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

參考文獻

[1] F. Qu, W. Li, W. Dong, V. W. Y. Tam, and T. Yu, “Durability deterioration of concrete under marine environment from material to structure: A critical review.” J. Build. Eng., Vol. 35, March 2021, pp.102074.
[2] H. P. Chen, Structural health monitoring of large civil engineering structures. John Wiley & Sons, 2018.
[3] P. K. Mehta and P. J. M. Monteiro, Concrete: microstructure, properties, and materials. McGraw-Hill Education, 2014.
[4] J. Sun and Z. Zhang, “A post-disaster resource allocation framework for improving resilience of interdependent infrastructure networks.” Transp. Res. D: Transp. Environ., Vol. 85, August 2020, pp. 102455.
[5] Y. P. Fang and G. Sansavini, “Optimum post-disruption restoration under uncertainty for enhancing critical infrastructure resilience.” Reliab. Eng. Syst. Saf., Vol. 185, May 2019, pp.1-11.
[6] S. Hassani and U. Dackermann, “A Systematic Review of Advanced Sensor Technologies for Non-Destructive Testing and Structural Health Monitoring.” Sensors, Vol. 23, February 2023, pp. 2204.
[7] P. Pecho, M. Hrúz, A. Novák, and L. Trško, “Internal damage detection of composite structures using passive RFID tag antenna deformation method: Basic Research.” Sensors, Vol. 21, December 2021, pp. 8236.
[8] R. E. Martínez-Castro, S. Jang, J. Nicholas, and R. Bansal, “Experimental assessment of an RFID-based crack sensor for steel structures.” Smart Mater. Struct., Vol. 26, July 2017, pp. 085035.
[9] B. Bruciati, S. Jang, and P. Fils, “RFID-based crack detection of ultra high-performance concrete retrofitted beams.” Sensors, Vol. 19, April 2019, pp. 1573.
[10] R. E. Martínez-Castro and S. Jang, “Crack sensor using commercial UHF RFID technology for metallic structures.” Struct. Cong., April 2019, pp. 68-77.
[11] J. Jia and Y. Li, “Deep Learning for Structural Health Monitoring: Data, Algorithms, Applications, Challenges, and Trends.” Sensors, Vol. 23, October 2023, pp. 8824.
[12] M. Azimi, A. D. Eslamlou, and G. Pekcan, “Data-driven structural health monitoring and damage detection through deep learning: State-of-the-art review.” Sensors, Vol. 20, May 2020, pp. 2778.
[13] A. Malekloo, E. Ozer, M. AlHamaydeh, M. Girolami, “Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights.” Struct. Health Monit., Vol. 21, August 2021, pp. 1906-1955.
[14] H. Sohn, C. R. Farrar, F. M. Hemez, D. D. Shunk, D. W. Stinemates, B. R. Nadler, and J. J. Czarnecki, “A review of structural health monitoring literature: 1996–2001.” Los Alamos National Laboratory, USA 1 (2003): 16.
[15] J. M. W. Brownjohn, “Structural health monitoring of civil infrastructure,” Philos. Trans. R. Soc. A-Math. Phys. Eng. Sci., Vol. 365, February 2007, pp. 589-622.
[16] H. N. Li, L. Ren, Z. G. Jia, T. H. Yi, and D. S. Li, “State-of-the-art in structural health monitoring of large and complex civil infrastructures.” J. Civ. Struct. Health Monit., Vol. 6, February 2016, pp. 3-16.
[17] Q. Li, Y. He, K. Zhou, X. Han, Y. He, and Z. Shu, “Structural health monitoring for a 600 m high skyscraper.” The Struct. Design of Tall and Special Build., Vol. 27, August 2018, pp. e1490.
[18] C. Gragnaniello, G. Mariniello, T. Pastore, and D. Asprone, “BIM-Based Design and Setup of Structural Health Monitoring Systems.” Autom. Constr., Vol. 158, February 2024, pp. 105245.
[19] S.U. Rehman, M. Usman, M.H.Y. Toor, and Q.A. Hussaini, “Advancing Structural Health Monitoring: A Vibration-Based IoT Approach for Remote Real-Time Systems.” Sens. Actuator A-Phys., Vol. 365, January 2024, pp. 114863.
[20] R.P. Longman, Y. Xu, Q. Sun, Y. Turkan, and M. Riggio, “Digital Twin for Monitoring In-Service Performance of Post-Tensioned Self-Centering Cross-Laminated Timber Shear Walls.” J. Comput. Civil. Eng., Vol. 37, March 2023, pp. 04022055.
