变形监测网在变形监测工作中布设原理与监测方法的探讨

变形是指构筑物或者建筑物在外界各种相关因素的影响、作用下,构筑物或者建筑物的大小以及形状发生改变的过程。变形监测通常是指利用普通测绘仪器或者专用仪器和设备对构筑物或者建筑物的变形状况进行监测的测绘活动。变形监测目的是通过相应仪器的测量、监测以获取构筑物或者建筑物的空间位置随时间变化而改变的特征,并根据监测的数据分析引起构筑物或者建筑物变形的因素。变形监测的主要原理是通过监测构筑物或者建筑物上有一定代表性的变形监测点的数据变化来确定构筑物及建筑物的变形,变形监测根据测量方式和测量仪器的不同,分为静态变形监测和动态变形监测。静态变形监测主要方式是周期观测,动态变形监测主要方式是一直持续观测。变形监测主要依靠变形监测网开展实施,变形监测网由基准点、工作基点、变形观测点等各种监测点组成。变形监测网中所有监测点的布设对于所有的变形监测都有共性,一般根据学科理论知识加以确定。同时,因为变形监测网监测数值的细微性,变形监测网除有其他控制网的特性外,还需要有一定可靠性。本文主要讨论变形监测网在实际变形监测工作中布设原理与变形监测方法,分析变形监测网中各监测点的布点位置、埋设方法,以及变形监测网中各监测...     

基于经验模态分解和遗传支持向量机的多尺度大坝变形预测

 利用经验模态分解算法分解大坝变形数据,得到不同物理特征尺度的变形分量,分析各变形分量特征及其相关影响因素。针对各变形分量的特点,分别建立基于GA-SVM的各变形分量预测模型,将各分量预测模型相加,最终构建基于经验模态分解和支持向量机的多尺度变形预测模型。由大坝变形数据的经验模态分解实例分析,证实经验模态分解算法能有效对大坝变形数据进行多尺度分解,由经验模态分解算法分解得到的各变形分量其物理特征更加显著,更易于各变形分量影响因素分析和变形模型建立,因此,针对各变形分量的特点所建立的GA-SVM的各变形分量模型具有较高精度。基于经验模态分解和支持向量机的多尺度变形预测模型由各分量预测模型相加而得,能充分挖掘大坝变形中隐含的多种内在规律,能同时在不同特征尺度上进行大坝变形预测。通过对多尺度大坝变形预测模型和多元回归、时间序列分析、GM(1,4)、BP网络和GA-SVM大坝变形预测模型进行精度对比,证实基于经验模态分解和支持向量机的多尺度变形预测模型是一种精度较高的大坝变形预测新方法。     

钢结构建筑物变形监测方案设计

介绍了变形监测对物体的安全维护与运作的重要作用,阐述了目的、意义及基础理论等,重点对钢结构建筑物变形监测方案设计思路、数据处理、观测周期的确定方法及变形监测网点的布设等进行论述。     

中国矿业大学图书馆大楼沉降变形监测

以中国矿业大学图书馆大楼的变形监测实例介绍了建筑物变形监测的周期确定、点位布设等技术设计,并分析了选用仪器及设计路线的精度,通过成果资料的整理和分析,掌握了建筑物的沉降动态,验证了建筑物的设计,绘制了建筑沉降等值线图,为确保建筑物今后的正常施工和安全运营提供了可靠的依据。     

基于独立成分分析的尖轨导波监测信号处理方法研究

道岔尖轨属于一种非规则变截面波导,复杂的结构导致其在应用导波进行检测时,存在的模态众多,而尖轨本身也处于一种动态且复杂的工作状态,以上因素造成对于超声导波监测信号使用传统的残差分析的方法难以解读的问题。因此,通过基准信号与当前信号在同一特征空间分解的方式,对空间中表征缺陷对于监测信号影响的维度进行了分析,提出了基于独立成分分析的尖轨导波监测信号处理方法,在理论分析的基础上使用超声导波监测仪对尖轨进行了模拟监测实验。研究结果表明:使用本处理方法能够对尖轨导波监测信号进行分解,从中提取有效信息实现缺陷的准确判别与定位,对于同一组缺陷,监测结果信噪比残差分析法更高。     

