Research On Pile Foundation Defect Detection Technology Based On Wavelet And Support Vector Machine

Cast-in-place pile foundation (hereinafter referred to pile foundation) is builtof reinforced concrete underground structures, used for carrying civil constructionsuch as bridges or buildings. Certain pile foundation defects will exist after thecompletion because of the subjective or the objective factors, which directly affectthe pile load-bearing properties related to the quality, safety and service life of theconstruction. The pile foundation with the serious defects can not carry rated loadof the building, it can easily lead to major engineering accidents, or even result insignificant loss of personal and state property. Therefore, after the pile foundation isbuilt, the presence and the extent of defects of pile foundation must be placed onrigorous detection to identify the quality type of the pile foundation. The pilefoundation with the serious defects has to be dealt with accordingly.There are four detection methods prescribed by the pile foundation defectdetection standard, the low strain stress wave reflected method (referred to as stresswave reflection method), the ultrasonic transmission method, the high straindetection method and the borehole method. The stress wave reflected method is themost important detection methods among them. This method has a disadvantage ofpoor accuracy of detecting the pile foundation type, degree and position. However itis widely used in our country because of its low cost and shortcut. Wavelet analysis,support vector machine, window extraction of feature vector, as well asone-dimensional component and other mathematical theories are applied to improvethe existing stress wave signal analysis methods, which to improve the accuracy ofdetecting resultBased on wavelet theory and relation degree theory, this thesis extracts thedefect wavelet from a typical defect waveform, and constructs pile foundationwavelet function. The relation degree theory and method applied can ensure goodsimilarity between pile foundation wavelet function and defects wavelet, whichimprove the accuracy of detecting results and the adaptability of differentgeological environments by applying pile foundation wavelet function.The existing feature vector extraction method has the disadvantage of thesignal time information missing and lacking the ability of detecting multi-defect,this thesis proposed the window extraction method of feature vector, explained howto choose the parameters of window width and translational step. The matrix modelof feature vector was built for window extraction method. The experiment and theaccuracy analysis of pile defects detection based on window extraction method were made.The support vector machine classification performance advantages and thecharacteristics of various pile defects were adopted to improve support vectormachine multiple classifiers structure. The multi-layer one-to-one support vectormachine classifiers and its model based on pattern recognition theory wereproposed. The comparative test and performance analysis between multi-layersupport vector machine classifiers and PB classifiers were carried out.The Thesis analyzed uncertainty factors of the current pile defect degreemeasurement and proposed the power spectrum based measurement methods of thepile defect location and defect levels by applying the power spectrum of defectclassification and intermediate results, etc. It provide of a simple and effectivemeasurement of the degree of pile defect.