[21] F.I. Sakiyama, G.S. Veríssimo, F. Lehmann, and H. Garrecht, “Quantifying the Extent of Local Damage of a 60-Year-Old Prestressed Concrete Bridge: A Hybrid SHM Approach.” Struct. Health Monit., Vol. 22, January 2023, pp. 496-517.
[22] T. Bergmayr, S. Höll, C. Kralovec, and M. Schagerl, “Local Residual Random Forest Classifier for Strain-Based Damage Detection and Localization in Aerospace Sandwich Structures,” Compos. Struct., Vol. 304, January 2023, pp. 116331.
[23] T. Wu, L. Tang, F. Zhou, Y. Zhang, and Z. Zhou, “Damage Detection Based on Accelerometers and Computer Vision Measurements of Moving Load-Induced Structural Responses.” Mech. Syst. Signal Proc., Vol. 211, April 2024, pp. 111246.
[24] M. Crognale, C. Rinaldi, F. Potenza, V. Gattulli, A. Colarieti, and F. Franchi, “Developing and Testing High-Performance SHM Sensors Mounting Low-Noise MEMS Accelerometers.” Sensors, Vol. 24, April 2024, pp. 2435.
[25] N. Liu, T. Schumacher, Y. Li, L. Xu, and B. Wang, “Damage Detection in Reinforced Concrete Member Using Local Time-Frequency Transform Applied to Vibration Measurements.” Buildings, Vol. 13, January 2023, pp. 148.
[26] W.A. Altabey, Z. Wu, M. Noori, and H. Fathnejat, “Structural Health Monitoring of Composite Pipelines Utilizing Fiber Optic Sensors and an AI-Based Algorithm—A Comprehensive Numerical Study.” Sensors, Vol. 23, April 2023, pp. 3887.
[27] F. Falcetelli, D. Cristiani, N. Yue, C. Sbarufatti, E. Troiani, R. Di Sante, and D. Zarouchas, “Qualification of Distributed Optical Fiber Sensors Using Probability of Detection Curves for Delamination in Composite Laminates.” Struct. Health Monit., Vol. 22, September 2023, pp. 2972-2986.
[28] B. Richter, M. Herbers, and S. Marx, “Crack Monitoring on Concrete Structures with Distributed Fiber Optic Sensors—Toward Automated Data Evaluation and Assessment.” Struct. Concr., Vol. 25, April 2024, pp. 1465-1480.
[29] I. Fernandez, C.G. Berrocal, S. Almfeldt, and R. Rempling, “Monitoring of New and Existing Stainless-Steel Reinforced Concrete Structures by Clad Distributed Optical Fibre Sensing.” Struct. Health Monit, Vol. 22, January 2023, pp. 257-275.
[30] W. J. Staszewski and H. Sohn, “Signal processing for structural health monitoring.” Encyclop. of Aerospace Eng., December 2010.
[31] D. Balageas, C. P Fritzen, and A. Güemes, Structural health monitoring. John Wiley & Sons, 2010.
[32] F. Ansari, Sensing issues in civil structural health monitoring. Springer, 2005.
[33] N. Shlezinger, J. Whang, Y. C. Eldar, and A. G. Dimakis, “Model-based deep learning.” Proc. of the IEEE, Vol. 111, May 2023, pp. 465-499.
[34] V. D. Hunt, A. Puglia, and M. Puglia, RFID: a guide to radio frequency identification. John Wiley & Sons, 2017.
[35] Z. N. Chen, Antennas for portable devices. John Wiley & Sons, 2007.
[36] M. N. Ochi “Investigating Present and Future Interactions Between Radio Frequency Identification, Additive Manufacturing and Supply Chain Management.” Master Thesis, Master of Science in Industrial Engineering-California Polytechnic State University, May 2014. (Available at: https://digitalcommons.calpoly.edu/theses/1231)
[37] T. Blecker, RFID in operations and supply chain management: research and applications. Erich Schmidt Verlag GmbH & Co KG, 2008.
[38] A. Khattab et al., Introduction to RFID. RFID Security: A Lightweight Paradigm. Springer International Publishing AG, 2017.
[39] B. S. Cook, R. Vyas, S. Kim, T. Thai, T. Le, A. Traille, H. Aubert, and M. M. Tentzeris, “RFID-based sensors for zero-power autonomous wireless sensor networks.” IEEE Sens. J., Vol. 14, August 2014, pp.2419-2431.
