施工期大型水工系统监测数据分析与稳定性评估
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摘要
大型水利水电工程在施工期的安全监测对工程安全稳定性起着非常重要的作用,其监测数据和稳定性分析贯穿于工程每个阶段。由于施工期,工程所处的环境非常复杂,监测数据的预处理显得更加必要。同时,其稳定性的定量分析也难以实现。本文全面介绍了监测数据预处理的方法和措施,并建立沉降模型对大坝进行了沉降分析,最后对糯扎渡水电站3#导流洞的稳定性分析作出了定性的模糊评估。
本文主要研究内容如下：
(1)在监测数据预处理方面,总结了可以防止监测数据出现误差的措施,提出了可用于数据降噪的小波分析法,分析和总结了异常值的识别方法,结合工程应用对异常值中野值和突变的区分方法进行了总结,针对三种监测数据的修补方法,建立了基于回归分析和RBF神经网络的模型,并对监测数据进行了修补。
(2)在建立监测数据模型的研究方面,结合糯扎渡水电站大坝C断面的工程实际,应用逐步回归法对施工期的沉降模型因子及模型进行了选择,分别建立了时空模型、空间模型和等效模型,并对模型进行分析,得出时空模型和等效模型比较适用于施工期的沉降监测。
(3)在地下洞室稳定性评估的研究方面,采用层次分析法对影响稳定性的因素进行了权重分析和定性分析,建立了稳定性的模糊评估模型。针对糯扎渡水电站3#导流洞工程实例,利用该模型进行了稳定性评估,得出其评估结果为稳定。
During the construction period, the safety monitoring of large water conservancy and hydropower project for engineering safety stability plays a very important role. The analysis of monitoring data and engineering stability throughout the project each phase. For the construction period, the surrounding of engineering is very complicated, so the preprocessing of monitoring data appears more necessary. At the same time, the quantitative analysis of engineering stability is also difficult to achieve. This paper comprehensive introduction the preprocessing methods and measures of monitoring data, and a settlement model for dam conducts sedimentation analysis in filling stage, has been established. In the end, for the stability analysis of the3#diversion tunnel of NuoZhaDu Hydropower Station make qualitative fuzzy assessment.
In this paper, the main research contents are as follows:
(1)In the preprocessing of monitoring data, some measures are summarized for preventing human factors, which leading to monitoring data errors. Wavelet analysis is presented which can be used for data reduction, The identification methods of abnormal value for data are analyzed and summarized, the outlier and mutation are distinguished combined with the engineering application. The three kinds of monitoring data mending methods are put forward, and mending the data by using the regression analysis and RBF neural network model.
(2)In the establishment of model, which used for monitoring data's research. In the construction period, combine with engineering practice of Nuozhadu Hydropower Station Dam's Section C, for selecting the settlement model factors and the model use the stepwise regression method. The time-space model、space model and equivalent model are established and analysis. Draw the best settlement monitoring model for the construction period are time-space model equivalent model.
(3)In the stability assessment of researching underground caverns, use AHP for analysis the factors'weight that affect stability, and has carried on the qualitative analysis, establish the fuzzy evaluation model for stability analysis. For the3# Diversion Tunnel of Nuozhadu Hydropower Station, evaluate its stability and the result is stable.
