摘要
近几十年来，随着经济实力的不断增强，我国的基础建设发展迅猛，涌现了大量大跨径桥梁和大型空间结构。但这些大型工程结构在长达几十年、甚至上百年的服役期间，受到环境载荷作用、疲劳效应、腐蚀效应和材料老化等不利因素的影响，结构不可避免地产生损伤积累、抗力衰减，故对结构的健康状况进行评估显得非常有必要。对这些大型工程结构进行基于环境激励的模态测试，然后利用输出响应识别结构模态参数，再利用模态参数进行状况评估是目前国内外应用很广泛的结构状况评估方法。但环境激励下，由于噪声的干扰、环境激励的不充分以及受到环境温度、湿度、风荷载、交通荷载环境因素等的影响，存在着测试信号的信噪比低、结构模态参数随环境因素变化而变化等问题，这些问题导致一些评估方法虽然在数值或实验室模型中有效，但却很难直接应用到实际工程中。本文以崖门大桥、杜坑特大桥及广州新电视塔Benchmark为工程背景，对上述问题进行研究，主要内容包括以下几个方面：
(1)基于遗传算法-奇异值分解(GA-SVD)的自适应降噪方法。针对基于SVD降噪方法中难以确定矩阵维数p和重构阶次k的问题，提出基于GA-SVD的自适应降噪方法，利用GA的优化寻优功能，使得上述两个关键参数能自适应地确定。通过对两组含不同频率成分的仿真信号进行降噪处理，总结出取N4或N3（N为数据长度），重构阶次可取信号频率主成分个数2倍的规律。但考虑到实测信号的频率成分比较复杂，为了避免遗漏真实的频率成分，对实测信号进行降噪处理时建议取重构阶次应该大于真实频率主成分的2倍，仍旧取或即可。
(2)对自然激励技术/特征系统实现算法(NExT/ERA)算法中关键参数的取值进行探讨。基于两座桥梁的实测振动信号，研究了NExT/ERA算法中快速傅里叶变换抽取点数(NFFT)、分析数据的长度和Hankel矩阵的维数这三个关键参数的选取问题，指出：①NFFT过大反而会减少平均次数，造成数据的信噪比低，建议取值为1024或2048；②而分析数据长度不能过小，应至少大于第一阶自振周期T1的60倍才能使识别的模态参数稳定、一致；③对于如何确定Hankel矩阵的维数，提出一取值经验公式，并通过这两座桥梁的实测振动信号进行分析验证。
(3)介绍了几种常用的模态精度判别指标，对基于输出矩阵的一致模态指标（CMI_O）进行补充，通过数值算例验证其可靠性并提出合适的取值范围。
(4)针对激励未知情况下，运行模态分析容易遗漏真实模态和产生虚假模态的问题，提出了基于NExT/ERA的多参考点稳定图算法(M-NExT/ERA)。该算法通过设置不同的参考点，利用NExT/ERA识别模态参数，在每一步循环中以阻尼比、CMI_O和模态振幅相干系数(MAC)作为判别标准，能自动确定可信度最高的模态参数，减少主观因素对识别结果的影响。通过一8自由度剪切模型作为数值算例，验证了该算法在非白噪声激励以及强噪声干扰下的适应性。
(5)MIDP模态参数识别工具箱的开发与工程应用。基于MATLAB平台，开发出模态参数识别工具箱―MIDP‖，使之具有数据载入、数据预处理、模态参数识别和结果后处理四种功能。通过同时使用MIDP和ARTeMIS软件对瑞士Z24桥（连续梁）进行模态参数识别，可知两种软件的识别结果很吻合，MIDP工具箱的可靠性得以验证，最后将MIDP应用于崖门大桥（斜拉桥）和杜坑特大桥（连续梁拱桥）的模态参数识别工作。
(6)环境因素影响下高耸结构模态参数的变异性分析。基于广州新电视塔(GuangzhouNew TV Tower, GNTVT)Benchmark的实测数据，分析了环境因素对模态参数的影响规律，可知模态频率值随着温度的增加而减少，但阻尼比值的识别精度不高、分布较为离散，难以观察到其变化趋势；利用修正后的ANSYS有限元模型分析温度对GNTVT模态频率的影响机理，可知无论是仅考虑温度影响材料弹模的情况，还是仅考虑温度影响结构内力的情况，温度的升高均会使结构频率降低，但前者所引起的频率改变量较大，应引起重视，后者所引起的频率改变量非常小，几乎可以忽略。
(7)建立环境因素与结构频率之间的非线性主成分分析-支持向量回归(NLPCA-SVR)模型。提出利用NLPCA消除各环境因素之间的相关性，得到温度和风速为环境因素的主要特征向量，然后输入到SVR模型的建模方法。在本文所讨论的问题上，通过利用网格搜索法（GSM）、遗传算法（GA）和果蝇优化算法（FOA）三种方法确定SVR模型最优的超参数，优化结果表明NLPCA-SVR模型比OriginalData-SVR模型具有更好的泛化性能。通过对NLPCA-SVR模型进行分析与检验，可知该模型可以对环境因素-模态频率数据进行较好的拟合与预测，结合消除环境因素影响的公式，能使前4阶模态频率变幅度减少，与均值更接近。由此可见，该方法具有一定的实用价值。
During the past few decades, along with the growth of China's economic development,infrastructure construction has developed rapidly and a lot of long-span bridges andlarge-scale space structures have been built. These large-scale civil engineering structures areprone to be damaged during their service lives, caused by factors such as complex conditionloads, fatigue effect, corrosion effect and material aging. So structural health monitoring(SHM) is very necessary. The ambient excitation method to identify large-scale structuremodal parameter is widely used for structural health monitoring. Different from traditionalone, modal parameter can be identified from output-only data in ambient excitation method.But due to noise interference, inadequately excitation or uncertain environmental conditions(for example, temperature, humidity, wind loads and traffic loads), it would meet someproblems in ambient excitation method, such as low Signal to Noise Ratio (SNR), modalparameter changes under different environmental conditions. Because of these problems,algorithms for SHM may work in numerical or laboratory model, but difficult to be applied topractical engineering. This dissertation focus on the core problems of output-only modalparameter identification and the Yamen Bridge, Dukeng Bridge and GNTVT Benchmark aretaken as engineering background. The main work and conclusions include as follows:
(1)An adaptive noise reduction method based on Genetic Algorithm (GA) and SingularValue Decomposition (SVD). The row number of reconstruction matrix (p) and the order ofeffective rank(k) both are difficult to determine for noise reduction based on singular valuedecomposition. An improved adaptive noise reduction method is proposed, using theoptimization function of GA, the two key parameters can be determined adaptively. Twonumerical simulation signals with different frequency components are employed. The resultsshow that can beN4orN3(N is the length of data), is twice as the number ofdominating frequency. For measured signal, the frequency components are more complicatedin order not to miss the true frequency components, when dealing with measured signals,should be more than twice as the number of dominating frequency, but can still beor.
