應用智慧標籤及數據驅動方法於水接觸結構物之結構評估

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普艾倫(Alan Putranto) 查詢紙本館藏

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論文名稱

應用智慧標籤及數據驅動方法於水接觸結構物之結構評估
(Structural Evaluation for Water-Contacted Structures with Smart Tags and Data-Driven Approach)

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摘要(中)

這項研究提出了一種新的數據驅動非破壞性檢測技術(NDT),專門用於評估與水接觸的結構健康監測(SHM)系統。該方法將射頻辨識(RFID)感測器數據與深度學習模型相結合。實驗驗證包含在混凝土和鋼筋混凝土梁(RCB)試體上測試負載-開裂-水滲入機制,以及模擬鋼箱梁的模擬滲漏。我們的研究還探討了奈米材料的整合,以增強智慧感測標籤的靈敏度。結果表明, 智慧感測標籤可以通過sensor code 的總損壞指數(TDI)檢測圓柱形混凝土試樣中的滲漏和裂縫。此外,對鋼筋混凝土梁(RCBs)的測試證實,帶有3D 列印保護外殼的智慧感測標籤可用嵌入式應用中,通過sensor code 指數檢測滲漏和裂縫。通過改性等離子體輔助電化學剝離石墨(MPGE)奈米塗層改善智慧感測標籤,在奈米片合成過程中不使用額外的架橋劑(CA)時,感應靈敏度提高了約53%,使用額外CA 時提高了約121%。這些結果突出了智慧感測標籤在檢測水的有效性,並確認了試體中存在裂縫。結果還表明,帶有3D 打印保護外殼的智慧標籤可以通過sensor code 和頻率的關係檢測鋼箱梁內的滲漏。此外,我們利用電磁波影像數據集,並使用傳統和預訓練的卷積神經網絡(CNNs)進行分析。研究結果顯示準確率達96%,F1 分數達92%。這項研究通過為水接觸結構的結構評估提供一個強大的框架,為SHM 的發展做出了貢獻。

摘要(英)

This research presents a novel data-driven non-destructive technique (NDT) designed specifically for assessing structures in contact with water, aimed at enhancing structural health monitoring (SHM) systems. The methodology integrates empirical data of radio-frequency identification (RFID)-based smart sensor system with deep learning models. The experimental validation involved testing the loading-cracking-immersing mechanism on concrete and reinforced concrete beam (RCB) specimens, as well as simulating constant water flow through a steel box girder to mimic leakage. Our study also explores the integration of nanosheet materials to augment smart tag sensitivity.
Results indicate that smart sensor tags can detect seepage and cracks in cylindrical concrete specimens through the total damage index (TDI) of the sensor code. Additionally, tests on reinforced concrete beams (RCBs) verified that smart tags with 3D-printed protective cases can be used to detect seepage and cracks through the sensor code index in embedding applications. Modifications to the smart tags with a modified plasma-assisted electrochemical exfoliated graphite (MPGE) nanosheet coating layer improved sensing sensitivity by approximately 53% without an additional crosslinking agent (CA) in the nanosheet synthesis process, and by approximately 121% with the inclusion of an additional CA. These results highlight the efficacy of smart tags in detecting seepage as water penetrates through cracks due to the loading-cracking-immersing mechanism, and confirming the presence of cracks in the specimens. The results also show that smart tags with 3D-printed protective cases can detect leakage within the steel box girder through the relation of sensor code and frequency. Moreover,we utilized the electromagnetic-wave (EM-wave) images dataset and analyzed them using classical and pre-trained convolutional neural networks (CNNs). The findings demonstrate promising results, with an accuracy of 96% and an F1-score of 92% in predicting the condition of RCBs under water-contact scenarios. This study contributes to the advancement of SHM by providing a robust framework for the structural assessment of water-contacted structures.

關鍵字(中)

★ 非破壞檢測
★ RFID 智慧感測器系統
★ 智慧感測標籤
★ 奈米材料
★ 電磁波
★ 卷積神經網絡
★ 結構健康監測系統

關鍵字(英)

★ Non-destructive technique
★ RFID-smart sensor system
★ Smart tags
★ Nanosheet material
★ Electromagnetic-wave
★ Convolutional neural network
★ SHM system

論文目次

摘要 i
ABSTRACT ii
Acknowledgments iii
Table of Contents iv
List of Figures vii
List of Tables xi
Explanation of Symbols xii
1. Chapter I Introduction 1
1-1 Background and Motivations 1
1-2 Objectives 3
1-3 Dissertation Outlines 3
Chapter II Literature Review 5
2-1 Structural Health Monitoring 5
2-2 Development of Radio Frequency Identification Technology for Non-Destructive Evaluation 7
2-3 Nanosheet Technology in Engineering Application 11
2-4 Data-Driven Model-Based Approach 14
2-5 Summary 15
Chapter III Research Methodology 16
3-1 Overview 16
3-2 Proposed Smart Tags and Data-Driven Damage detection Approach for Water-Contacted Structures 16
3-3 Principles of Smart Sensor Tags based on Sensor Code Integrated Circuits 19
3-4 Experimental Design and Test Procedures 23
3-5 Analysis 42
Chapter IV Experimental Validation 44
4-1 Seepage Detection in Concrete Specimen 44
4-2 Improving the RFID-Smart Sensor System for Seepage Detection in Concrete Specimen 48
4-3 Seepage Detection in Reinforced Concrete Structures 51
4-4 Leakage Detection on Steel Structures 64
4-5 Summary 67
Chapter V Data-Driven Approach for Evaluating Water-Contacted Reinforced Concrete Structures 69
5-1 Overview 69
5-2 Structural Evaluation via Electromagnetic wave Spectrum Analysis and Hybrid Machine Learning-Deep Learning Techniques 69
5-3 Radio Frequency Identification System 71
5-4 Building a Dataset through Optimized Parameter Selection with Machine Learning Classifiers and Image Processing Techniques 74
5-5 Deep Learning Analytical Framework 90
5-6 Result of Data-driven Model Prediction for RC Structure Evaluation 96
5-7 Improving Model Prediction with Hybrids Resampling Methods 101
5-8 Summary 112
Chapter VI Conclusions and Future Work 114
6-1 Conclusions 114
6-2 Future Work 116
References 117
Appendix 133
List of Publications 134

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指導教授

林子軒(Tzu-Hsuan Lin)

審核日期

2024-7-17

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