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研究生: 羅狄
APRIADI,
論文名稱: 基於震動量測之鋪面損害識別之初探
Preliminary Study on The Vibration-Based Pavement Distress Identification
指導教授: 楊士賢
Yang, Shih-Hsien
學位類別: 碩士
Master
系所名稱: 工學院 - 土木工程學系
Department of Civil Engineering
論文出版年: 2020
畢業學年度: 108
語文別: 英文
論文頁數: 58
中文關鍵詞: 遇险识别垂直加速度快速傅立叶变换连续小波变换
外文關鍵詞: Distress Identification, Vertical Acceleration, Fast Fourier Transform, Continuous Wavelets Transform
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  • 路面遇险调查的原因如此重要,以支持维护和修复活动中的决策。对于手动勘测,其优点是训练有素的勘测员可以提供高质量和准确的结果,以识别道路上的各种遇险类型和样式。过去的研究没有专门分析遇险的类型和遇险的位置。 ,使用安装在小型摩托车上的单向加速度计收集特定路面遇险类型的垂直加速度数据。这项研究是初步确定使用信号处理方法(如快速傅立叶变换(FFT)和小波变换(WT))来分析遇难特性的方法,这些方法通常用于分析垂直加速度数据。为了从FFT和WT参数之间看待重要性,采用了ANOVA和T检验。从统计上看,所有这四个参数都能够区分路面,而不会造成困扰,疲劳裂纹和坑洼。区分不同遇险类型和严重性级别的顺序是ESD> DF> WE> WC。第一个ESD10具有25对显着性水平的显着性差异,第二个DF3具有23对显着性的显着性水平,WE10具有22对显着性的显着性水平,而WC5具有17对显着性的显着性差异。

    The reason of the pavement distress survey is so important, to support decision making during maintenance and rehabilitation activities. For the manual survey, the advantage is that a well-trained surveyor can provide high quality and accurate results to identify various distresses type and pattern on the road. The past studies did not specifically analyze the type of distress and the location of the distress. , the vertical acceleration data over specific pavement distress types were collected using the one-directional accelerometer installed on an electrical scooter. This research is preliminary to identify a characteristic of distress using signal processing methods such as Fast Fourier transform (FFT) and Wavelet transform (WT) are commonly used to analyse the vertical acceleration data. ANOVA and T-test have employed in order to see the significance among the parameter of FFT and WT Statistically, all four parameters are able to distinguish pavement with no distress, fatigue cracking and pothole. the order to differentiate different distress types and severity levels are ESD > DF > WE > WC. First ESD10s have 25 pairs severity level show the significant difference, second DF3s are 23 pairs significant, WE10s are 22 pairs significant severity level and WC5s has17 pairs show a significant difference.

    摘要 I ABSTRACT I ACKNOWLEDGEMENTS III TABLE OF CONTENTS IV LIST OF TABLES VI LIST OF FIGURES VII 1. CHAPTER ONE INTRODUCTION 1 1.1. Background 1 1.2. Research Objective 2 1.3. Thesis Organization. 3 2. CHAPTER TWO LITERATURE REVIEW 4 2.1. Fault Identification Using Vibration Analysis in Mechanical Engineering 4 2.2. Distress Identification Using Vibration Analysis in Civil Engineering 5 2.3. Signal Analysis Technique 8 2.3.1. Fast Fourier Transform 8 2.3.2. Wavelet Transform 9 3. CHAPTER THREE DATA COLLECTION AND analysis 11 3.1. Field Data Collection 12 3.1.1. Data Collection Tool 12 3.1.2. Pavement Distress Type and Severity 13 3.1.3. Raw Data from the Field 15 3.2. Data Transformation and Parameter analysis 18 3.2.1. Fast Fourier Transform (FFT) 18 3.2.2. Continues Wavelet Transforms (WT) 22 3.3. Statistical Analysis 25 4. CHAPTER FOUR RESULT AND DISCUSSION 26 4.1. Fast Fourier Transform 26 4.1.1. Dominant Frequency 26 4.1.2. Energy Spectral Density 28 4.2. Perform Continues Wavelet Transform 30 4.2.1. Wavelet Coefficient 30 4.2.2. Wavelet energy 32 4.3. Statistical Analysis 33 5. CHAPTER FIVE CONCLUSIONS AND RECOMMENDATION 44 5.1. Conclusions 44 5.2. Recommendation 45 6. Reference 46 7. Appendix A 49 A.1 Fast Fourier Transform Analysis (FFT) 49 A.1.1 Dominant Frequency of No Distress, Fatigue and Pothole 49 A.1.2 Energy Spectral Density of No Distress, Fatigue and Pothole 51 A.2 Wavelet Transform Analysis (WT) 54 A.2.1 Wavelet Coefficiet of No Distress, Fatigue and Pothole 54 A.2.2 Wavelet Energy of No Distress, Fatigue and Pothole 56

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