The use of index parameters to predict soil geotechnical properties

This work aimed to analyze the soil quality and heavy metal contamination in the Jumar watershed of Jharkhand and suggests possible measures that could be taken to guarantee suitable management practices. Therefore, in the present work, the soil sampling was done at 8 locations, selected based on site visit. The GPS coordinates of the sampling sites were noted. The soil samples were collected during pre-monsoon and post-monsoon seasons for 2017 and 2018 at a depth of 20 cm from the top. Water samples were collected from the river flowing closer to the sampling points. Then, soil quality parameters such as pH; electrical conductivity; bulk density; moisture content; and surface water quality parameters such as pH, EC, TDS, DO, COD, BOD, alkalinity, turbidity, and nutrients (N & P) were studied. For heavy metal analysis (Pb, Zn, Cu, As, Ni), both soil and water samples were analyzed using an inductive coupled plasma-optical emission spectrophotometer (ICP-OES). Statistical tools like descriptive statistics, multivariate analysis, principal component analysis, correlation coefficient, and ANOVA were applied using SPSS version 21. The data indicate that the values of moisture content varied between 22.06 and 45.7%, soil bulk density varied between 1.20 and 1.55 g/cm 3 , pH varied between 7.79 and 8.17, and EC varied between 116.66 and 207.08 μS/cm. The concentrations of heavy metals in the pre-monsoon season were higher (Zn 0.183 mg/l, Cu 0.09 mg/l, Ni 0.061 mg/l) than those in the post-monsoon season (Zn 0.17 mg/l, Cu 0.076 mg/l, Ni 0.138 mg/l). The water quality index found using pH, EC, TDS, DO, COD, BOD, alkalinity, turbidity, BOD, COD, nutrients, and heavy metals indicated that most sites had excellent water quality, especially in the pre-monsoon period. But in the post-monsoon period of 2017, the water quality index showed that water was in poor condition. Overall water quality was found to be good. It was found that the soil had very slight traces of heavy metals and was slightly alkaline indicating the need for better watershed management practices for the future. PCA showed that the component variables like pH and EC were principal components specially for both pre-monsoon seasons, whereas through ANOVA, it was found that the variable has strong relationship among themselves.

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“…Soil bulk density represents the compactness of the soil. Increased compactness (Jamshidi Chenari et al 2015) restricts the vertical…”

Section: Materials and Methodologymentioning

confidence: 99%

Soil quality and pollution assessment around Jumar watershed of Jharkhand, India

2021

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“…Further, soil grain size distribution predominantly affects soil shear strength compared to its grain shape [5]. Soil dry density and confining stress also positively influence shear strength [6,7]. Soil shear strength also depends on soil moisture content during shearing [8].…”

Section: Introductionmentioning

confidence: 99%

The Effect of Moisture Content at Compaction and Grain Size Distribution on the Shear Strength of Unsaturated Soils

Ahmad

Uchimura

2023

Sustainability

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The soil moisture content at shearing and other factors, including dry density and grain size, influence its shear strength. This study investigated the effect of moisture content at compaction and grain size distribution on the unsaturated soil shear strength. Triaxial compression tests were performed in the laboratory using the modified triaxial apparatus on silica sands No. 3 and 6 without fines and with 20% fines to explore the unsaturated soil shear strength characteristics. Test samples were compacted and sheared at various combinations of the soil’s optimum and residual moisture content. The analysis of the triaxial compression test results shows that moisture content at compaction and the grain size distribution influence the unsaturated soil shear strength. The test samples compacted at optimum moisture content showed higher peak shear strength when sheared at residual moisture content. Further, test results show that the test samples of soil without fines, when compacted at residual moisture content, show higher peak shear strength at optimum moisture content. The finding of this study endorses considering the moisture content at compaction for the geotechnical design of structures while predicting the soil shear strength.

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“…Among other authors, Ardakani and Kordnaeij [23] used genetic and ANN reproductive methods to achieve w opt and g , dmax also used to achieve similar communications. Chenari et al [24] evaluate an evolutionary polynomial regression algorithm to extend models to forecast w opt and g dmax in the time of while Gansonré et al [25] lately extended a forecasting algorithm in situ g d from penetrometer trials in chamber of calibration.…”

Section: Introductionmentioning

confidence: 99%

Comparison of SVR models for predicting the compaction properties of lateritic soils as novel hybrid methods

Zhu¹,

Zhu²,

Zhang³

2022

Eng. Res. Express

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Soil compaction and related parameters play a pivotal role in the material selection process for earth constructions. Because of time limitations and attention to completion resources, the need to develop models for predicting compaction properties is felt more than ever (i.e., maximum dry unit weight (γ_dmax) and optimum moisture content (ω_opt)) using easily measured index properties. The main purpose of this study is to evaluate the usefulness of conventional SVR, ensembled (additive regression (AD-SVR)), and hybrid SVR (imperialist competitive algorithm (ICA-SVR)) models for predicting the maximum dry unit weight (γ_dmax) and optimum water content (ω_opt) related to proctor compaction test of lateritic soils, where the used dataset was collected from published literature from specific sites. Results depict that all developed models have acceptable efficiency in predicting the γ_dmax with R^2 larger than 0.844 and 0.719, and the ω_opt with R^2 larger than 0.732 and 0.714 for the training and testing data, respectively, representing the acceptable correlation between observed and predicted values. The results of all SVR models were better than the literature, in which the hybrid ICA-SVR model outperformed other models by considering performance criteria. Overall, the hybrid ICA-SVR algorithm could get the most suitable results than others for estimating both γ_dmax and ω_opt. The ICA-SVR model outperforms other approaches and demonstrates the optimization algorithm's capacity throughout the optimization process.

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The use of index parameters to predict soil geotechnical properties

Cited by 15 publications

References 20 publications

Soil quality and pollution assessment around Jumar watershed of Jharkhand, India

Soil quality and pollution assessment around Jumar watershed of Jharkhand, India

The Effect of Moisture Content at Compaction and Grain Size Distribution on the Shear Strength of Unsaturated Soils

Comparison of SVR models for predicting the compaction properties of lateritic soils as novel hybrid methods

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