建筑物沉降观测结束之后,为降低和预防因地基不均匀沉降等因素造成的不安全事故发生率,准确预测建筑物沉降量变化趋势已引起相关科研单位的重视。首先,将人工神经网络数据分析与灰色GM(1,1)模型相结合,提出GM-ANN预测模型。然后,结合工程实例验证模型对监测沉降危险点数据变化的准确性,形成Matlab拟合曲线和预测趋势图。最终,结果表明仅考虑时间因素,GM-ANN模型明显优于灰色GM(1,1)模型,可使预测精度提高将近三倍。因此,利用GM-ANN预测模型可以对建筑物安全性进行有效预测。
After the observation of building settlement,in order to reduce and prevent the occurrence rate of unsafe accidents caused by the uneven settlement of foundation,the accurate prediction on change trend of building settlement has attracted the attention of relevant research institutions. A GM- ANN prediction model was proposed by combining the data analysis of artificial neural network with grey GM( 1,1) model. Combining with engineering example,the accuracy of the model for monitoring the data change of the settlement dangerous points was verified,and the MATLAB fitting curve and the prediction trend chart were obtained. The results showed that the GM- ANN model is obviously better than the grey GM( 1,1) model only considering the time factor,which can improve the accuracy of prediction by nearly three times. Therefore,the GM-ANN model can be applied to effectively predict the safety of buildings.