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Geomechanics and Engineering
  Volume 20, Number 5, March10 2020, pages 385-397
DOI: http://dx.doi.org/10.12989/gae.2020.20.5.385
 


Application of artificial neural networks in settlement prediction of shallow foundations on sandy soils
Nguyen-Vu Luat, Kihak Lee and Duc-Kien Thai

 
Abstract
    This paper presents an application of artificial neural networks (ANNs) in settlement prediction of a foundation on sandy soil. In order to train the ANN model, a wide experimental database about settlement of foundations acquired from available literatures was collected. The data used in the ANNs model were arranged using the following five-input parameters that covered both geometrical foundation and sandy soil properties: breadth of foundation B, length to width L/B, embedment ratio Df/B, foundation net applied pressure qnet, and average SPT blow count N. The backpropagation algorithm was implemented to develop an explicit predicting formulation. The settlement results are compared with the results of previous studies. The accuracy of the proposed formula proves that the ANNs method has a huge potential for predicting the settlement of foundations on sandy soils.
 
Key Words
    neural networks; sandy soils; shallow foundation; settlement prediction; back propagation
 
Address
Nguyen-Vu Luat and Kihak Lee: Department of Architectural Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul, 173-147, South Korea

Duc-Kien Thai: Department of Civil and Environmental Engineering, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul, 173-147, South Korea
 

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