TPGM(1,1)预测模型在基坑变形中的应用研究
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摘要
依据深基坑周边地表沉降监测信息,运用灰色理论的方法,对基坑周边地表沉降发展变化规律进行合理的预测可以有效的确保基坑施工期间变形安全和正常使用要求。针对传统的GM(1,1)和DGM(1,1)模型本身数学结构的不足,提出采用TPGM(1,1)预测模型对基坑周边地表沉降进行预测。结合工程实例计算表明,在TPGM(1,1)模型中,利用已知监测数据预测未来一段时间内的基坑周边地表沉降能获得更加精确的预测结果。研究成果能为动态信息化施工以及施工阶段的基坑灾害评估、预警提供有力指导。
Based on the monitoring information of the ground settlement around the deep foundation pit, reasonable prediction of the law of the ground settlement development and change around the foundation pit using the grey theory method can effectively ensure the deformation safety and normal operation requirements during the foundation pit construction. In view of the deficiency of the traditional GM(1,1) and DGM(1,1) models themselves, this paper proposes to use the TPGM(1,1) prediction model to predict the ground settlement around the foundation pit. Combined with calculation of engineering examples, it is shown that in TPGM(1,1) model, more accurate prediction results can be obtained by using the known monitoring data to predict the ground settlement around the foundation pit in a period of the future. The research results can provide strong guidance for dynamic information construction and foundation pit disaster assessment and early warning during construction.
Based on the monitoring information of the ground settlement around the deep foundation pit, reasonable prediction of the law of the ground settlement development and change around the foundation pit using the grey theory method can effectively ensure the deformation safety and normal operation requirements during the foundation pit construction. In view of the deficiency of the traditional GM(1,1) and DGM(1,1) models themselves, this paper proposes to use the TPGM(1,1) prediction model to predict the ground settlement around the foundation pit. Combined with calculation of engineering examples, it is shown that in TPGM(1,1) model, more accurate prediction results can be obtained by using the known monitoring data to predict the ground settlement around the foundation pit in a period of the future. The research results can provide strong guidance for dynamic information construction and foundation pit disaster assessment and early warning during construction.
引文
[1] 徐杨青,程琳.基坑监测数据分析处理及预测预警系统研究[J].岩土工程学报,2014(1):219-224.
[2] 曹净,丁文云,赵党书,等.基于 LSSVM-ARMA 模型的基坑变形时间序列预测[J].岩土力学,2014(2).578-586.
[3] 刘美麟,房倩,张顶立,等.基于改进 MSD 法的基坑开挖动态变形预测[J].岩石力学与工程学报,2018(7):1700-1707.
[4] 陈艳茹.基于遗传算法和极限学习机的智能算法在基坑变形预测中的应用[J].隧道建设,2018(6):941-947.
[5] 傅理文,彭渊,翁湛,等.深基坑安全监测与预警平台的开发与应用[J].地下空间与工程学报,2018(1):423-429.
[6] 王海飙,杨海旭,张华.深基坑工程施工安全监测与预警[J].建筑技术,2010(3):257-260.
[7] 向玮,张爱军,高伟.深基坑施工变形预测与控制方法研究[J].岩土工程学报,2012(1):634-637.
[8] 周星勇,杨容浩,王志胜,等.一种改进遗传神经网络的建筑基坑沉降预测模型[J].测绘工程,2018(3):53-57.
[9] 邓聚龙.灰色预测与决策[M].武汉:华中科技大学出版社,2002.
[10] 战立青,施化吉.近似非齐次指数数据的灰色建模方法与模型[J].系统工程理论与实践,2013(3):689-694.
[11] 刘思峰,党耀国,方志耕,等.灰色系统理论及其应用[M].北京:科学出版社,2010.
[2] 曹净,丁文云,赵党书,等.基于 LSSVM-ARMA 模型的基坑变形时间序列预测[J].岩土力学,2014(2).578-586.
[3] 刘美麟,房倩,张顶立,等.基于改进 MSD 法的基坑开挖动态变形预测[J].岩石力学与工程学报,2018(7):1700-1707.
[4] 陈艳茹.基于遗传算法和极限学习机的智能算法在基坑变形预测中的应用[J].隧道建设,2018(6):941-947.
[5] 傅理文,彭渊,翁湛,等.深基坑安全监测与预警平台的开发与应用[J].地下空间与工程学报,2018(1):423-429.
[6] 王海飙,杨海旭,张华.深基坑工程施工安全监测与预警[J].建筑技术,2010(3):257-260.
[7] 向玮,张爱军,高伟.深基坑施工变形预测与控制方法研究[J].岩土工程学报,2012(1):634-637.
[8] 周星勇,杨容浩,王志胜,等.一种改进遗传神经网络的建筑基坑沉降预测模型[J].测绘工程,2018(3):53-57.
[9] 邓聚龙.灰色预测与决策[M].武汉:华中科技大学出版社,2002.
[10] 战立青,施化吉.近似非齐次指数数据的灰色建模方法与模型[J].系统工程理论与实践,2013(3):689-694.
[11] 刘思峰,党耀国,方志耕,等.灰色系统理论及其应用[M].北京:科学出版社,2010.