MATEC Web Conf.
Volume 67, 2016International Symposium on Materials Application and Engineering (SMAE 2016)
|Number of page(s)||5|
|Section||Chapter 7 Materials Application and Engineering|
|Published online||29 July 2016|
Settlement Prediction of Road Soft Foundation Using a Support Vector Machine (SVM) Based on Measured Data
1 JiLin Communications polytechnic, Changchun, 130021, China
2 JiLin Provincial Transport Scientific Research Institute, Changchun, 130001, China
* Corresponding author: email@example.com
The suppor1t vector machine (SVM) is a relatively new artificial intelligence technique which is increasingly being applied to geotechnical problems and is yielding encouraging results. SVM is a new machine learning method based on the statistical learning theory. A case study based on road foundation engineering project shows that the forecast results are in good agreement with the measured data. The SVM model is also compared with BP artificial neural network model and traditional hyperbola method. The prediction results indicate that the SVM model has a better prediction ability than BP neural network model and hyperbola method. Therefore, settlement prediction based on SVM model can reflect actual settlement process more correctly. The results indicate that it is effective and feasible to use this method and the nonlinear mapping relation between foundation settlement and its influence factor can be expressed well. It will provide a new method to predict foundation settlement.
© The Authors, published by EDP Sciences, 2016
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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