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http://dx.doi.org/10.7843/kgs.2006.22.6.15

A Study on Optimized Artificial Neural Network Model for the Prediction of Bearing Capacity of Driven Piles  

Park Hyun-Il (R & D Team, Samsung Corporation)
Seok Jeong-Woo (R & D Team, Samsung Corporation)
Hwang Dae-Jin (R & D Team, Samsung Corporation)
Cho Chun-Whan (T.A. Team, Samsung Corporation)
Publication Information
Journal of the Korean Geotechnical Society / v.22, no.6, 2006 , pp. 15-26 More about this Journal
Abstract
Although numerous investigations have been performed over the years to predict the behavior and bearing capacity of piles, the mechanisms are not yet entirely understood. The prediction of bearing capacity is a difficult task, because large numbers of factors affect the capacity and also have complex relationship one another. Therefore, it is extremely difficult to search the essential factors among many factors, which are related with ground condition, pile type, driving condition and others, and then appropriately consider complicated relationship among the searched factors. The present paper describes the application of Artificial Neural Network (ANN) in predicting the capacity including its components at the tip and along the shaft from dynamic load test of the driven piles. Firstly, the effect of each factor on the value of bearing capacity is investigated on the basis of sensitivity analysis using ANN modeling. Secondly, the authors use the design methodology composed of ANN and genetic algorithm (GA) to find optimal neural network model to predict the bearing capacity. The authors allow this methodology to find the appropriate combination of input parameters, the number of hidden units and the transfer structure among the input, the hidden and the out layers. The results of this study indicate that the neural network model serves as a reliable and simple predictive tool for the bearing capacity of driven piles.
Keywords
Artificial neural network; Bearing capacity; Driven pile; Genetic algorithm;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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