基于SSA-LMD-GM的大坝变形组合预测模型

为提高大坝变形预测精度，针对大坝原始监测信号中的噪声，以及其非平稳性、非线性等特点，引入奇异谱分析(SSA)和局部均值分解(LMD)方法，提出SSA-LMD-GM模型。采用奇异谱分析(SSA)对原始监测信号进行去噪处理，为充分提取大坝形变信息特征，利用局部均值分解(LMD)对去噪后的监测信号进行分解。针对乘积函数(PF)分量的特征采用合适的模型预测分析，剩下余项则采用GM(1,1)模型。利用实际工程案例进行检验，结果表明，相较于其他模型，SSA-LMD-GM模型预测精度和拟合精度更加优秀，能较好地预测大坝变形趋势，具有一定的应用价值。     

建筑物的垂直变形监测

介绍了垂直变形监测在建筑物安全监测中的意义，从监测基准的建立、沉降观测点的设置、观测精度和周期的确定、监测外业的实施、计算整理等方面论述了垂直变形监测的方法，以达到为建筑工程防灾减灾的目的。     

基于本征模态函数重构的近断层地震动识别与响应谱验证

为了充分考虑近断层地震动对中长周期结构的不利影响,提出了一种近断层速度脉冲型地震记录的量化识别方法。该方法基于经验模态分解(EMD)数据自驱动特征,充分考虑了本征模态函数(IMF)对原始记录脉冲尺度和形状的自适应匹配能力,以及单阶IMF固有的平滑特征,借助Tanimoto相似度的去中心化表达,给出了一种基于IMF信号自适应重构的地震记录去噪算法,用于识别主脉冲的幅值和周期。为了对主脉冲包含的能量进行度量,给出了基于指数函数的脉冲能量指示器,并验证了其有效性。通过与既有方法在周期、波形、反应谱等各方面的对比表明,本征模态函数与原始地震记录信号间的相似度可以作为筛选本征模态的重要依据。动力响应反应谱分析表明,抽取的脉冲对主脉冲中长周期的动力特性保留更为完整。总体上,由于经验模态分解方法的数据自驱动效应,所提方法对波形显著非对称的脉冲和多脉冲记录识别更为有效。     

应用小波分析对监测建筑物变形的GPS数据进行处理的方法研究

介绍了应用小波分析对监测建筑物变形的GPS数据进行处理的方法。通过将监测某建筑物变形的GPS数据结果加入粗差后的信号波形分别进行傅里叶变换去噪和小波变换去噪,结果显示,采用小波变换去噪得到的波形质量要优于采用傅里叶变换得到的波形。可以得出:采用小波分析对监测建筑物变形的GPS数据进行处理具有良好的适应性,可作为GPS变形监测数据处理的主要方法。     

基于激光扫描的邻近基坑建筑物变形监测技术

深基坑开挖施工过程中必须对邻近建筑物的变形进行监测。鉴于目前变形监测常用的散点式坐标直接监测法难以实现对建筑物整体变形特征进行监测的缺陷,尝试采用三维激光扫描技术对基坑周边建筑物变形进行监测。该方法先对建筑物整体进行粗扫,对建筑物外轮廓和参照不动点进行高精度扫描,拼合成为含有参照不动点和建筑物的合理点云数据,再通过对前后两次点云数据的整体对比,获得建筑物的整体变形特征。监测结果表明,该方法扫描效率高,监测结果和精度基本满足工程要求。     

基于小波消噪的EMD模型在GPS振动信号处理中的应用

经验模式分解和小波分解是当前有效处理非平稳信号的两种时频分析方法,它们各具有其优缺点,适用于不同的应用.从对GPS振动信号的预处理、时域和频域处理入手,结果分析表明,对GPS振动信号先进行小波滤波消除随机噪声的干扰,再应用经验模式分解更有利于变形特征信息的分离和提取,提取的信号与振动平台记录数据更加吻合.     

