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基于灰色理论预测模型的商业综合体项目基坑形变监测研究

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由于基坑形变成因过多,导致安全监测难度增加,因此提出了一种基于灰色理论预测模型的基坑形变监测方法.首先建立灰色理论预测模型和一阶线性动态微分方程,并利用样本数据和基坑形变参数的时间和状态序列计算响应函数,将函数值转换为测试点形式,求解每个点的向量和权重参数,将其作为模型的初始参照.其次建立监测模型的初始序列,采用最小二乘积算法生成监测均值方程,计算背景值、灰度值以及邻近系数,比较三者的线性关系给出监测点的残差条件序列,并求得监测模型可实施条件,完成基坑形变的有效监测.以远洋新光商业综合体项目为例进行了实验验证,结果表明,灰色理论预测模型的监测精准度较高、误差较小、实用性强.
Research on deformation monitoring of foundation pit in commercial complex projects based on grey theory prediction model
Due to the excessive formation of foundation pits,the difficulty of safety monitoring has in-creased.A deformation monitoring method for foundation pits based on grey theory prediction model is proposed in this research.Firstly,a grey theory prediction model and a first-order line-ar dynamic differential equation are established,and the response function is calculated using sample data and the time and state sequences of excavation deformation parameters.The function values are trans-formed into test point forms,and the vector and weight parameters of each point are solved as the initial reference for the model.Secondly,the initial sequence of the monitoring model is established,and the least squares integration method is used to generate the monitoring mean equation,then the background value,grayscale value,and proximity coefficient are calculated.Finally,the linear relationship is compared to pro-vide the residual condition sequence of the monitoring points,and to obtain the implementable conditions of the monitoring model to effectively monitor the deformation of the foundation pit.The experimental verification was carried out using the Ocean New Light Commercial Complex project as an example,and the results showed that the grey theory prediction model has high monitoring accuracy,small errors,and strong practicality.

Grey theory prediction modelFoundation pit deformationResponse functionState sequenceMonitoring mean equation

吕果、王博、李江

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建设综合勘察研究设计院有限公司,北京 100000

灰色理论预测模型 基坑形变 响应函数 状态序列 监测均值方程

2024

甘肃科学学报
甘肃省科学院 中国科学院资源环境科学信息中心

甘肃科学学报

CSTPCD
影响因子:0.414
ISSN:1004-0366
年,卷(期):2024.36(6)