基于改進泊松-復合小波模型的復合地基全過程沉降預測

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河北省“十五”科技攻關項目(No.Z2005304)

Prediction of composite foundation settlement process based on a modified Poisson-superposition wavelet model

1. Civil and Environmental Engineering School, University of Science and Technology Beijing, Beijing 100083, China;
2. Department of Architectural Engineering, North China Institute of Science and Technology, Beijing 101601, China

摘要: 對復合地基全過程沉降預測模型與方法進行了研究.分析了改進泊松模型的特點和適用性,提出了改進泊松-復合小波神經網絡修正模型.結合實際工程數據對CFG樁復合地基全過程沉降進行了分析和預測,并與改進的泊松模型進行了對比分析.結果表明,提出的模型適用性強,具有更高的預測精度,其絕對誤差在1mm以內.
- 復合地基 /
- 全過程沉降 /
- 泊松模型 /
- 小波模型
Abstract: The model and method used to predict a composite foundation settlement process were studied. The characteristics of the modified Poisson model and its applicability were analyzed and a modified Poisson-superposition wavelet neural net model was proposed. Combined with practical observation data, the CFG pile composite foundation settlement process was analyzed and predicted. A comparison of the obtained theoretical results with those from the modified Poisson model was made. It is shown that the suggested model has better applicability and enables to predict with a higher accuracy, whose absolute error is less than 1mm.
- composite foundation /
- complete process settlement /
- Poisson model /
- wavelet model