地铁车站深基坑的变形预测及稳定性研究

为实现基坑变形预测及稳定性的综合研究,先以极限学习机(ELM)神经网络和灰色模型为基础,建立了基坑变形的串联、并联和混联耦合预测模型,以实现基坑变形预测;其次,再利用尖点突变理论和Mann-Kendall检验对基坑稳定性及变形趋势进行综合判断,以佐证预测结果的准确性。实例检验结果表明:3种耦合模型均能不同程度地提高预测精度,且以混联式模型的预测稳定性最高,其次是并联式模型和串联式模型;同时,预测结果与尖点突变理论和Mann-Kendall检验的分析结果相符,验证了该预测思路的有效性和可行性。研究方法可为基坑的变形预测提供一种的新思路。

Application of Coupling Prediction Model and Cusp Catastrophe Theoryto Deformation Prediction of Deep Foundation Pit of Subway Station

In an attempt to comprehensively research the deformation prediction and stability of foundation pit, the series model, parallel model and parallel-serial coupled prediction model of foundation pit deformation are established on the basis of limit learning machine (ELM neural network) and grey model. Furthermore, the cusp catastrophe theory and Mann-Kendall test are employed to predict the stability and deformation trend of foundation pit to verify the correctness of prediction results. Case study show that the series model, parallel model and parallel-serial coupled model could all enhance prediction accuracy, among which the parallel-serial coupled model is of the highest stability, followed by parallel model and then serial model. In addition, the prediction results are in consistency with those by cusp catastrophe theory and Mann-Kendall test, indicating the effectiveness and feasibility of the present prediction method.