[40] H. Nesser, H. A. Mahmoud, and G. Lubineau, “High‐Sensitivity RFID Sensor for Structural Health Monitoring.” Adv. Sci., Vol. 10, September 2023, pp. 2301807.
[41] C. Walther, F. Alkam, L. Nguyen‐Tuan, M. Lieboldt, and T. Lahmer, “Challenges for Embedding RFID Sensors in Reinforced Concrete for Low‐effort Structural Health Monitoring.” Proc. in Civ. Eng., Vol.6, December 2023, pp. 1460-1469.
[42] P. Fils, S. Jang, and R. Sherpa, “Field Implementation of Low-Cost RFID-Based Crack Monitoring Using Machine Learning.” Struct. Monit. Maint, Vol. 8, September 2021, pp. 257-278.
[43] W.S. Kuan, Z.Y. Pang, K.W. Tam, C.H. Chio, S.W. Ting, and I.P. Leong, “Design of UHF RFID Tag Antenna Applied in Railway Axle Counting.” in IEEE 13th International Conference on RFID Technology and App., September 2023, pp. 21-24.
[44] N. Javed, M.A. Azam, and Y. Amin, “Chipless RFID Multisensor for Temperature Sensing and Crack Monitoring in an IoT Environment.” IEEE Sens. Lett., Vol. 5, May 2021, pp. 1-4.
[45] P. Wang, L. Dong, H. Wang, G. Li, Y. Di, X. Xie, and D. Huang, “Passive Wireless Dual-Tag UHF RFID Sensor System for Surface Crack Monitoring.” Sensors, Vol. 21, January 2021, pp. 882.
[46] J. Zhang, C. Li, Y. Gao, J. Tan, F. Xuan, and X. Ling, “Flexible Multimode Antenna Sensor with Strain and Humidity Sensing Capability for Structural Health Monitoring.” Sens. Actuator A-Phys., Vol. 347, November 2022, pp. 113960.
[47] S. Suresh, M.R. Prasath, and G. Chakaravarthi, “In-situ Surface Crack Detection on Metal Using a Passive Wireless RFID-Based NDT Sensor.” in IEEE Microwaves, Antennas, and Propagation Conf., December 2023, pp. 1-5.
[48] D. Gunjic, J. Walk, M. Fischer, and T. Ussmueller, “Realization of a Passive UHF RFID Sensor Platform for the Detection of Damages on a Concrete Reinforcement.” in 52nd European Microwave Conf., September 2022, pp. 584-587.
[49] K. Mc Gee, P. Anandarajah, and D. Collins, “Use of Chipless RFID as a Passive, Printable Sensor Technology for Aerospace Strain and Temperature Monitoring,” Sensors, Vol. 22, November 2022, pp. 8681.
[50] S. Xue, H. Wang, L. Xie, X. Li, Z. Zheng, and G. Wan, “Bolt Loosening Detection Method Based on Double-Layer Slotted Circular Patch Antenna.” Struct. Health Monit., February 2024, pp. 14759217241227992.
[51] T. You, W. Wang, Y. Zhang, and C. Fang, “Multi-Level Breakage-Triggered Radio Frequency Identification-Based Deformation Sensor for Rapid Post-Earthquake Loss Assessment of Buildings: Concept, Development, and Application.” Struct. Control. Health Monit., Vol. 29, July 2022, pp. e2947.
[52] T. You, W. Wang, C. Fang, and Y. Chen, “Rapid Probabilistic Loss Assessment of Buildings Based on Post-Earthquake Structural Deformation Conditions.” J. Build. Eng., Vol. 45, January 2022, pp. 103629.
[53] A. Charléty, M. Le Breton, L. Baillet, and E. Larose, “RFID Landslide Monitoring: Long-Term Outdoor Signal Processing and Phase Unwrapping.” IEEE J. Radio Freq. Identif., Vol. 7, March 2023, pp. 319-329.
[54] M. Stanaćević, A. Ahmad, X. Sha, A. Athalye, S. Das, K. Caylor, B. Glisić, and P.M. Djurić, “RF Backscatter-Based Sensors for Structural Health Monitoring.” in 2021 International Balkan Conference on Communications and Networking (BalkanCom), IEEE, September 2021, pp. 71-74.
[55] A. Gregori, C. Castoro, A. Di Natale, M. Mercuri, and E. Di Giampaolo, “Using Commercial UHF-RFID Wireless Tags to Detect Structural Damage.” Procedia Struct. Integr., Vol. 44, January 2023, pp. 1586-1593.
