一种改进的动态灰色GM(1,1)模型在深基坑形变监测中的预测分析

针对传统GM(1,1)模型在处理浮动较大数据时精度不高的问题,提出了一种基于背景值优化和残差改进的动态GM(1,1)模型。利用复化Simpson3/8求积公式取代传统的算数均值计算模式,再通过原始序列的新陈代谢来实现模型的动态更新,在此基础上联立残差GM(1,1)模型,得到改进后的GM(1,1)模型。结合某地铁深基坑沉降观测数据,并对比于传统GM(1,1)模型的预测结果,发现提出的改进后GM(1,1)模型具有更高的精度和更好的适用性。

Predictive analysis based on an improved dynamic grey GM (1,1) model in deep-base pit deformation monitoring

In view of the problem that the traditional GM (1,1) model is not accurate in dealing with floating large data, a dynamic GM (1,1) model based on background value optimization and residual improvement is proposed. The traditional calculation mean calculation mode is realized by using the compounding Simpson 3/8 accumulation formula, and the dynamic update of the model is realized through the metabolism of the original sequence, on the basis of which the residual GM (1,1) model is combined, and the improved GM (1,1) model is obtained. Combined with the observation data of a subway deep pit subsidence and compared with the prediction results of the traditional GM (1,1) model, it is found that the improved GM (1,1) model has higher precision and better applicability.