基坑沉降监测小样本数据非等间距灰色建模与应用  

Non-equidistant grey modeling and application of foundation pit subsidence monitoring under small sample sets

作　　者：郭松 尹晓星[2] 李福平[3] 刘贤俊 Guo Song;Yin Xiaoxing;Li Fuping;Liu Xianjun(School of Environment Science and Spatial Informatics,China University of Mining and Technology,Xuzhou 221116,China;School of Architecure Engineering,Jiangxi College of Applied Technology,Ganzhou 341000,China;Lida Technology Derelopment Corporation,Jiangxi College of Applied Technology,Ganzhou 341000,China;School of Civil Engineering,Changsha University of Science and Technology,Changsha 410000,China)

机构地区：[1]中国矿业大学环境与测绘学院,江苏徐州221116 [2]江西应用技术职业学院建筑工程学院,江西赣州341000 [3]江西应用技术职业学院立达科技开发总公司,江西赣州341000 [4]长沙理工大学土木工程学院,长沙410000

出　　处：《工程勘察》2022年第3期67-71,共5页Geotechnical Investigation & Surveying

摘　　要：采用非等间距灰色GM(1,1)模型模拟和揭示基坑沉降动态特征和变形规律,为工程设计和灾害防治提供科学依据。针对非等间距GM(1,1)模型不能识别处理突变数据的缺点,结合数据异常值检验的格拉布斯准则(Grubbs criterion)的基本原理和方法,提出一种改进的非等间距灰色GM(1,1)预报算法。利用改进前后的模型分别对某一建筑物基坑沉降监测点的16期监测数据进行预处理和分析,对比采用Grubbs异常值检验法前后模型的预报结果表明,采用Grubbs检验法剔除异常值后,非等间距灰色GM(1,1)模型的均方误差为0.17,验证了改进后模型对描述小样本沉降监测数据的有效性和精确度。The non-equidistant GM(1,1)model was used to simulate the dynamic characteristics and reveal the mechanism of foundation pit subsidence,which could provide scientific basis for engineering design and disaster prevention.In view of the deficiency of non-equidistant GM(1,1)model in identifying and processing of the mutation data,an improved non-equidistant GM(1,1)prediction model based on Grubbs anomaly detection criterion is proposed.The original and new model are used to process 16 sets data of a subsidence monitoring point of a building’s foundation pit respectively.Compared these two models after Grubbs anomaly detection,the forecast result indicates that,the Mean Square Error of non-equidistant GM(1,1)model was 0.17,the improved non-equidistant model is more effective and accurate in describing the small sample dataset.

关 键 词：基坑工程 沉降监测 非等间距GM(1 1) Grubbs准则 

分 类 号：O141.4[理学—数学]