[56] A. DiNatale, A. DiCarlofelice, and E. DiGiampaolo, “A Crack Mouth Opening Displacement Gauge Made with Passive UHF RFID Technology.” IEEE Sens. J., Vol. 22, November 2021, pp. 174-181.
[57] P. Wang, L. Dong, H. Wang, G. Li, and X. Xie, “Passive Ultra High Frequency RFID Sensor with Reference Tag for Crack Detection of Aluminum Alloy Structure.” J. Instrum., Vol. 16, November 2021, pp. P11018.
[58] M. Pour-Ghaz, T. Barrett, T. Ley, N. Materer, A. Apblett, and J. Weiss, “Wireless crack detection in concrete elements using conductive surface sensors and radio frequency identification technology.” J. Mater. Civ. Eng., Vol. 26, July 2013, pp.923-929.
[59] S. Caizzone and E. DiGiampaolo, “Wireless passive RFID crack width sensor for structural health monitoring.” IEEE Sens. J., Vol. 15, December 2015, pp. 6767-6774.
[60] J. Zhang, G. Y. Tian, and A. B. Zhao, “Passive RFID sensor systems for crack detection & characterization.” NDT E Int., Vol. 86, March 2017, pp. 89-99.
[61] M. Norouzi, N. Masoumi, and H. Jahed, “Nondestructive Phase Variation-Based Chipless Sensing Methodology for Metal Crack Monitoring.” IEEE Trans. Instrum. Meas., Vol. 70, March 2021, pp. 1-11.
[62] K. McGee, “Development of a Chipless RFID-Based Aerospace Structural Health Monitoring Sensor System.” Doctoral Dissertation, Dublin City University, March 2023. (Available at: https://doras.dcu.ie/28152/)
[63] X. Li, S. Xue, L. Xie, and G. Wan, “Simultaneous Crack and Temperature Sensing with Passive Patch Antenna,” Struct. Health Monit., Vol. 23, May 2024, pp. 14759217231184115.
[64] J.A.B. Durtschi, A. Chrysler, and P. Bodily, “Detecting Vibration Frequencies of Concrete Structures via RFID Tags.” in Proc. of ISCA 34th International Conf., Vol. 79, 2021, pp. 1-10.
[65] Y. Quan, X. Wang, Y. Liu, H. Sun, and F. Wang, “Real-Time Monitoring of Concrete Vibration Depth Based on RFID Scales.” Buildings, Vol. 14, March 2024, pp. 885.
[66] Y. He, M. M. Li, G. C. Wan, and M. S. Tong, “A passive and wireless sensor based on RFID antenna for detecting mechanical deformation.” IEEE Open J. Antennas Propag., Vol. 1, August 2020, pp. 426-434.
[67] T. You, W. Wang, Y. Zhang, and C. Fang, “Multi‐level breakage‐triggered radio frequency identification‐based deformation sensor for rapid post‐earthquake loss assessment of buildings: Concept, development, and application.” Struct. Control. Health Monit., Vol. 29, July 2022, pp. e2947.
[68] M. F. Kuhn, G. P. Breier, A. R. Dias, and T. G. Clarke, “A novel RFID-based strain sensor for wireless structural health monitoring.” J. Nondestruct. Eval., Vol. 37, February 2018, pp. 1-10.
[69] S. Wang and M. S. Tong, “Mechanical deformation detection of building structures using microstrip patch antennas as sensors.” IEEE Sens. J., Vol. 18, November 2018, pp. 8676-8684.
[70] S. Zhou, F. Deng, L. Yu, B. Li, X. Wu, and B. Yin, “A novel passive wireless sensor for concrete humidity monitoring.” Sensors, Vol. 16, September 2016, pp. 1535.
[71] S. Capdevila, G. Roqueta, M. Guardiola, L. Jofre, J. Romeu, and J. C. Bolomey, “Water infiltration detection in civil engineering structures using RFID.” 6th European Conf. on Antennas and Propagat., March 2012, pp. 2505-2509.
[72] C. Y. Chang and S. S. Hung, “Implementing RFIC and sensor technology to measure temperature and humidity inside concrete structures.” Constr. Build. Mater., Vol. 26, January 2012, pp. 628-637.
[73] J. H. Kang and J. Gandhi, “Readability test of RFID temperature sensor embedded in fresh concrete.” J. Civ. Eng. Manag., Vol. 16, September 2010, pp. 412-417.
[74] H. Chen, S. Zhou, F. Deng, J. Zou, X. Wu, and Z. Fu, “Novel concrete-temperature monitoring method by using embedded passive wireless sensor.” Mag. Concr. Res., Vol. 70, May 2018, pp. 452-458.
