多阶段递进式预测模型在基坑变形中的应用研究

摘要

为实现对基坑变形的高精度预测,提高预测结果的稳定性,采用支持向量机、BP神经网络及GM(1,1)作为基础预测模型,并建立了对应各模型参数优化的一阶递进预测模型.以一阶递进预测结果为基础,构建了多种定权与非定权的二阶组合预测模型;以马尔可夫链理论为基础,建立了三阶递进的误差修正模型,实现了对基坑变形的多阶段递进式预测.结果表明:通过各阶段的递进预测,预测精度及稳定性都有了很大的提高,验证了递进预测思路的有效性和可行性.通过对基坑变形的递进式预测研究,以期为基坑的变形提供一种新的思路.%The aim of this research is to improve the precision of pit deformation prediction and enhance the stability of prediction results.Support vector machine,BP neural network and GM (1,1) are used as the basis of prediction model,and the corresponding first-order prediction models with parameters optimized are established.On this basis,the second-order combinatorial forecasting model of multiple fixed weight and non-fixed weight is established.In subsequence,on the basis of the Markov chain theory,the error correction model of three steps is established,and the progressive prediction of foundation pit deformation is realized.Results demonstrate that the prediction accuracy and stability are greatly improved by the progressive prediction of multiple stages,which verifies the validity and feasibility of the proposed method in this paper.The result is expected to provide a new idea for the prediction of foundation pit deformation.