本文主要研究内容如下：
(1)在监测数据预处理方面,总结了可以防止监测数据出现误差的措施,提出了可用于数据降噪的小波分析法,分析和总结了异常值的识别方法,结合工程应用对异常值中野值和突变的区分方法进行了总结,针对三种监测数据的修补方法,建立了基于回归分析和RBF神经网络的模型,并对监测数据进行了修补。
(2)在建立监测数据模型的研究方面,结合糯扎渡水电站大坝C断面的工程实际,应用逐步回归法对施工期的沉降模型因子及模型进行了选择,分别建立了时空模型、空间模型和等效模型,并对模型进行分析,得出时空模型和等效模型比较适用于施工期的沉降监测。
(3)在地下洞室稳定性评估的研究方面,采用层次分析法对影响稳定性的因素进行了权重分析和定性分析,建立了稳定性的模糊评估模型。针对糯扎渡水电站3#导流洞工程实例,利用该模型进行了稳定性评估,得出其评估结果为稳定。
During the construction period, the safety monitoring of large water conservancy and hydropower project for engineering safety stability plays a very important role. The analysis of monitoring data and engineering stability throughout the project each phase. For the construction period, the surrounding of engineering is very complicated, so the preprocessing of monitoring data appears more necessary. At the same time, the quantitative analysis of engineering stability is also difficult to achieve. This paper comprehensive introduction the preprocessing methods and measures of monitoring data, and a settlement model for dam conducts sedimentation analysis in filling stage, has been established. In the end, for the stability analysis of the3#diversion tunnel of NuoZhaDu Hydropower Station make qualitative fuzzy assessment.
In this paper, the main research contents are as follows:
(1)In the preprocessing of monitoring data, some measures are summarized for preventing human factors, which leading to monitoring data errors. Wavelet analysis is presented which can be used for data reduction, The identification methods of abnormal value for data are analyzed and summarized, the outlier and mutation are distinguished combined with the engineering application. The three kinds of monitoring data mending methods are put forward, and mending the data by using the regression analysis and RBF neural network model.
(2)In the establishment of model, which used for monitoring data's research. In the construction period, combine with engineering practice of Nuozhadu Hydropower Station Dam's Section C, for selecting the settlement model factors and the model use the stepwise regression method. The time-space model、space model and equivalent model are established and analysis. Draw the best settlement monitoring model for the construction period are time-space model equivalent model.
(3)In the stability assessment of researching underground caverns, use AHP for analysis the factors'weight that affect stability, and has carried on the qualitative analysis, establish the fuzzy evaluation model for stability analysis. For the3# Diversion Tunnel of Nuozhadu Hydropower Station, evaluate its stability and the result is stable.
引文
[1]吴中如,顾冲时.重大水工混凝土结构病害检测与健康诊断[M]北京：高等教育出版社,2005.
[2]张风堂.糯扎渡水电站导流隧道围岩稳定及支护研究[D].南京：河海大学,2008.
[3]钱正英.中国水利[M].北京：水利电力出版社,1991.
[4]范庆来.大坝监测资料分析与安全指标拟定的研究[D],浙江大学硕士学位论文,2004.
[5]陈久宇.应用实测位移资料研究刘家峡重力坝横缝的结构作用[J].水利学报,1982(12)
[6]吴中如.混凝土坝观测物理量的数学模型及其应用[J].华东水利学院学报,1984(3).
[7]吴中如,沈长松等.论混凝土坝变形统计模型的因子选择[J].河海大学学报,1988(6)
[8]加拿大大坝安全协会(CDSA)主编,大坝安全导则及其评注.
[9]Thun JLV. Application of decision analysis techniques in dam safety evaluation and modification. Proc of the ICOSD, C2.2, Rotterdam,1984:265-271.
[10]李君纯.水库大坝安全评判的研究[J].水利水运科学研究,1999(1)
[11]中国科学院武汉岩土力学研究所,中国水电顾问集团昆明勘测设计研究院.糯扎渡水电站重大专项科研课题结题报告[M].2009,10.
[12]魏植生,何伟.糯扎渡水电站枢纽区主要工程地质问题研究[J].水利发电,Vol.31, No.5, 2005,5.
[13]张宗亮.糯扎渡水电站工程特点及关键技术研究[J].水利发电,Vol.31, No.5,2005,5.
[14]魏植生,何伟.糯扎渡水电站枢纽区主要工程地质问题研究[J].水利发电,Vol.31, No.5, 2005,5.
[15]昆明勘测设计研究院.糯扎渡水电站可行性研究报告[R],2005.
[16]卓战伟,张维熙.糯扎渡水电站右岸3#导流洞堵头段混凝土施工技术[J].科技信息,2009,12.
[17]崔长勇,曹桂凤,马双科,唐垂柳.某水电工程导流洞施工期安全监测分析[J].土木基础,Vol.23, No.3,2009.
