In view of the characteristics of soft soil deep foundation pit for the construction and geotechnical characteristics of the special medium,it is difficult to calculate theoreti- cally accurately structural deformatio...In view of the characteristics of soft soil deep foundation pit for the construction and geotechnical characteristics of the special medium,it is difficult to calculate theoreti- cally accurately structural deformation of the foundation pit,so in the course of excavation on the construction of the information is particularly important.The analysis and compari- son of several popular non-linear forecasting methods,combined with the actual projects, set up a grey theoretical prediction model,time series forecasting model,improved neural network model to predict deformation of the foundation pit.The results show that the use of neural network to predict with high accuracy solution,it is the foundation deformation prediction effective way in underground works with good prospects.展开更多
To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitori...To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitoring datum has been discussed. According to a comprehensive survey, data of 16 stages at operating control point, were verified by a standard t test to determine the stability of the operating control point. A stationary auto-regression model, AR(p), used for the observation point settlement prediction has been investigated. Given the 16 stages of the settlement data at an observation point, the applicability of this model was analyzed. Settlement of last four stages was predicted using the stationary auto-regression model AR (1); the maximum difference between predicted and measured values was 0.6 mm, indicating good prediction results of the model. Hence, this model can be applied to settlement predictions for buildings surrounding foundation pits.展开更多
基金the Educational Department of Liaoning Province Through Scientific Research Project(20060051)National Natural Science Foundation of China(50604009)Universities Excellent Talents Support Plan to Train Foundation of Liaoning(RC-04-13)
文摘In view of the characteristics of soft soil deep foundation pit for the construction and geotechnical characteristics of the special medium,it is difficult to calculate theoreti- cally accurately structural deformation of the foundation pit,so in the course of excavation on the construction of the information is particularly important.The analysis and compari- son of several popular non-linear forecasting methods,combined with the actual projects, set up a grey theoretical prediction model,time series forecasting model,improved neural network model to predict deformation of the foundation pit.The results show that the use of neural network to predict with high accuracy solution,it is the foundation deformation prediction effective way in underground works with good prospects.
基金Project 50279005 supported by the National Natural Science Foundation of China
文摘To ensure the safety of buildings surrounding foundation pits, a study was made on a settlement monitoring and trend prediction method. A statistical testing method for analyzing the stability of a settlement monitoring datum has been discussed. According to a comprehensive survey, data of 16 stages at operating control point, were verified by a standard t test to determine the stability of the operating control point. A stationary auto-regression model, AR(p), used for the observation point settlement prediction has been investigated. Given the 16 stages of the settlement data at an observation point, the applicability of this model was analyzed. Settlement of last four stages was predicted using the stationary auto-regression model AR (1); the maximum difference between predicted and measured values was 0.6 mm, indicating good prediction results of the model. Hence, this model can be applied to settlement predictions for buildings surrounding foundation pits.
