一种基坑地面沉降新型预测方法

摘要

Combined with gray theory and Kalman filter prediction method, this paper proposes a new Gray Kalman Filtering(GKF) method to predict the ground subsidence, which utilizes the relationship between the displacement of inclinometer supporting structure and ground subsidence. The new model is verified by Ningbo metro engineering using the comparison between actual monitoring data and predicted results. To test the effect of GKF prediction method, it uses residual test method and the results show that GKF method achieves high accuracy in predicting the ground subsidence of foundation pit excavation.%根据支护结构测斜位移和地面沉降的关系,结合灰色理论和卡尔曼滤波预测方法,提出一种地面沉降预测方法—灰色卡尔曼滤波(GKF),将预测结果与宁波地铁舟孟北路的实际监测数据进行对比.采用残差检验法检测GKF的预测效果,结果表明,GKF方法具有较高预测精度.