grained Soil ◽  
Fig Tree

The reaction of fig plants to the properties of agro-brown terraced soils of the Southern Coast of the Crimea (SCC) was studied, unfavorable soil properties for plants, as well as their optimal and permissible parameters were revealed. The dependence of the volume mass of fine grained soil on the content of silt particles and silt with fine dust in the layer of 0-60 cm and in the root layer was revealed. The quantitative dependences of fig tree trunk circumference on the properties of agro-brown terraced skeletal soil were revealed. The growth of figs depended on the depth of dense underlying rocks, fine grained soil reserves in the layer deeper than 60 cm, as well as in the root layer. Valid parameters are set for these values. The tendency to the dependence of the circumference of the tree trunk on the humus reserves in the root layer and the content of the skeleton in the layer deeper than 60 cm was revealed.

scholarly journals Modeling the Compaction Characteristics of Fine-Grained Soils Blended with Tire-Derived Aggregates

2021 ◽  
Vol 13 (14) ◽  
pp. 7737
Author(s):  
Amin Soltani ◽  
Mahdieh Azimi ◽  
Brendan C. O’Kelly
Keyword(s):  
Energy Levels ◽  
Model Development ◽  
Dry Mass ◽  
Unit Weight ◽  
Power Functions ◽  
Compaction Characteristics ◽  
Practical Applications ◽  
Fine Grained ◽  
Practical Procedure ◽  
Fine Grained Soil

This study aims at modeling the compaction characteristics of fine-grained soils blended with sand-sized (0.075–4.75 mm) recycled tire-derived aggregates (TDAs). Model development and calibration were performed using a large and diverse database of 100 soil–TDA compaction tests (with the TDA-to-soil dry mass ratio ≤ 30%) assembled from the literature. Following a comprehensive statistical analysis, it is demonstrated that the optimum moisture content (OMC) and maximum dry unit weight (MDUW) for soil–TDA blends (across different soil types, TDA particle sizes and compaction energy levels) can be expressed as universal power functions of the OMC and MDUW of the unamended soil, along with the soil to soil–TDA specific gravity ratio. Employing the Bland–Altman analysis, the 95% upper and lower (water content) agreement limits between the predicted and measured OMC values were, respectively, obtained as +1.09% and −1.23%, both of which can be considered negligible for practical applications. For the MDUW predictions, these limits were calculated as +0.67 and −0.71 kN/m3, which (like the OMC) can be deemed acceptable for prediction purposes. Having established the OMC and MDUW of the unamended fine-grained soil, the empirical models proposed in this study offer a practical procedure towards predicting the compaction characteristics of the soil–TDA blends without the hurdles of performing separate laboratory compaction tests, and thus can be employed in practice for preliminary design assessments and/or soil–TDA optimization studies.

scholarly journals Assessment for Thermal Conductivity of Frozen Soil Based on Nonlinear Regression and Support Vector Regression Methods

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Fu-Qing Cui ◽  
Wei Zhang ◽  
Zhi-Yun Liu ◽  
Wei Wang ◽  
Jian-bing Chen ◽  
...  
Keyword(s):  
Thermal Conductivity ◽  
Support Vector Regression ◽  
Nonlinear Regression ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Frozen Soil ◽  
Support Vector ◽  
Soil Thermal Conductivity ◽  
Fine Grained ◽  
Fine Grained Soil

The comprehensive understanding of the variation law of soil thermal conductivity is the prerequisite of design and construction of engineering applications in permafrost regions. Compared with the unfrozen soil, the specimen preparation and experimental procedures of frozen soil thermal conductivity testing are more complex and challengeable. In this work, considering for essentially multiphase and porous structural characteristic information reflection of unfrozen soil thermal conductivity, prediction models of frozen soil thermal conductivity using nonlinear regression and Support Vector Regression (SVR) methods have been developed. Thermal conductivity of multiple types of soil samples which are sampled from the Qinghai-Tibet Engineering Corridor (QTEC) are tested by the transient plane source (TPS) method. Correlations of thermal conductivity between unfrozen and frozen soil has been analyzed and recognized. Based on the measurement data of unfrozen soil thermal conductivity, the prediction models of frozen soil thermal conductivity for 7 typical soils in the QTEC are proposed. To further facilitate engineering applications, the prediction models of two soil categories (coarse and fine-grained soil) have also been proposed. The results demonstrate that, compared with nonideal prediction accuracy of using water content and dry density as the fitting parameter, the ternary fitting model has a higher thermal conductivity prediction accuracy for 7 types of frozen soils (more than 98% of the soil specimens’ relative error are within 20%). The SVR model can further improve the frozen soil thermal conductivity prediction accuracy and more than 98% of the soil specimens’ relative error are within 15%. For coarse and fine-grained soil categories, the above two models still have reliable prediction accuracy and determine coefficient (R2) ranges from 0.8 to 0.91, which validates the applicability for small sample soils. This study provides feasible prediction models for frozen soil thermal conductivity and guidelines of the thermal design and freeze-thaw damage prevention for engineering structures in cold regions.

Physical and Mechanical Properties of Fine-Grained Soil in the Zhejiang-Fujian Coastal Area, China

2011 ◽  
Vol 29 (4) ◽  
pp. 333-345 ◽  
Author(s):  
Yuan-Qin Xu ◽  
Pei-Ying Li ◽  
Ping Li ◽  
Le-Jun Liu ◽  
Cheng-Xiao Cao ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Coastal Area ◽  
Physical And Mechanical Properties ◽  
Fine Grained ◽  
Fine Grained Soil

scholarly journals Centrifuge testing of soil–structure interaction effects on cyclic failure potential of fine-grained soil

2021 ◽  
pp. 875529302098197
Author(s):  
Jason M Buenker ◽  
Scott J Brandenberg ◽  
Jonathan P Stewart
Keyword(s):  
Soil Structure ◽  
Interaction Effects ◽  
Soil Structure Interaction ◽  
Centrifuge Testing ◽  
Geotechnical Centrifuge ◽  
Structure Interaction ◽  
Fine Grained ◽  
Stiffness Properties ◽  
Fine Grained Soil ◽  
Strip Footings

We describe two experiments performed on a 9-m-radius geotechnical centrifuge to evaluate dynamic soil–structure interaction effects on the cyclic failure potential of fine-grained soil. Each experiment incorporated three different structures with a range of mass and stiffness properties. Structures were founded on strip footings embedded in a thin layer of sand overlying lightly overconsolidated low-plasticity fine-grained soil. Shaking was applied to the base of the model container, consisting of scaled versions of recorded earthquake ground motions, sweep motions, and step waves. Data recorded during testing were processed and published on the platform DesignSafe. We describe the model configuration, sensor information, shaking events, and data processing procedures and present selected processed data to illustrate key model responses and to provide a benchmark for data use.

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