[75] G. González-López, J. Romeu, I. Cairó, I. Segura, T. Ikumi, and L. Jofre-Roca, “Wireless sensing of concrete setting process.” Sensors, Vol. 20, October 2020, pp. 5965.
[76] Y. Gu, S. Roh, W. Lin, Y. Jung, and Y.H. Cho, “Investigation of the Signal Reach Performance of the Ultra-High-Frequency Identification Tag for Underground Utility Management.” Appl. Sci., Vol. 13, February 2023, pp. 2294.
[77] T. Ikumi, I. Cairó, J. Groeneveld, A. Aguado, and A. de la Fuente, “Embedded Wireless Sensor for In Situ Concrete Internal Relative Humidity Monitoring.” Sensors, Vol. 24, March 2024, pp. 1756.
[78] W. D. Leon-Salas and C. Halmen, “A RFID sensor for corrosion monitoring in concrete.” IEEE Sens. J., Vol. 16, January 2016, pp. 32-42.
[79] K. Bouzaffour, B. Lescop, P. Talbot, F. Gallée, and S. Rioual, “Development of an embedded UHF-RFID corrosion sensor for monitoring corrosion of steel in concrete.” IEEE Sens. J., Vol. 21, March 2021, pp. 12306-12312.
[80] M. Bartholmai, S. Johann, and C. Strangfeld, “Embedded wireless sensor systems for long-term SHM and corrosion detection in concrete components.” 8th Int. Conf. on Struct. Health Monit. of Intelligent Infrastruct., December 2017, pp. 1-7.
[81] H. Zhang, R. Yang, Y. He, G. Y. Tian, L. Xu, and R. Wu, “Identification and characterisation of steel corrosion using passive high frequency RFID sensors.” Measurement, Vol. 92, October 2016, pp. 421-427.
[82] J. Wu, M. Zhang, Z. Liu, and J. Zhang, “Magnetic Permeability Perturbation-Based RFID Sensor for Pipeline Internal Corrosion Monitoring.” IEEE Trans. Ind. Electron., Vol. 71, March 2023, pp. 1987-1996.
[83] K. Bouzaffour, B. Lescop, P. Talbot, and S. Rioual, “Monitoring Corrosion of Prestressed Concrete Bridges by Sensors Based on Sensitive Antennas.” in IEEE Conference on Antenna Measurements and App., November 2023, pp. 71-73.
[84] H. Nesser, H.A. Mahmoud, and G. Lubineau, “High-Sensitivity RFID Sensor for Structural Health Monitoring.” Adv. Sci., Vol. 10, September 2023, pp. 2301807.
[85] A. Gregori, C. Castoro, M. Mercuri, A. Di Natale, and E. Di Giampaolo, “Innovative Use of UHF-RFID Wireless Sensors for Monitoring Cultural Heritage Structures.” Buildings, Vol. 14, April 2024, pp. 1155.
[86] J.R. Tenório Filho, J. Goethals, R. Aminzadeh, Y. Abbas, D.E. Valdez Madrid, V. Cnudde, G. Vermeeren, D. Plets, and S. Matthys, “An Automated Wireless System for Monitoring Concrete Structures Based on Embedded Electrical Resistivity Sensors: Data Transmission and Effects on Concrete Properties.” Sensors, Vol. 23, October 2023, pp. 8775.
[87] C. Strangfeld, M. Stoppel, M. Bartholmai, S. Petrov, and A. Fakhouri, “Embedded RFID-sensors for concrete bridge structures.” Int. Symp. NDT-CE., September 2015.
[88] C. Strangfeld, S. Johann, and M. Bartholmai, “Smart RFID sensors embedded in building structures for early damage detection and long-term monitoring.” Sensors, Vol. 19, December 2019, pp. 5514.
[89] S. H. Jeong and H. W. Son, “UHF RFID tag antenna for embedded use in a concrete floor.” IEEE Antennas Wirel. Propag. Lett., Vol. 10, October 2011, pp. 1158-1161.
[90] R.T. Jacob, R. Salama, and R. Liyanapathirana, “Performance of a Dielectric Resonator Antenna for Structural Health Monitoring Applications.” in 28th Asia Pacific Conference on Comm., November 2023, pp. 207-212.
[91] S. Johann, F. Baensch, P. Sturm, C. Tiebe, S. Pötschke, and V. Lay, “HF RFID-Based Measurement Comparison for Method Optimization in M2 Concrete and Alkali-Activated Mortars.” Mater. Today: Proceedings, Vol. 62, June 2022, pp. 2571-2576.
