A Study on the Prediction Model of High Filling Foundation Settlement in Northwest China
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摘要: 沉降变形一直都是困扰填方工程的关键问题，原始地基中存在的湿陷性黄土与粉质黏土会导致沉降增大以及不均匀沉降等现象的发生。因此，做好工程的沉降监测和工后预测，对于保障高填方地基的稳定性至关重要。笔者以西北某机场迁建工程中高填方沉降监测数据作为依据，分别应用双曲线、对数和指数拟合曲线对填方地基的沉降进行预测、分析与对比，总结出各模型的拟合特点。同时采用灰色系统理论GM（1，1）对高填方地基建立另一种沉降预测模型，而针对GM（1，1）灰色模型预测值在沉降后期开始偏离实测曲线的问题，提出GM（1，1）-BP神经网络联合预测模型。最后通过采用不等权系数将灰色GM（1，1）模型和曲线模型结合，构建组合模型，以求最大程度提高GM（1，1）灰色模型预测精度，为西北地区未来类似工程的地基沉降预测提供借鉴。Abstract: Settlement deformation has always been a key problem in filling projects. The presence of wet-set loess and pulverized clay in the original foundation leads to increased and uneven settlement.Therefore, settlement monitoring and post-work prediction are essential to ensure the stability of high filling foundations. Based on the settlement monitoring data of high fill in an airport relocation project in northwest China, this paper applies hyperbolic, logarithmic and exponential fitting curves to predict the settlement of the fill foundation, analyzing and comparing them, and summarizes the fitting characteristics of each model. A settlement prediction model is established for high filling foundations using the gray system theory GM (1, 1), while a joint GM (1, 1)-BP neural network prediction model is proposed aimed at solving the problem of the deviation of the GM (1, 1) gray model from the measured curve in later stage. Finally the authors combine the gray GM (1, 1) model and the curve model with unequal weight coefficients in order to maximize the prediction accuracy of the GM (1, 1) gray model.The study provides reference for the prediction of similar foundation settlement in northwest China.
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