爆破微差延时的EMD识别法

EMD（Empirical Mode Decomposition）方法是基于信号的局部特征的信号分解方法。能把复杂的信号分解为有限的具有物理意义的固有模态函数（Intrinsic Mode Function）。它自适应性强且非常适合处理非平稳信号。文章提出了爆破微差延时的EMD识别法,并以某工程中的微差爆破监测到的爆破振动信号为例。利用EMD法将爆破振动信号分解成IMF分量,再通过Himen变换提取IMF主成分分量的包络幅值来达到识别实际微差延时的目的。     

Continuous modal parameter identification of cable-stayed bridges based on a novel improved ensemble empirical mode decomposition

The objective of this paper was to perform an effective and meticulous continuous modal parameter identification. Since the data obtained from the cable-stayed bridge was non-linear and time varying, and also there exists a phenomenon of mode mixing in the current decomposition techniques, such as empirical mode decomposition (EMD), which further complicates the extraction of accurate structural information, therefore, a novel improved Ensemble EMD method was proposed. This method can effectively deal with the non-linear and time varying structural behaviour and eliminate the phenomenon of mode mixing effectively, specially for cable-stayed bridges, because in this method the added white noise was selected by a pre-defined process and also the intrinsic mode function (IMF) selection was made self-adaptively, then finally Pareto technique was adopted to reconstruct the IMF. After the signal decomposition and reconstruction, Recursive Stochastic Subspace Identification was employed to carry out the continuous modal parameter identification. Sutong Yangtze Bridge, a long-span cable-stayed bridge, with main span of 1088 m was taken as a case study and the proposed method was applied. The result showed that the proposed method was effective in attaining its goals and can endows better results in real life bridge health monitoring.     

小波分解技术在深基坑监测数据处理中的应用

从小波分解技术的强大的去噪功能出发,提出了基于小波技术的监测数据前处理方法.结合润扬长江公路大桥南锚碇排桩冻结法深基坑的变形监测数据,利用小波分解与重构手段,对变形监测数据进行消噪滤波处理,结果表明,小波分解技术去噪合理有效,可以有效地从误差干扰的变形监测数据中提取数据所反应的原始特征,同时,也不需要监测数据的先验知识,为监测数据的误差消除的一种有效方法,具有一定的理论价值和应用价值.     

EMD-based random decrement technique for modal parameter identification of an existing railway bridge

Vibrational measurement data are often nonstationary and modal parameter identification based on these data is of practical value for structural health monitoring and condition assessment. The empirical mode decomposition (EMD) is a most recent tool for analysis of nonstationary signals. An EMD-based random decrement (RD) technique is presented to identify modal parameters from monitoring vibrational data. The nonstationary measurement data are first decomposed into a series of quasi-stationary intrinsic mode functions (IMFs) by EMD. The RD technique is then applied to the selected IMFs to obtain the free-decay response. The modal frequencies and damping ratios are finally identified from the free-decay response by minimizing the error between the measured free-decay responses and the predicted responses from a parametric model. The present method is applied to extract the modal parameters of the Nanjing Yangtze River Bridge from the measured responses. The identification result is compared to those from finite element analysis as well as from the experimental result identified with the peak-picking (PP) method. In addition, the modal frequencies of the bridge loaded with heavy trains are also identified and compared to the ‘empty’ bridge. The EMD-based random decrement (RD) technique provides an effective and promising tool for modal parameter identification for large bridges and other structures.     

