长江科学院院报 ›› 2016, Vol. 33 ›› Issue (5): 53-57.DOI: 10.11988/ckyyb.20150828

• 工程安全与灾害防治 • 上一篇    下一篇

基于最优加权组合预测的隧道监控量测数据分析

邱子锋1, 沈简1, 傅旭东1, 罗浩威2   

  1. 1.武汉大学 土木建筑工程学院,武汉 430072;
    2.中铁西北科学研究院有限公司,兰州 730070
  • 收稿日期:2015-09-30 出版日期:2016-05-01 发布日期:2016-05-10
  • 通讯作者: 罗浩威(1987-),男,湖北孝感人,工程师,硕士,主要从事隧道工程和边坡工程的监测及研究,(电话)13823728496(电子信箱)luohaowei520@163.com。
  • 作者简介:邱子锋(1987-),男,湖北应城人,硕士研究生,主要从事隧道工程与桩基工程方面的研究,(电话)15071168780(电子信箱)ycqzf0301@sina.cn。
  • 基金资助:
    国家科技支撑计划课题(2014BAL05B07)

Analysis of Tunnel Monitoring Measurement Data Based on the Optimum Weighted Combinatorial Prediction Model

QIU Zi-feng1, SHEN Jian1, FU Xu-dong1, LUO Hao-wei2   

  1. 1.School of Civil and Architectural Engineering, Wuhan University, Wuhan 430072, China;
    2.Northwest Research Institute Co., Ltd. of CREC, Lanzhou 730070, China
  • Received:2015-09-30 Published:2016-05-01 Online:2016-05-10

摘要: 监控量测技术可收集能反映施工过程中围岩动态的信息,据此判断围岩的稳定状态、确定二次衬砌时机及验证所选支护方式的合理性。通过对华蓥山隧道的监控量测数据分析,建立了多个回归模型进行比选,得到拟合精度较高的回归模型;基于最优加权组合预测法对拱顶沉降量进行预测,通过对比组合预测和单一预测模型的预测精度,验证了最优加权组合预测法的优越性;将最优加权组合预测的结果应用于沉降速率的分析,可以确定隧道二次衬砌的时间。研究结果表明:最优加权组合预测法在隧道监控量测数据分析中的应用,可以提高预测精度,较单一预测模型能更加有效地反映拱顶沉降的发展趋势;选取精度较高的单一预测模型进行最优加权组合预测,分析变形速率,可以为确定隧道的二次衬砌时间提供依据,具有一定的实用价值。

关键词: 隧道工程, 监控量测数据, 回归分析模型, 最优加权组合预测, 预测精度

Abstract: By using monitoring technology we can collect information of the dynamics of surrounding rock in the process of construction, and hence judging the stability of surrounding rock condition and determining the right time of secondary lining and verifying the rationality of supporting mode. Through the analysis of tunnel monitoring measurement data of Huaying mountain tunnel, we established several regression models for comparison, and obtained two regression models of high precision. Then we applied the optimum weighted combinatorial prediction model (OWCPM) to predict the arch crown settlement, and compared the result with those of single prediction models. The OWCPM is verified to be superior to single models. According to the results of the OWCPM, we analyzed the deformation rate, and hence determining the timing of secondary lining. The results show that the OWCPM in analyzing tunnel monitoring measurement data improves the prediction accuracy, and better reflects the development trend of crown settlement compared with single forecast models.

Key words: tunneling engineering, monitoring measurement data, regression analysis model, optimum weighted combinatorial prediction model, prediction accuracy

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