[18]Chrzanowski, Adam, Szostak-Chrzanowski, Anna.Automation of deformation monitoring techniques and integration with Prediction modeling[J], Geomatica,2010,64(2).
[19]Mallat S.A Theory of Multiresolution Signal Decomposition:The Wavelet Transform[J].IEEE Trans On Pattern Machine Intelligence,1989,11(7):674-693.
[20]胡昌华等.基于MATLAB 6.X的系统分析与设计——小波分析[M].西安：西安电子科技大学出版社,2004.1.
[21]陈志坚,陈松,董学武,李筱艳.岩土工程安全监测异常值属性的识别方法[J].2004,1,40-43.
[22]魏治文,程琳,来记桃,吴火兵.几种异常值判别准则在安全监测数据处理中的应用[J].大坝与安全,2009,1,67-69.
[23]Zadeh I.A.Fuzzy Logic Computing with Words[J].IEEE Trans.on Fuzzy Systems,1996(2): 103-111.
[24]史忠植.神经网络[M].北京：高等教育出版社,2009.5.
[25]张德丰等MATLAB神经网络应用设计[M].北京：机械工业出版社,2009,1.
[26]Tsuking H.Extraction Rules for trained Neural Networks.IEEE Trans NN,2000,11(2):377-389.
[27]Chen-Chia Chuang,Jin-Tsong Jengl.CPBUM neural networks for modeling with outliers and noise[J].Applied Soft Computing,2007,(7):957-967.
[28]Zhao Erfeng,Jin Yongqiang.Dam Deformation Monitoring Model and Forecast Based on Hierarchical Diagonal Neural Network.Proceedings of the 7th World Congress on Intelligent Controland Automation,2008.
[29]Anon.The design,construction and performance of concrete faced rockfill dams[J]. International Journal on Hydropower and Dams,2000,7 (6):56-60.
[30]黄铭.数学模型与工程安全监测[M].上海：上海交通大学出版社,2008.
[31]张德丰,周燕主MATLAB在统计与工程数据分析中的应用[M].北京：电子工业出版社,2010,6.
[32]唐彤芝,李国英,徐竹青.土石坝沉降统计预报模型[N].水利水运工程学报,2001(3)：29-34.
[33]陈亮,黄铭.土石坝施工期沉降影响因子确定及监测模型分析[J].水电自动化与大坝监测,2004,6,45-48.
[34]肖浩波,顾冲时,张燕.面板堆石坝施工期沉降二维时空分析模型[J].水电自动化与大坝监测,2005,12,46-48.
[35]王孝仁,王松桂编译.实用多元统计分析[M].上海：科学技术出版社,1990.
[36]郭禄光,攀功瑜编.最小二乘法与测量平差[M].上海：同济大学出版社,1985,7.
[37]汪应洛.系统工程[M]北京：机械工业出版社,2011,6.
[38]李景龙.大型地下洞室群工程稳定性风险评估系统及其应用研究[D].济南：山东大学,2008.
[2]张风堂.糯扎渡水电站导流隧道围岩稳定及支护研究[D].南京：河海大学,2008.
[3]钱正英.中国水利[M].北京：水利电力出版社,1991.
[4]范庆来.大坝监测资料分析与安全指标拟定的研究[D],浙江大学硕士学位论文,2004.
[5]陈久宇.应用实测位移资料研究刘家峡重力坝横缝的结构作用[J].水利学报,1982(12)
[6]吴中如.混凝土坝观测物理量的数学模型及其应用[J].华东水利学院学报,1984(3).
[7]吴中如,沈长松等.论混凝土坝变形统计模型的因子选择[J].河海大学学报,1988(6)
[8]加拿大大坝安全协会(CDSA)主编,大坝安全导则及其评注.
[9]Thun JLV. Application of decision analysis techniques in dam safety evaluation and modification. Proc of the ICOSD, C2.2, Rotterdam,1984:265-271.
