Abstract:Research purposes: The static load test is the most visual and effective approach in all foundation pile tests and the scientific validity of the tested data is essential to analysis of test result.By using parallel gray neural network,this paper discusses the effective prediction of foundation pile settlement and data remedy in static tests under the similar geological conditions,and offers the calculation methods for them.
Research conclusions: The correlation degree of the soil mound pile is decided by making gray correlation analysis. With the aid of the linear—weighted method,the gray model is integrated with BP neural network to optimize the noisy—reduction for sole model.By adopting parallel gray neural network,the effective static load test data of sole pile in the area of the similar soil layer are predicted,and the errors are compared.The results indicate that overall factors could be considered in this approach,and the decisions of missing data remedy in the test,fitting of known settlement,prediction of future settlement and reference value of foundation pile settlement have practical values in correlation region.
申耀伟,王 杰. 基于并联灰色神经网络的基桩沉降量预测研究[J]. 铁道工程学报, 2009, 26(6): 25-29.
SHEN Yao—wei,WANG Jie. Research on the Prediction of Settlement of Foundation Pile with Parallel Gray Neural Network. 铁道工程学报, 2009, 26(6): 25-29.