基于卡尔曼滤波的离散灰度模型在基坑沉降数据的研究

Application of Discrete Gray Model Based on Kalman Filter to the Foundation Pit Settlement Data

为了提高深基坑开挖过程中实时沉降监测预测的可靠性与准确性,保障基坑施工和周边环境安全,针对深基坑开挖过程中周围底层移动、施工、环境因素及实际观测过程中原始数据存在较多噪声对原始沉降数据产生一定影响等诸多问题。本文考虑使用卡尔曼滤波理论对沉降数据进行去噪预处理,并建立离散灰度模型,通过该模型对沉降数据进行分析及预测。通过实验数据分析处理,验证模型预测精度有了一定的提高,且具有一定的参考价值。

Improving the reliability and accuracy of the Deep Foundation Pit Excavation Process in the real time settlement monitoring forecast,so we can protect the foundation of pit construction and environment security. There are so many problems in the Deep Foundation Pit Excavation Process such as the underlying move,construction,environment factors and the actual observation process in the original data which exists more noise and influence the original settlement data. So,this text consider using the Kalman Filter Theory to overcome the noise pretreatment,and establish the discrete gray model,through the model to make data analysis and forecast. Over the analysis of experimental data processing,we can verificat the model prediction accuracy,and have some value reference. The Discrete Grey model in foundation pit of Kalman Filter is based on the settlement data＇ application.