Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils
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2009
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Abstract
This study deals with development of artificial neural networks (ANNs) and multiple regression analysis (MRA) models for determining hydraulic conductivity of fine-grained soils. To achieve this, conventional falling-head tests, oedometer falling-head tests, and centrifuge tests were conducted on silty sand and marine clays compacted at different dry densities and moisture contents. Further, results obtained from ANN and MRA models were compared vis-à-vis experimental results. The performance indices such as the coefficient of determination, root mean square error, mean absolute error, and variance were used to assess the performance of these models. The ANN models exhibit higher prediction performance than the MRA models based on their performance indices. It has been demonstrated that the ANN models developed in the study can be employed for determining hydraulic conductivity of compacted fine-grained soils quite efficiently. © 2009 NRC Canada.
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Backpropagation , Centrifugation , Centrifuges , Clay minerals , Hydraulic conductivity , Neural networks , Regression analysis , Soils , Testing , Artificial neural network models , Artificial neural networks , Centrifuge modeling , Centrifuge tests , Coefficient of determination , Dry density , Falling-head tests , Fine grained soil , Fine-grained soils , Marine clays , Mean absolute error , Moisture contents , Multiple regression analysis , Oedometers , Performance indices , Prediction performance , Root mean square errors , Silty sands , artificial neural network , centrifugal model test , compacted sediment , dry density , error analysis , fine grained sediment , hydraulic conductivity , moisture content , multiple regression , oedometer test , silty clay , variance analysis , Geologic models