The Application Of Support Vector Machine Method To Structural Damage Identification

In this dissertation, the state of arts of the researches about structural damageidentification is depicted, and the rationale of support vector machine and thearithmetic are also introduced. Then the background of the research project and thecontent of this dissertation are outlined. This dissertation focuses on patternrecognition and online parameter identification, and improves the traditional methodto increase the precision, efficiency and robustness of the identification.Pattern recognition is a simple and convenient method, but whether the dynamicfingerprint is right or not makes great effect on the results. The natural frequency isan easy obtained dynamic fingerprint, further more the measure error of it is least. Butthe traditional method is insensitive to the natural frequency, and it is difficult toidentify the degree of the damage. Other dynamic fingerprints are more sensitive todamage, but the great measure error limits their application. However, the supportvector machine method overcomes the drawback of the traditional method, which isinsensitivity to the natural frequency. It can not only recognize the location of damage,but also can identify the degree of damage.Kernel function is the key for the support vector machine in the patternrecognition. It reflects the relation between the dynamic fingerprint and damage, andaffects the result of identification. Combining the wavelet function and Goss Kernelfunction, the Wavelet Kernel function is presented. Numerical analysis shows that theWavelet Kernel function is evidently better than the others, especially for the case ofmultiple damages.The Wavelet Kernel function LS-SVM is an convenient method for patternrecognition, further more it is more sensitive to the change rate of natural frequencywhich has less measure error. This new method remedies the shortages of traditionalmethod. The result of numerical simulation about a 10-story frame shows that thismethod has a better precision. Especially under the condition of single damage, it cannot only recognize the location of damage correctly, but also has a high precision on the damage degree. The machine which trained by single damage is also suitable tothe multiple damage. Under the condition of multiple damage, although the result ofdamage degree identification has some errors, it also can get the satisfying result andhas better robustness as well.The technique of online identification can identify the structural dynamicparameters directly, which can fit the need of project better. Least Square SupportVector Machine (LS-SVM) is an improvement on traditional methods, which makesa great improvement on the training speed. But LS-SVM method loses the matrix'ssparseness of standard SVM method. Furthermore, it needs to re-resolve the wholelinear equation set when a new set of data is input. So the LS-SVM method still hassome shortages such as low identification precision, low identification efficiency, andweak robustness. Therefore, improved method based on LS-SVM is put forward inthis dissertation.At first, the incremental weighted eigenvector LS-SVM regression method basedon the incremental LS-SVM regression method is presented. This new methodimproved the identification precision via weighting the eigenvector anddistinguishing the contribution magnitude of every sample. But the incrementalalgorithm puts the whole data into the identification sample set, which makes thecomputing efficiency lower. It requires relatively higher hardware to implement theonline identification. Thus, the equal sample LS-SVM recursion algorithm ispresented, according to partial incremental algorithm and pruned algorithm to updatethe sample in time, when the new sample is input, the outdated sample is eliminatedat the same time. The repeating matrix inversions are avoided to greatly improve thecomputing efficiency. Consequently, the identification online becomes possible.On the base of the equal sample LS-SVM, the adaptive error weighted LS-SVMand the adaptive eigenvector exponential weighted LS-SVM are presented. These twonew methods put the eigenvector different weight according to the error. Thereby, theprecision and robustness are improved. Some numerical simulation examples,including different damage patterns and different forms of noise, are analyzed, whichindicate that the weighted equal sample LS-SVM methods are excellent methods foronline identification, which are more accurate and efficient, and have better robustness as well, especially for the adaptive eigenvector exponential weightedLS-SVM method.The methods introduced above are applied to an experimental structure which isa 12-story frame. The result of the damage identification is in accord with thephenomena of the experiment by and large.At last, the main contributions and conclusions of this dissertation aresummarized and some problems which need further research are set forward.