[92] J. Bas, T. Dutta, I. Llamas Garro, J.S. Velázquez-González, R. Dubey, and S.K. Mishra, “Embedded Sensors with 3D Printing Technology.” Sensors, Vol. 24, March 2024, pp. 1955.
[93] C. Walther, F. Alkam, L. Nguyen-Tuan, M. Lieboldt, and T. Lahmer, “Challenges for Embedding RFID Sensors in Reinforced Concrete for Low-Effort Structural Health Monitoring.” ce/papers, Vol. 6, December 2023, pp. 1460-1469.
[94] M. Pekgor, R. Arablouei, M. Nikzad, and S. Masood, “Displacement Estimation Using 3D-Printed RFID Arrays for Structural Health Monitoring.” Sensors, Vol. 22, November 2022, pp. 8811.
[95] Y. Yu, J. Li, J. Li, Y. Xia, Z. Ding, and B. Samali, “Automated Damage Diagnosis of Concrete Jack Arch Beam Using Optimized Deep Stacked Autoencoders and Multi-Sensor Fusion.” Dev. Built Environ., Vol. 14, April 2023, pp. 100128.
[96] G. Wan, Z. Jiang, and L. Xie, “A Multi-parameter Integration Method and Characterization Study of Chipless RFID Sensors with Spiral Shape.” IEEE Trans. Instrum. Meas., Vol. 72, August 2023, pp. 1-11.
[97] G. Wan, M. Li, M. Zhang, L. Kang, and L. Xie, “A Novel Information Fusion Method of RFID Strain Sensor Based on Microstrip Notch Circuit.” IEEE Trans. Instrum. Meas., Vol. 71, March 2022, pp. 1-10.
[98] L. Chen, L. Kang, L. Liu, J. Hu, G. Wan, and L. Xie, “An Encoded Reconfigurable RFID Strain Sensor and Its Information Fusion Method.” Smart Mater. Struct., Vol. 31, September 2022, pp. 105011.
[99] D. Mair, M. Fischer, J. Konzilia, M. Renzler, and T. Ussmueller, “Evolutionary Optimization of Antennas for Structural Health Monitoring.” IEEE Access, Vol. 11, January 2023, pp. 4905-4913.
[100] M. Pekgor, R. Arablouei, M. Nikzad, and S. Masood, “Displacement Estimation via 3D-Printed RFID Sensors for Structural Health Monitoring: Leveraging Machine Learning and Photoluminescence to Overcome Data Gaps.” Sensors, Vol. 24, February 2024, pp. 1233.
[101] C. Song and Z. Wu, “Artificial Intelligence-Assisted RFID Tag-Integrated Multi-Sensor for Quality Assessment and Sensing.” Sensors, Vol. 24, March 2024, pp. 1813.
[102] T.H. Pranto, M.N. Neloy, A.A. Noman, S. Wasif, M.A. Wahab, and R.M. Rahman, “Utilizing Deep Learning in Chipless RFID Tag Detection: An Investigation on High-Precision mm-Wave Spatial Tag Estimation from 2D Virtual Imaging.” J. Inform. Telecommun., January 2024, pp. 1-23.
[103] S. Suresh and G. Chakaravarthi, “RFID Technology and Its Diverse Applications: A Brief Exposition with a Proposed Machine Learning Approach.” Measurement, Vol. 195, May 2022, pp. 111197.
[104] B. Qiu, M. Xing, and J. Zhang, “Recent advances in three-dimensional graphene based materials for catalysis applications.” Chem. Soc. Rev., Vol. 47, February 2018, pp. 2165-2216.
[105] S. Guo and S. Dong, “Graphene nanosheet: synthesis, molecular engineering, thin film, hybrids, and energy and analytical applications.” Chem. Soc. Rev, Vol. 40, January 2011, pp. 2644-2672.
[106] X. Yu, W. Zhang, P. Zhang, and Z. Su, “Fabrication technologies and sensing applications of graphene-based composite films: advances and challenges.” Biosens. Bioelectron., Vol. 89, March 2017, pp. 72-84.
[107] X. Kang, S. Yip, Y. Meng, W. Wang, D. Li, C. Liu, and J. C. Ho, “High-performance electrically transduced hazardous gas sensors based on low-dimensional nanomaterials.” Nanoscale Adv., Vol. 3, September 2021, pp. 6254-6270.
