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Journal ArticleDOI

Application of Neural Network Model for the Containment of Groundwater Contamination

01 Jan 2005-Land Contamination & Reclamation-Vol. 13, Iss: 1, pp 81-98
About: This article is published in Land Contamination & Reclamation.The article was published on 2005-01-01. It has received 2 citations till now.
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Journal ArticleDOI
TL;DR: The ANN model can be used to predict CBR value of the Aegean sands included in this study as an inexpensive substitute for the laboratory testing, quite easily and efficiently.
Abstract: This study deals with the development of an artificial neural network (ANN) and a multiple regression (MR) model that can be employed for estimating the California bearing ratio (CBR) value of some Aegean sands. To achieve this, the results of CBR tests performed on the compacted specimens of nine different Aegean sands with varying soil properties were used in the development of the ANN and MR models. The results of the ANN and MR models were compared with those obtained from the experiments. It is found that the CBR values predicted from the ANN model matched the experimental values much better than the MR model. Moreover, several performance indices, such as coefficient of determination, root-mean-square error, mean absolute error, and variance, were used to evaluate the prediction performance of the ANN and MR models. The ANN model has shown higher prediction performance than the MR model based on the performance indices, which demonstrates the usefulness and efficiency of the ANN model. Thus, the ANN model can be used to predict CBR value of the Aegean sands included in this study as an inexpensive substitute for the laboratory testing, quite easily and efficiently.

38 citations


Cites background from "Application of Neural Network Model..."

  • ...ANNs are a form of artificial intelligence which are based on the biological nervous system and inspired by the structure of biological neural networks and their way of encoding and solving problems [49]....

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Journal ArticleDOI
TL;DR: In this paper, artificial neural networks (ANNs) were used to predict the settlement of pad footings on cohesionless soils based on standard penetration test, and the predicted values were found to be quite close to the calculated values.
Abstract: In this study, artificial neural networks (ANNs) were used to predict the settlement of pad footings on cohesionless soils based on standard penetration test. To achieve this, a computer programme was developed to calculate the settlement of pad footings from five traditional methods. The footing geometry (length and width), the footing embedment depth, Df, the bulk unit weight, γ, of the cohesionless soil, the footing applied pressure, Q, and corrected standard penetration test, Ncor, varied during the settlement analyses and the settlement value of each footing was calculated for each method. Then, an ANN model was developed for each traditional method to predict the settlement by using the results of the analyses. The settlement values predicted from the ANN model were compared with the settlement values calculated from the traditional method for each method. The predicted values were found to be quite close to the calculated values. It has been demonstrated that the ANN models developed can be used as an accurate and quick tool at the preliminary designing stage of pad footings on cohesionless soils without a need to perform any manual work such as using tables or charts. Sensitivity analyses were also performed to examine the relative importance of the factors affecting settlement prediction. According to the analyses, for each traditional method, Ncor is found to be the most important parameter while γ is found to be the least important parameter.

11 citations