【作者】 晋侃；

【导师】 薛松涛；

【作者基本信息】 同济大学 ， 结构工程， 2005， 硕士

【摘要】 随着高层及超高层建筑的日益增多,对结构在突发荷载(地震、台风、爆炸等)后的健康状况做出评估已经成为一个迫切需要解决的问题。本文首先从结构健康监测的概念出发,简述了当前此领域的研究状况和发展趋势,介绍了本论文的主要研究内容。 传统的统计学研究的是样本无穷大时的渐近理论。然而在实际的问题中,样本数往往是有限的。现有的基于传统统计学的学习方法在有限样本的情况下难以取得理想的效果。统计学习理论是在有限样本情况下新建立起来的统计学理论体系。统计学习理论为人们系统地研究小样本情况下机器学习问题提供了有力的理论基础。支持向量机是在该理论体系下产生的一种新的、非常有力的机器学习方法。它较好地解决了以往困扰很多学习方法的小样本、非线性、过学习、高维数、局部极小点等实际问题,具有很强的推广能力。本文从支持向量机理论、方法和应用相结合的角度出发,在统计学习理论、支持向量机回归和核函数方面进行了系统的研究。 标准的支持向量机方法需要求解二次规划问题,在数据集比较大时求解非常困难,并且由于标准支持向量机处理数据的方式是批处理,不适合于动态系统。最小二乘支持向量机是标准支持向量机的扩展,通过采用等式约束和最小二乘损失函数简化了支持向量机方法的求解过程。本文在最小二乘支持向量机方法的基础上提出了用于识别结构系统的增量最小二乘支持向量机和序贯最小二乘支持向量机方法,当新样本加入进来或老的样本被修剪下去的时候可以有效地更新,这种方法克服了标准最小二乘支持向量机方法的稀疏性缺失,使得结构参数的在线识别成为可能。对线性单自由度、线性多自由度、非线性单自由度数值模拟的结果证明了所提出方法的鲁棒性和有效性以及对损伤时刻和位置的跟踪能力。 为了验证最小二乘支持向量机方法在实际应用中的表现,本文对振动台试验模型处于弹性阶段的层间刚度进行识别,采用识别刚度建立的系统模型和实际结构在相同地震作用下加速度时程的比较证明了识别结果的正确性。 最后对全文的主要工作和研究结果进行总结,并指出了课题中有待进一步更多还原

【Abstract】 As more and more high buildings are constructed, it is imperative to assess health states of the structures after catastrophic events, such as earthquake, typhoon and explosion. In the first chapter of this thesis, from the viewpoint of structural health monitoring, the state-of-art of the researches about structural health monitoring is depicted, some ongoing implementations of structural monitoring systems are introduced, the background of the research project and the content of this thesis are outlined also.Traditional statistical theory aims at the asymptotic theory when sample size is tend to infinity. However, in many practical cases, samples are limited. Most of existing method based on traditional statistical theory may not work well for the situation of limited samples. Statistical Learning Theory (SLT) is a new statistical theory framework established from finite samples. SLT provides a powerful theory fundament to solve machine learning problem with small samples. Support Vector Machine (SVM) is a novel powerful machine learning method based on SLT. SVM solves practical problems such as small samples, nonlinearity, over learning, high dimension and local minima, which exist in most of learning methods, and has high generalization. In this thesis, SLT, support vector regression and kernel methods are studied from the respective of integration of theory, algorithm and application.The standard SVM need to solve a quadratic problem, it is time-consuming for large data sets and not suitable to solve dynamic problems. Least Squares Support Vector Machines (LS-SVM) is an SVM version which involves equality instead of inequality constraints and works with a least squares cost function, the problem is simplified by this way. Based on the Least Squares Support Vector Machines method, Incremental LS-SVM and Online LS-SVM method is proposed for identification of structural systems in this paper. It efficiently updates a trained LS-SVM by means of incremental and decremental pruning algorithms whenever a sample is added to, or removed from, the training set. The method overcomes the drawback of sparsenesslost within the standard LS-SVM and makes online training for the LS-SVM possible. Examples of linear and nonlinear hysteretic structure parameters for online identification problems show the robustness, efficiency, and capability of damage tracing of the proposed method.To verify the practical performance of LS-SVM method, an experimental model under the excitation of EL Centra wave was studied. The LS-SVM method identified the stiffness of the model in the elastic phase only from acceleration records. Comparison between the actual records and identified records showed the efficiency of LS-SVM.At last, the main contributions and conclusions of this thesis are summarized and some problems to be addressed about the research are set forward.更多还原