[108] G. Ji, J. Tian, F. Xing, and Y. Feng, “Optical biosensor based on graphene and its derivatives for detecting biomolecules.” Int. J. Mol. Sci., Vol. 23, September 2022, pp. 10838.
[109] T. M. Huang, C. K. Lin, R. J. Wu, Y. T. Liu, W. Y. Hsieh, and J. H. Chang, “Development of segregated 3D graphene networks in rubber nanocomposites with enhanced electrical and mechanical properties.” J. Polym. Res., Vol. 26, April 2019, pp.1-10.
[110] S. Ručevskis, T. Rogala, and A. Katunin, “Monitoring of Damage in Composite Structures Using an Optimized Sensor Network: A Data-Driven Experimental Approach.” Sensors, Vol. 23, February 2023, pp. 2290.
[111] M. Pekgor, R. Arablouei, M. Nikzad, and S.H. Masood, “Non-destructive Testing of FRPs via 3D-printed RFID Tag Arrays and Deep Learning.” IEEE Sens. J., Vol. 23, September 2023, pp. 25534 – 25546.
[112] Y. Reuland, P. Martakis, and E. Chatzi, “A Comparative Study of Damage-Sensitive Features for Rapid Data-Driven Seismic Structural Health Monitoring.” Appl. Sci., Vol. 13, Februar 2023, pp. 2708.
[113] H. Fathnejat, B. Ahmadi-Nedushan, S. Hosseininejad, M. Noori, and W.A. Altabey, “A Data-Driven Structural Damage Identification Approach Using Deep Convolutional-Attention-Recurrent Neural Architecture Under Temperature Variations.” Eng. Struct., Vol. 276, February 2023, pp. 115311.
[114] J. Shu, C. Zhang, X. Chen, and Y. Niu, “Model-Informed Deep Learning Strategy with Vision Measurement for Damage Identification of Truss Structures.” Mech. Syst. Signal Proc., Vol. 196, August 2023, pp. 110327.
[115] F. Luleci, F.N. Catbas, and O. Avci, “Generative Adversarial Networks for Labeled Acceleration Data Augmentation for Structural Damage Detection.” J. Civ. Struct. Health Monit., Vol. 13, January 2023, pp. 181-198.
[116] F. Cavadas, I. F. C. Smith, and J. Figueiras, “Damage detection using data-driven methods applied to moving-load responses.” Mech. Syst. Signal Proc., Vol. 39, August-September 2013, pp. 409-425.
[117] F. Cavadas, B. J. Afonso Costa, J. A. Figueiras, M. Pimentel, and C. Félix, “Data-Driven Damage Detection Based on Moving-Loads Responses-The Luiz I Bridge.” In European Workshop on Structural Health Monitoring, Vol. 128, January 2021, pp. 837-846.
[118] R. Corbally and A. Malekjafarian, “A data-driven approach for drive-by damage detection in bridges considering the influence of temperature change.” Eng. Struct., Vol. 253, February 2022, pp. 113783.
[119] H. Li, D. Tao, Y. Huang, and Y. Bao, “A data‐driven approach for seismic damage detection of shear‐type building structures using the fractal dimension of time–frequency features.” Struct. Control. Health Monit., Vol. 20, October 2021, pp. 1191-1210.
[120] S. O. Sajedi and X. Liang, “A data‐driven framework for near real‐time and robust damage diagnosis of building structures.” Struct. Control. Health Monit., Vol. 27, March 2020, pp. e2488.
[121] M. A. Torres-Arredondo, I. Buethe, D. A. Tibaduiza, J. Rodellar, and C. P. Fritzen, “Damage detection and classification in pipework using acousto-ultrasonics and non-linear data-driven modelling.” J. Civ. Struct. Health Monit., Vol. 3, September 2013, pp. 297-306.
[122] F. Archetti, G. Arosio, A. Candelieri, I. Giordani, and R. Sormani, “Smart data driven maintenance: Improving damage detection and assessment on aerospace structures.” MetroAeroSpace, May 2014, pp. 101-106.
[123] A. Pagani and M. Enea, “Displacement and strain data-driven damage detection in multi-component and heterogeneous composite structures.” Mech. Adv. Mater. Struct., Vol. 31, May 2024, pp. 2053–2068.
[124] S. Yin, G. Wang, and H. R. Karimi, “Data-driven design of robust fault detection system for wind turbines.” Mechatronics, Vol. 24, June 2014, pp. 298-306.
