Computer Vision-Based Post-Earthquake Inspections on Building Safety Assessment

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研究生：	Alvin Kwek Alvin Kwek
論文名稱：	Computer Vision-Based Post-Earthquake Inspections on Building Safety Assessment Computer Vision-Based Post-Earthquake Inspections on Building Safety Assessment
指導教授：	鄭明淵 Min-Yuan Cheng
口試委員:	楊亦東 I-Tung Yang 吳育偉 Yu-Wei Wu
學位類別：	碩士 Master
系所名稱：	工程學院 - 營建工程系 Department of Civil and Construction Engineering
論文出版年：	2022
畢業學年度：	110
語文別：	英文
論文頁數：	78
外文關鍵詞：	SHM, ESRGAN, DeblurGANv2
相關次數：	點閱：135 下載：0
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Safety assessment in structural health monitoring is one of the important factors for buildings. Structural health monitoring for building inspection is a necessity to provide faster danger responses and reduce threats. However, building infrastructures were exposed to natural disaster and lifetime. For the safety assessment during disaster, the image that were taken by the expert urgently from the site does not always produce a good quality image. Therefore, the Hybrid-GAN which comprise of ESRGAN and DeblurGANv2 were used as the image repairing method that generating better images. Meanwhile, Deep Learning techniques of Convolutional Neural Network (CNN) usage has significantly increase in the modern days especially computer vision-based approach for safety assessment in semantic segmentation task. Moreover, this research proposed Transfer Learning U-Net (TF-Unet) Algorithm to detect and classify building components which are column and structural wall alongside with the damage level according to the Taiwan codes for building evaluation assessment. Furthermore, the pre-train model were used to predict three case studies to test the capability of TF-Unet in handling real word dataset.

ABSTRACT ............................................................................................................................... i
ACKNOWLEDGEMENT ....................................................................................................... ii
TABLE OF CONTENTS ....................................................................................................... iv
LIST OF FIGURES ............................................................................................................... vii
LIST OF TABLES ................................................................................................................... x
CHAPTER 1: INTRODUCTION ...................................................................................... 1
1 Background ................................................................................................................. 1
2 Research Objective ...................................................................................................... 3
3 Research Scope and Assumptions ............................................................................... 3
4 Research Methodology ................................................................................................ 4
5 Research Outline ......................................................................................................... 5
CHAPTER 2: LITERATURE REVIEW .......................................................................... 6
1 Generative Adversarial Networks (GAN) ................................................................... 6
1.1 Image Resolution ................................................................................................. 7
1.2 Enhanced Super Resolution GAN (ESRGAN) .................................................... 8
1.3 Image Blurriness .................................................................................................. 9
1.4 DeblurGANv2 .................................................................................................... 11
2 Computer Vision ....................................................................................................... 14
3 U-Net Algorithm ....................................................................................................... 15
4 Previous Research on Building Evaluation System .................................................. 18
5 Taiwan Emergency and Evaluation of Dangerous Buildings after Disaster (2014) . 19
CHAPTER 3: METHODOLOGY ................................................................................... 23
1 Data Collection .......................................................................................................... 25
v
1.1 DEEDS Dataset .................................................................................................. 26
1.2 PEER Hub ImageNet (Φ-Net) Dataset .............................................................. 27
1.3 Purdue University Research Repository Dataset ............................................... 27
2 Data Preprocessing .................................................................................................... 28
2.1 Hybrid GAN Model ........................................................................................... 28
2.1.1 Super Resolution Phase .............................................................................. 28
2.1.2 Blur Detection Phase .................................................................................. 28
2.2 Image Labelling Annotation .............................................................................. 29
2.3 Data Splitting ..................................................................................................... 31
3 TF-Unet Initialization ................................................................................................ 31
3.1 Hyperparameter Configuration .......................................................................... 31
3.2 TF-Unet Model Training and Validation ........................................................... 33
3.3 TF-Unet Performance Evaluation ...................................................................... 34
4 Evaluation on Case Study ......................................................................................... 36
4.1 Inputting Structure Member for Safety Damage Assessment ............................ 36
4.2 Structural Damage Index Calculation Phase ...................................................... 36
CHAPTER 4: MODEL EVALUATION AND IMPLEMENTATION ........................ 41
1 Dataset Introduction .................................................................................................. 41
2 Hybrid GAN .............................................................................................................. 42
2.1 ESRGAN Image Enhancement .......................................................................... 42
2.2 DeblurGANv2 Image Enhancement .................................................................. 43
3 Training Validation & Testing Results ..................................................................... 43
3.1 Column Damage Recognition ............................................................................ 43
3.2 Structural Wall Damage Recognition ................................................................ 47
3.3 Case 1 Yonkang Elementary School.................................................................. 50
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3.4 Case 2 Farmers Supermarket Yujing ................................................................. 53
3.5 Case 3 Jun-gong Elementary School ................................................................. 56
CHAPTER 5: CONCLUSION AND RECOMMENDATION ...................................... 59
1 Conclusion ................................................................................................................. 59
2 Recommendation ....................................................................................................... 60
REFERENCES ....................................................................................................................... 61
                                

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全文公開日期 2025/01/24 (校內網路)
全文公開日期 2027/01/24 (校外網路)
全文公開日期 2027/01/24 (國家圖書館：臺灣博碩士論文系統)

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