基于熵权法的PHC管桩承载力组合预测

为克服单项预测方法产生的误差,利用灰色模型GM（1,N）、多元线性回归、BP神经网络等3种单项预测方法建立组合预测模型,并采用熵值法确定加权系数。通过对PHC管桩承载力进行比较预测,结果显示GM（1,N）法平均绝对百分比误差（MAPE）值为5.4%,多元线性回归法的MAPE为3.0%,BP神经网络法的MAPE为2.8%,组合预测法的MAPE为2.3%。因此组合预测法精度较高,实用性更强。

Combination forecasting of bearing capacity of PHC pipe pile based on entropy method

The combination forecasting model was building to overcome the potential errors generated by single forecast model on the basis of the grey system GM（1,N）,multiple linear regression and back-propagation neural network,and the weighting coefficients were determined by the entropy method.The contrast test was conducted to predict the bearing capacity of PHC pile,and the results show that the method means absolute percentage error（MAPE） of GM（1,N） is 5.4%,the MAPE of multiple linear regression is 3.0%,the MAPE of BP neural network method is 2.8%,and the MAPE of the combined forecasting method is 2.3 %.Therefore the combined forecasting has high precision and practicability.