[125] K. Guo and L. Zhang, “Data-driven optimization for mitigating tunnel-induced damages.” Appl. Soft. Comput., Vol. 115, January 2022, pp. 108128.
[126] H. Zhu, M. Huang, and Q. B. Zhang, “TunGPR: Enhancing data-driven maintenance for tunnel linings through synthetic datasets, deep learning and BIM.” Tunn. Undergr. Space Technol., Vol. 145, March 2024, pp. 105568.
[127] M. Zhu, H. Zhu, F. Guo, X. Chen, and J. W. Ju, “Tunnel condition assessment via cloud model‐based random forests and self‐training approach.” Comput.-Aided Civ. Infrastruct. Eng., Vol. 36, February 2021, pp. 164-179.
[128] T. H. Lin, A. Putranto, and Y. T. Wang, “Smart sensor tags for seepage sensing protected by 3D-printed case for embedding in concrete structures.” Constr. Build. Mater., Vol. 284, May 2021, pp. 122784.
[129] T. H. Lin, A. Putranto, Y. T. Wang, Q. H. Yang, R. J. Wu, C. H. Liu, C. K. Lin, and M. Chavali, “Enhancing Smart Sensor Tag Sensing Performance-Based on Modified Plasma-Assisted Electrochemical Exfoliated Graphite Nanosheet.” Polymers, Vol. 14, November 2022, pp. 5067.
[130] A. Putranto, T. H. Lin, and B. X. Huang, “Deep learning-integrated electromagnetic imaging for evaluating reinforced concrete structures in water-contact scenarios.” Autom. Constr., Vol. 164, August 2024, pp.105459.
[131] A. Van Beek and M.A. Milhorst, “Dielectric measurements to characterize the microstructural changes of young concrete.” HERON, Vol. 44, 2000, pp. 3-17.
[132] G. Klysz, J.P. Balayssac, and X. Ferrières, “Evaluation of dielectric properties of concrete by a numerical FDTD model of a GPR coupled antenna—parametric study.” NDT E Int., Vol. 41, December 2008, pp. 621-631.
[133] M.N. Soutsos, J.H. Bungey, S.G. Millard, M.R. Shaw, and A. Patterson, “Dielectric properties of concrete and their influence on radar testing.” NDT E Int., Vol. 34, September 2001, pp. 419-425.
[134] S. Kim, J. Surek, and J. Baker-Jarvis, “Electromagnetic metrology on concrete and corrosion.” J Res Natl Inst Stand Technol., Vol. 116, May-June 2011, pp. 655-669.
[135] X. Dérobert and G. Villain, “Effect of water and chloride contents and carbonation on the electromagnetic characterization of concretes on the GPR frequency band through designs of experiment.” NDT E Int., Vol. 92, December 2017, pp. 187-198.
[136] S. Jiang and S.V. Georgakopoulos, “Optimum wireless powering of sensors embedded in concrete,” IEEE Trans. Antennas Propag., Vol. 60, February 2012, pp. 1106-1113.
[137] American Concrete Institute Committee 350. Code requirements for Environmental Engineering Concrete Structures and Commentary. Farmington Hills, MI, 2006.
[138] L. Breiman, “Random Forests. Machine Learning.” Machine Learning, Vol. 45, October 2001, pp. 5-23.
[139] X. P. Huynh, S. M. Park, and Y. G. Kim, “Detection of Driver Drowsiness Using 3D Deep Neural Network and Semi-Supervised Gradient Boosting Machine” In: Chen, CS., Lu, J., Ma, KK. (eds) Computer Vision – ACCV 2016 Workshops. Lecture Notes in Computer Science, Vol. 10118, March 2017, pp. 134–145.
[140] A. Putranto, T. H. Lin, and B. X. Huang, “Investigating the Effect of Corrosion in Reinforced Concrete Structure with Novel Encoded Electromagnetic Wave Imaging Technique.” Conf. on Concrete Eng., December 2023.
[141] R. C. Gonzalez and R. E. Woods, Digital image processing. Pearson, 2018.
[142] G. Marrocco, “The art of UHF RFID antenna design: impedance-matching and size-reduction techniques.” IEEE Antennas Propag. Mag., Vol, 55, February 2008, pp. 66–79.
[143] C. Occhiuzzi, S. Caizzone, and G. Marrocco, “Passive UHF RFID antennas for sensing applications: principals, methods, and classifications.” IEEE Antennas Propag. Mag., Vol. 55, December 2013, pp. 14–34.

指導教授

林子軒(Tzu-Hsuan Lin)

審核日期

2024-7-17

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