基于EMD小波的高速电梯振动信号去噪

结合经验模态分解与小波阈值去噪方法,提出基于EMD小波的高速电梯振动信号去噪方法,并对高速电梯振动信号进行了实例分析。结果表明,应用基于EMD小波阈值去噪方法相比于低通滤波去噪方法与单纯小波阈值去噪方法,具有更高的信噪比,能够提高分析的精度。有效保留了信号特征。     

Inverse analysis of deformation moduli for high arch dams using the displacement reconstruction technique and multi-objective optimization

The inverse analysis of the deformation moduli of high arch dams based on displacement monitoring data is essential for structural safety assessment. In traditional inverse analysis methods, the deformation moduli are identified based on the single-objective optimization and the hydrostatic component derived from the statistical model. This type of method has two main shortcomings: First, it treats the essential multi-objective optimization problem as a single-objective problem; second, the extracted hydrostatic component may be biased due to the multicollinearity of variables in the statistical model. This paper presents a methodology for the inverse analysis of the deformation moduli of high arch dams under a multi-objective optimization strategy. The methodology employs empirical mode decomposition to extract the aging component from displacement monitoring data. Then, thermomechanical analysis is used to reconstruct the remaining hydrostatic and temperature components, thereby avoiding the biases encountered in solving the statistical model. The adaptive polynomial chaos expansion method is embedded in the NSGA-III algorithm to establish and solve multi-objective functions in the inverse analysis. Additionally, a composite decision index considering errors and test information is proposed to determine acceptable deformation moduli from the Pareto solution set. A high arch dam is selected to illustrate this methodology with static and dynamic monitoring data. The results show that the identified deformation moduli have errors of 3.8% and 7.2% in displacement and acceleration, respectively. The proposed methodology can yield deformation modulus values that are more consistent with the physical implications than those of the single-objective optimization method.     

组合EMD和PCA的多路径改正方法

多路径效应及高频随机噪声是目前影响GPS变形监测精度的主要因素。针对GPS变形监测中的多路径效应与高频随机噪声的周日重复性特征,提出了EMD(Empirical Mode Decomposition)与PCA(Principal Component Analysis)组合的GPS噪声改正方法。该方法先通过EMD对各天数据剔除高频随机噪声,再采用PCA对相关性较强的多路径效应进行提取和消除。实测数据分析表明,该组合方法能有效地削弱多路径效应及高频随机噪声,较单一滤波方法具有一定的优越性。     

Time-frequency analysis of typhoon effects on a 79-storey tall building

Di Wang Tower located in Shenzhen, PR China, has a height of approximately 325 m and is a 79-storey tall building. This paper presents selected results of full-scale measurements of typhoon effects on Di Wang Tower. Wind speed, wind direction, wind-induced acceleration and displacement responses were simultaneously and continuously measured from the super tall building with anemometers, accelerometers and global position system (GPS) during a typhoon. The advanced data analysis method called Hilbert-Huang transform (HHT) was adopted in this study to analyze the non-stationary characteristics of wind speed and wind-induced responses of this building under typhoon condition. By using the empirical mode decomposition (EMD) method, the measured data were decomposed into several inherent intrinsic mode functions (IMFs). The probability density and power spectral density of fluctuating wind speed were obtained by traditional methods and were further analyzed by considering time-varying mean values of the measured data via the EMD method. The wind-induced responses with non-stationary features were studied by applying the HHT to each IMF for obtaining their instantaneous frequency and Hilbert-Huang composite spectrum. Meanwhile, the transient energy distributions of the wind-induced responses were analyzed in time-frequency domain, which were compared with the traditional power spectral densities obtained from the fast Fourier transform (FFT) method and those from the wavelet transform. Furthermore, the amplitude-dependent damping ratios were determined by combining the EMD and the random decrement technique (RDT) method. Through comprehensive analysis of the measured data, it was testified that the HHT method is a promising tool for the time-frequency analysis of random signal and can serve as a flexible and effective tool for analyzing field data of wind speed and wind-induced response with non-stationary features.