为准确掌握软土地区基坑侧位移变形特性,构建了基坑侧位移的预警模型和预测模型,其中,预警模型先以多重分形去趋势波动分析方法构建预警判别指标,再利用Spearman秩次检验实现判别指标的变化趋势判断,进而完成预警等级划分;预测模型则以脊波神经网络为基础,通过粗集理论和试错法优化模型参数,构建出优化变形预测模型。实例研究表明:通过预警分析,得出所给实例的预警等级为2级,说明其基坑侧位移趋于不利方向发展,应加强监测频率,提高施工安全预警;同时,在变形预测方面,参数优化能有效提高脊波神经网络的预测精度和稳健性,所得预测结果的平均相对误差均<2%,具有较高预测精度,且其预测结果与预警结果一致,佐证了分析结果的准确性,可为基坑安全施工提供一定指导。

Early-warning model and prediction model for the side displacement of foundation pit were built in the aim of accurately grasping the deformation characteristics of foundation pit's side displacement in soft soil area. In the early-warning model, the early-warning discrimination indices were constructed using the multifractal detrended fluctuation analysis method, and then the change trends of the discrimination indices were determined by the Spearman rank test, hence the early-warning classification was completed. In the prediction model that is based on ridgelet neural network, the model parameters were optimized by rough set theory and trial and error method. Case study demonstrated that the early warning of the case in this paper was at level two, which indicated that the side displacement of the foundation pit tended to develop toward an unfavorable direction. Monitoring should be strengthened to improve the early warning for construction safety. In addition, the prediction accuracy and robustness of the ridgelet neural network can be effectively enhanced by parameter optimization, with the average relative error of the prediction results not exceeding 2%. The prediction results were consistent with the early warning results, which proved the accuracy of the analysis results.