基坑变形组合预测分析及安全性评价

为提高基坑变形预测精度及合理评价基坑所处的安全状态，提出以支持向量机、极限学习机和GM(1,1)模型为单项预测模型，构建定权法、非定权法确定组合权值的组合预测模型，并利用累计变形量与变形控制值构建基坑变形的安全性评价指标，以判断基坑所处的安全状态，且采用重标极差法分析基坑安全性的发展趋势。实例分析表明： 1）组合预测较单项预测具有更高的预测精度，且能有效降低预测风险，增加预测结果的稳定性； 2）非定权组合的预测精度要略优于定权组合的预测精度，且以BP神经网络权值法的组合效果最优； 3）通过对某基坑的安全性分析，得知该基坑处于危险阶段，需采取必要的安全措施，且预测结果与安全分析结果一致，验证了预测方法和安全性评价方法2种分析方法的有效性和准确性。

Combined Prediction Analysis and Safety Evaluation of  Foundation Pit Deformation

In order to improve the prediction accuracy of foundation pit deformation and rationally evaluate the safety state of the foundation pit, a combined prediction model is established, in which the support vector machine, extreme learning machine and GM (1,1) model are proposed as single prediction model and the combination weight value is determined by fixed weight method and non fixed weight method. The safety evaluation indices of the foundation pit deformation are constructed by using cumulative deformation and deformation control value to evaluate the safety state of the foundation pit. The standard deviation method is used to analyze the development trend of foundation pit safety. The case analytical results show that: (1) The combined prediction is superior to single prediction in terms of prediction accuracy, prediction risk and prediction results stability. (2) The prediction accuracy of non fixed weight method is superior to that of fixed weight method, and the combination effect of BP neural network weight method is the optimum. (3) The prediction results show that the foundation pit is in dangerous and necessary safety measures should be conducted; and the prediction results coincide with the analytical results, which illustrates the effectiveness and feasibility of the prediction and evaluation methods mentioned above.