(2)Investigation of the key parameters in NExT/ERA algorithm. The number ofextraction point for fast Fourier transform(NFFT), the length of data used in modaldentification and the dimension of Hanke matrix are the three key parameters in NExT/ERA algorithm. The three key parameters are investigated through the output-only data of twobridges and the results show that:①NFFT shouldn’t be too large, or it will reduce the timesof average,1024or2048is suitable;②the length of data shouldn’t be too less, it should atleast longer than60times as the first natural vibration period (T1) of the structure;③aempirical equation is proposed for determination of the demension of Hankel matrix and theequation is verified by the output-only data of two bridges.
(3)Several mode accuracy indicators are introduced. Though numerical simulations, theConsistent-Mode Indicator for Observability matrix (CMI_O) is investigated and verified, andthe suitable range of value for distinguishing the physically true modes from the spuriousmodes is proposed.
(4)For Operational modal analysis, the key issue is how to identify the weakly excitedmodes and distinguish the physically true modes from the spurious modes. An improvedmultiple reference DOFs stabilization diagram algorithm based on NExT/ERA(M-NExT/ERA) is presented. By setting different reference DOFs in each group of data,NExT/ERA is used to identify modal parameters. Damping ratio, CMI_O and ModalAmplitude Coherence (MAC) was used as threshold to identify the most accuracy modalparameters. A numerical simulation of8DOFs shear model is employed, the results show thatM-NExT/ERA is reliable to identify modal parameter accurately under the conditions ofnon-white noise excitaion and strong noise interference.
(5)Development and engineering application of MIDP toolbox for modal parameteridentification. Base on MATLAB, a modal parameters identification toolbox named "MIDP"is developed. Data loading, data pre-process, modal identification and data post-processfunctions are involed in MIDP. MIDP and ARTeMIS are bothe used to identify the modalparameter of Z24bridge(Continious bridge) in Swiss.The results show that the modalparameters identified from the two methods are in good agreement and MIDP is reliable. Atlast, MIDP is applied to modal identification of Yamen Bridge(Cable-stayed bridge) andDukeng Bridge(Continious-Arch ccomposite bridge).
(6)Variability analysis in dynamic properties of high-rise structure under differentenviromental conditions. Basded on the measured data of GNTVT Benchmark, the influencelaw of dynamic properties caused by different environmental conditions is analysed. Theresults show that modal frequencies decrease as the temperature rise, but it is hard to observethe trend of damping ratio. Temperature to frequency influence mechanism of GNTVT isinvestigated through the updated ANSYS model. The results show that both the material property and internal force state of GNTVT will change along with different temperature.Modal frequency decrease as temperature rise, and the change of material property is the mainreason of the change of modal frequency.
(7)NLPCA-SVR model between envriomental factors and modal frequency.NLPCA-SVR mothod is proposed, NLPCA is first applied to eliminate the correlation amongenvriomental factors and extract principal components from the envriomental factors fordimensionality reduction. The predominant feature vectors in conjunction with the measuredmodal frequencies are then fed into a support vector algorithm to formulate regression models.Grid serach method (GSM), Genetic Algorithm (GA) and Flying fruit optimization algorithm(FOA) are used to determine the hyper-parameters of the SVR models.The results show thatNLPCA-SVR model is better than OriginalData-SVR model, it can accurately fit and predictthe changes of frequencies along with temperature changes.
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