Modification of the properties of salt affected soils using electrochemical treatments
- Authors: Jayasekera, Samudra , Hall, Stephen
- Date: 2007
- Type: Text , Journal article
- Relation: Geotechnical and Geological Engineering Vol. 25, no. 1 (2007), p. 1-10
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- Description: In this project, an in situ soil treatment technique using the principles of electrokinetics was tested using laboratory experimental models in order to identify the potential of this approach in modifying and reinstating the physical properties of salt affected soils. Experiments were conducted in the laboratory using saline-sodic soils collected from two salt affected regions in central Victoria, Australia. Soil specimens were compacted in glass tanks to reproduce in situ density and in situ water content. Using mild steel electrodes inserted into the soil, a direct current was passed through the soil under a constant potential gradient of 0.5 V/cm for a period of 14 days. In separate experiments, distilled water and a saturated lime solution were introduced to the soil via the anode over this experimental period. It was observed that the soil dispersion, otherwise known as soil sodicity (measured as ESP - Exchangeable Sodium Percentage and SAR - Sodium Absorption Ratio) decreased by up to 90% in most regions of the soil between the electrodes. The compressive strength of the soil increased in excess of 100% with electrokinetic treatment alone while the lime-enhanced electrokinetic treatment led to an almost 200% strength increase. The liquid limit and plastic limit of the soil increased causing the plasticity index to decrease, indicating increases in soil compressive strength and workability. These results indicate the potential of this technique for improving the physical properties of salt affected soils both effectively and efficiently, and in particular gives hope for the remediation of salt affected land for infrastructure management and development. © Springer Science+Business Media, Inc. 2006.
- Description: C1
- Description: 2003004772
Effect of rainfall intensity on infiltration into partly saturated slopes
- Authors: Xue, Jianfeng , Gavin, Kenneth
- Date: 2008
- Type: Text , Journal article
- Relation: Geotechnical and Geological Engineering Vol. 26, no. 2 (2008), p. 199-209
- Full Text: false
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- Description: This paper describes the development of a model to analyse the rate of infiltration and run-off experienced by a partly saturated soil slope during rainfall. The paper first reviews some of the most popular infiltration models used in geotechnical analysis, and highlights some of the problems associated with their application. One particular model, the Horton Equation is extended to include rainfall intensity directly in its formulation. The new model is shown to predict infiltration responses, which agree with field measurements. In the final section the influence of the rainfall intensity and pattern of rainfall (variation of rainfall intensity) on the infiltration response of a soil is investigated using the new model.
Design charts for the stability analysis of unsaturated soil slopes
- Authors: Gavin, Kenneth , Xue, Jianfeng
- Date: 2010
- Type: Text , Journal article
- Relation: Geotechnical and Geological Engineering Vol. 28, no. 1 (2010), p. 79-90
- Full Text: false
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- Description: Simple limit equilibrium analyses can be performed to determine the Factor of Safety (FOS) against slope failure of unsaturated soil slopes. However, many of the input parameters needed for these analyses are highly variable, and the FOS value obtained is critically dependent on assumptions made by the designer. This paper describes a suite of reliability analyses on unsaturated soil slopes performed using an invariant reliability model. The results are presented in design charts from which a designer can choose the FOS value required to ensure a given target reliability index for a slope. The approach ensures that despite the variability of input parameters the slope will have a probability of failure of 2.23% or less.
Prediction of drillability of rocks with strength properties using a hybrid GA-ANN technique
- Authors: Khandelwal, Manoj , Armaghani, Danial
- Date: 2016
- Type: Text , Journal article
- Relation: Geotechnical and Geological Engineering Vol. 34, no. 2 (2016), p. 605-620
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- Description: The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN.
- Description: The purpose of this paper is to provide a proper, practical and convenient drilling rate index (DRI) prediction model based on rock material properties. In order to obtain this purpose, 47 DRI tests were used. In addition, the relevant strength properties i.e. uniaxial compressive strength and Brazilian tensile strength were also used and selected as input parameters to predict DRI. Examined simple regression analysis showed that the relationships between the DRI and predictors are statistically meaningful but not good enough for DRI estimation in practice. Moreover, multiple regression, artificial neural network (ANN) and hybrid genetic algorithm (GA)-ANN models were constructed to estimate DRI. Several performance indices i.e. coefficient of determination (R2), root mean square error and variance account for were used for evaluation of performance prediction the proposed methods. Based on these results and the use of simple ranking procedure, the best models were chosen. It was found that the hybrid GA-ANN technique can performed better in predicting DRI compared to other developed models. This is because of the fact that the proposed hybrid model can update the biases and weights of the network connection to train by ANN. © 2015 Springer International Publishing Switzerland