BP神经网络-灰色系统联合模型预测软基沉降量

目前软基沉降预测多采用指数曲线和双曲线延伸法,其结果不够理想,神经网络在此方面的运用也存在一定的局限,虽然GM(1,1)模型在软基沉降预测领域已得到运用,但在已有的案例中所使用的等时距模型都没有明确说明所采用的插值方法。以深圳湾西部通道填海软基沉降预测分析为例,建立BP神经网络-灰色系统联合模型来探讨解决这一问题的方法。采用BP神经网络逼近非线性插值方法构建等时距时间序列数据,在此基础上建立沉降变形时间序列的GM模型,并建立相应的时间响应函数,预测沉降量。计算实例表明,该模型短期沉降预测结果比较准确,其最终沉降预测结果具有一定的工程参考价值。

Prediction of soft ground settlement based on BP neural network-grey system united model

At present,people often predict soft-ground settlement with the method of exponential curve and double-curve extent;but the result is not ideal.The application of neural network to that exists some limitations.GM(1,1) model has been applied to this field,but all the equal-time settlement data model do not explain definitely interpolation method in existed case.The paper takes the predicting the soft ground settlement of Xibu tunnel filling sea project in Shenzhen for example,discusses the way to solve the problem through building the BP neural network-grey system united model;constructing the equal-time settlement time series data by BP neural network nonlinear interpolation method,on the basis of it,building the time series GM model for settlement,and building the time function to predict the settlement.The case study shows that the model is quite accurate in short-term settlement prediction,its long-term settlement prediction also has some project referential value.