Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils

Erzin, Yusuf; Gumaste, Suchit D.; Gupta, Ashok Kumar; Singh, Devendrá

doi:10.1139/t09-035

Cited by 69 publications

(32 citation statements)

References 78 publications

(65 reference statements)

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“…Several learning algorithms have been developed. The back-propagation learning algorithm, the most commonly used neural network algorithm [9,35,[43][44][45][46][47][48], has been successfully applied with to model many phenomena in the field of geotechnical engineering [49][50][51]. In this algorithm, learning is performed through the gradient descent on the sum of the squares of the errors for all the training patterns [43,49].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

“…Recently, ANNs have been found to be a useful tool to solve many problems in the field of the geotechnical engineering [3]. Since the early 1990s, ANNs have been effectively applied to almost every problem in geotechnical engineering, including constitutive modeling [5,6]; geomaterial properties [3,[7][8][9]; bearing capacity of pile [10,11]; slope stability [12][13][14][15][16]; shallow foundations [17][18][19]; liquefaction potential [20][21][22][23][24][25][26]; and tunnels and underground openings [27,28].…”

Section: Introductionmentioning

confidence: 99%

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The use of neural networks for the prediction of cone penetration resistance of silty sands

Erzin

Ecemis

2016

Neural Comput & Applic

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In this study, an artificial neural network (ANN) model was developed to predict the cone penetration resistance of silty sands. To achieve this, the data sets reported by Ecemis and Karaman, including the results of three high-quality field tests, namely piezocone penetration test, pore pressure dissipation tests, and direct push permeability tests performed at 20 different locations on the northern coast of the Izmir Gulf in Turkey, have been used in the development of the ANN model. The ANN model consisted of three input parameters (relative density, fines content, and horizontal coefficient of consolidation) and a single output parameter (normalized cone penetration resistance). The results obtained from the ANN model were compared with those obtained from the field tests. It is found that the ANN model is efficient in determining the cone penetration resistance of silty sands and yields cone penetration resistance values that are very close to those obtained from the field tests. Additionally, several performance indices such as the determination coefficient, variance account for, mean absolute error, root mean square error, and scaled percent error were computed to examine the performance of the ANN model developed. The performance level attained in the ANN model shows that the ANN model developed in this study can be employed for predicting cone penetration of silty sands quite efficiently.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The use of neural networks for the prediction of cone penetration resistance of silty sands

Erzin

Ecemis

2016

Neural Comput & Applic

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“…Kumar and Samui (2008) analyzed pore water pressure response through neural network. Erzin et al (2009) studied the hydraulic conductivity of compacted fine grained soils developing ANN and multiple regression analysis (MRA) models. Lim and Kolay (2009) predicted hydraulic conductivity of tropical soils by using ANN and demonstrated a comparison between the conventional estimation of hydraulic conductivity by using Shepard's equation and the predicted hydraulic conductivity from ANN.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Hydraulic Conductivity of Soil Bentonite Mixture Using Hybrid-ANN Approach

Mishra¹

2015

J ENV INFORM

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ABSTRACT. Due to its low hydraulic conductivity compacted soil-bentonite mixture is widely used as a barrier material at waste disposal site. The experimental determination of hydraulic conductivity of soil-bentonite mixture, which depends on the various physical and chemical and mineralogical factors, requires expensive and time consuming setup. Thus, a hybrid neural network model (combining genetic algorithm with neural network) is presented here as a complementary tool to model hydraulic conductivity of soil-bentonite mixture. The prediction capability of the model has been found to be satisfactory. The developed model yielded correlation coefficients of 0.98 and 0.97 for training and testing data sets, respectively. The proposed model was compared with conventional neural network models by using different statistical indicators such as Nash-Sutcliffe model efficiency and discrepancy ratio with standard deviation. It was found that the predictions obtained from developed model agreed well with experimental observations. Identification of important parameters and ranking their order of influence on hydraulic conductivity has been discussed by using input significance test.

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“…In the last few years, artificial neural networks (ANNs) have played an important role in such fields as aerospace, automotive, telecommunications and transportation (Chaturvedi et al, 2002;Demuth and Beale, 2002;Smith and Demetsky, 1994). In geotechnical engineering, ANNs have been used successfully in the prediction of lateral load capacity of piles (Das and Basudhar, 2006), in the modeling of maximum dry density and optimum moisture content of soil (Alavi et al, 2010) and in determining the hydraulic conductivity of compacted finegrained soils (Erzin et al, 2009). It has been shown that ANNs can be used successfully for the prediction of moisture content of finegrained soil in the shortest time possible (Shetu and Masum, 2012), but this approach suffers from several limitations.…”

Section: Introductionmentioning

confidence: 99%

Platform for moisture content determination of fine-grained soil

Shetu¹,

Masum²

2014

Geotechnical Research

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Determination of moisture content of soil is crucial and must be performed as frequently as necessary to ensure the quality of construction work. In order to address the limitations and inaccuracies of existing methods, a new concept has been implemented. An artificial neural network and state-of-the-art electronic circuitry are embedded to realise an automatic measurement system based on a low-cost computing platform. Also, to facilitate user interaction with the system, a sophisticated graphical user interface was created. This system, from both a hardware and a software perspective, brings new ideas not only to the moisture content determination test but also to the geotechnical laboratory and field instrumentation in terms of accuracy, automation and machine intelligence. This work shows that hardware and software embedded in test equipment can automate the test procedures in geotechnical engineering, using an appropriate sensor and a data acquisition and processing routine. This paper presents the technical details of the system set-up, hardware and software development, workflow and system validation for the determination of moisture content of fine-grained soil.

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Artificial neural network (ANN) models for determining hydraulic conductivity of compacted fine-grained soils

Cited by 69 publications

References 78 publications

The use of neural networks for the prediction of cone penetration resistance of silty sands

The use of neural networks for the prediction of cone penetration resistance of silty sands

Prediction of Hydraulic Conductivity of Soil Bentonite Mixture Using Hybrid-ANN Approach

Platform for moisture content determination of fine-grained soil

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