[10]李君纯.水库大坝安全评判的研究[J].水利水运科学研究,1999(1)
[11]中国科学院武汉岩土力学研究所,中国水电顾问集团昆明勘测设计研究院.糯扎渡水电站重大专项科研课题结题报告[M].2009,10.
[12]魏植生,何伟.糯扎渡水电站枢纽区主要工程地质问题研究[J].水利发电,Vol.31, No.5, 2005,5.
[13]张宗亮.糯扎渡水电站工程特点及关键技术研究[J].水利发电,Vol.31, No.5,2005,5.
[14]魏植生,何伟.糯扎渡水电站枢纽区主要工程地质问题研究[J].水利发电,Vol.31, No.5, 2005,5.
[15]昆明勘测设计研究院.糯扎渡水电站可行性研究报告[R],2005.
[16]卓战伟,张维熙.糯扎渡水电站右岸3#导流洞堵头段混凝土施工技术[J].科技信息,2009,12.
[17]崔长勇,曹桂凤,马双科,唐垂柳.某水电工程导流洞施工期安全监测分析[J].土木基础,Vol.23, No.3,2009.
[18]Chrzanowski, Adam, Szostak-Chrzanowski, Anna.Automation of deformation monitoring techniques and integration with Prediction modeling[J], Geomatica,2010,64(2).
[19]Mallat S.A Theory of Multiresolution Signal Decomposition:The Wavelet Transform[J].IEEE Trans On Pattern Machine Intelligence,1989,11(7):674-693.
[20]胡昌华等.基于MATLAB 6.X的系统分析与设计——小波分析[M].西安：西安电子科技大学出版社,2004.1.
[21]陈志坚,陈松,董学武,李筱艳.岩土工程安全监测异常值属性的识别方法[J].2004,1,40-43.
[22]魏治文,程琳,来记桃,吴火兵.几种异常值判别准则在安全监测数据处理中的应用[J].大坝与安全,2009,1,67-69.
[23]Zadeh I.A.Fuzzy Logic Computing with Words[J].IEEE Trans.on Fuzzy Systems,1996(2): 103-111.
[24]史忠植.神经网络[M].北京：高等教育出版社,2009.5.
[25]张德丰等MATLAB神经网络应用设计[M].北京：机械工业出版社,2009,1.
[26]Tsuking H.Extraction Rules for trained Neural Networks.IEEE Trans NN,2000,11(2):377-389.
[27]Chen-Chia Chuang,Jin-Tsong Jengl.CPBUM neural networks for modeling with outliers and noise[J].Applied Soft Computing,2007,(7):957-967.
[28]Zhao Erfeng,Jin Yongqiang.Dam Deformation Monitoring Model and Forecast Based on Hierarchical Diagonal Neural Network.Proceedings of the 7th World Congress on Intelligent Controland Automation,2008.
[29]Anon.The design,construction and performance of concrete faced rockfill dams[J]. International Journal on Hydropower and Dams,2000,7 (6):56-60.
[30]黄铭.数学模型与工程安全监测[M].上海：上海交通大学出版社,2008.
[31]张德丰,周燕主MATLAB在统计与工程数据分析中的应用[M].北京：电子工业出版社,2010,6.
[32]唐彤芝,李国英,徐竹青.土石坝沉降统计预报模型[N].水利水运工程学报,2001(3)：29-34.
[33]陈亮,黄铭.土石坝施工期沉降影响因子确定及监测模型分析[J].水电自动化与大坝监测,2004,6,45-48.
[34]肖浩波,顾冲时,张燕.面板堆石坝施工期沉降二维时空分析模型[J].水电自动化与大坝监测,2005,12,46-48.
[35]王孝仁,王松桂编译.实用多元统计分析[M].上海：科学技术出版社,1990.
[36]郭禄光,攀功瑜编.最小二乘法与测量平差[M].上海：同济大学出版社,1985,7.
[37]汪应洛.系统工程[M]北京：机械工业出版社,2011,6.
[38]李景龙.大型地下洞室群工程稳定性风险评估系统及其应用研究[D].济南：山东大学,2008.