Estimation of consolidation settlement caused by groundwater drawdown using artificial neural networks

Kerh, Tienfuan; Hu, Yung-Ching; Wu, Changyou

doi:10.1016/s0965-9978(03)00053-x

Cited by 12 publications

(7 citation statements)

References 10 publications

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“…The method of back-propagation neural network is widely used in the community of computational intelligence due to its simplicity and effectiveness, so that the theoretical backgrounds and various applications of this approach can be found in many volumes of relative literature (Adeli, 2001;Arslan and Ince, 1996;Bodri, 2000;Hagan et al, 1995;Hirose et al, 1991;Kerh et al, 2003;Kerh and Yee, 2000;Lee and Jeng, 2002;Liong et al, 2000;Pandey and Barai, 1995;Tasadduq et al, 2002;Thirumalaiah and Makarand, 2000;Villiers and Barnard, 1993;Yagawa and Okuda, 1996). In general, this multi-layered neural network includes input layer, hidden layer, and output layer.…”

Section: Fundamentals Of Neural Network Methods and Data Treatmentmentioning

confidence: 99%

Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system

Kerh

Ting

2005

Engineering Applications of Artificial Intelligence

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Section: Fundamentals Of Neural Network Methods and Data Treatmentmentioning

confidence: 99%

Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system

Kerh

Ting

2005

Engineering Applications of Artificial Intelligence

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“…n is the number of inputs. The connection weights, W ji , are continuously adjusted for each training iteration step using the expression below [24].…”

Section: Methodsmentioning

confidence: 99%

Results of Application of Artificial Neural Networks in Predicting Geo-Mechanical Properties of Stabilised Clays—A Review

et al. 2021

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This study presents a literature review on the use of artificial neural networks in the prediction of geo-mechanical properties of stabilised clays. In this paper, the application of ANNs in a geotechnical analysis of clay stabilised with cement, lime, geopolymers and by-product cementitious materials has been evaluated. The chemical treatment of expansive clays will involve the development of optimum binder mix proportions or the improvement of a specific soil property using additives. These procedures often generate large data requiring regression analysis in order to correlate experimental data and model the performance of the soil in the field. These analyses involve large datasets and tedious mathematical procedures to correlate the variables and develop required models using traditional regression analysis. The findings from this study show that ANNs are becoming well known in dealing with the problem of mathematical modelling involving nonlinear functions due to their robust data analysis and correlation capabilities and have been successfully applied to the stabilisation of clays with high performance. The study also shows that the supervised ANN model is well adapted to dealing with stabilisation of clays with high performance as indicated by high R2 and low MAE, RMSE and MSE values. The Levenberg–Marquardt algorithm is effective in shortening the convergence time during model training.

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“…Thus the related equations and its wide range of applications will not be derived and addressed here, as they can be found in the published literatures [21][22][23][24][25][26]. To comply with the principle of neural network and to decrease unwanted noise due to extremes in data, the coordinates of sand line for each beach are at first normalized by using the following transformation:…”

Section: Development Of Neural Network Modelmentioning

confidence: 99%

Forecasting of Nonlinear Shoreline Variation Based on Aerial Survey Map by Neural Network Approach

Kerh¹,

Wang²,

Hsu³

et al. 2009

International Journal of Nonlinear Sciences and Numerical Simulation

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Shoreline erosion problem may cause the possibility of losing land, and this must be considered particularly for a country which is composed of islands. In this study, six sandy nonlinear beaches located at Kenting National Park of Taiwan, were investigated in accordance with aerial survey maps taken in three different years. The images of spatial information were loaded into computerized graphical software, and the set fold function was used to make comparison for obtaining the tendency of sand line variation for the three different times. Based on the available dataset, a back propagation neural network model was then developed for forecasting the long term variation of sand line at each beach. The results showed that the sand line does have a phenomenon of rise and fall at some local regions, but the total area of each beach does not undergo significant changes and are within an acceptable range of error from a statistical standpoint. The neural network model used in this study might offer a new approach for solving this type of nonlinear problem, and the result obtained might provide a valuable reference for a relevant agency working in the area studied.

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Estimation of consolidation settlement caused by groundwater drawdown using artificial neural networks

Cited by 12 publications

References 10 publications

Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system

Neural network estimation of ground peak acceleration at stations along Taiwan high-speed rail system

Results of Application of Artificial Neural Networks in Predicting Geo-Mechanical Properties of Stabilised Clays—A Review

Forecasting of Nonlinear Shoreline Variation Based on Aerial Survey Map by Neural Network Approach

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