Electromagnetic wave-driven deep learning for structural evaluation of reinforced concrete strength
Abstract
Monitoring the performance of reinforced concrete structures, particularly in terms of strength reduction, presents significant challenges due to the practical limitations of traditional detection methods. This study introduces an innovative framework that incorporates a non-destructive technique using electromagnetic waves (EM-waves) transmitted via Radio Frequency Identification (RFID) technology, combined with two-dimensional (2-D) Fourier transform, fractal dimension analysis, and deep learning techniques to predict reductions in structural strength. Experiments were conducted on three reinforced concrete beam (RCB) specimens exhibiting various levels of reinforcement corrosion. From these, a dataset of 1,800 EMwave images was generated and classified into “normal” and “reduced strength” categories. These categories were used to train and validate a Convolutional Neural Network (CNN), which demonstrated robust performance, achieving a high accuracy of 0.91 and an F1-score of 0.93 in classifying instances of reduced structural strength. This approach offers a promising solution for detecting strength reduction in reinforced concrete infrastructures, enhancing both safety and maintenance efficiency.
First published online 5 November 2024
Keyword : Convolutional Neural Network (CNN), electromagnetic waves, fractal dimension analysis, radio frequency identification (RFID), strength reduction detection
How to Cite
Putranto, A., Huang, B.-X., & Lin, T.-H. (2024). Electromagnetic wave-driven deep learning for structural evaluation of reinforced concrete strength. Journal of Civil Engineering and Management, 1-19. https://doi.org/10.3846/jcem.2024.22266
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
ASTM International. (2021). Standard guide for examination and evaluation of pitting corrosion (G 46-21).
Atha, D. J., & Jahanshahi, M. R. (2018). Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection. Structural Health Monitoring, 17(5), 1110–1128. https://doi.org/10.1177/1475921717737051
Azad, A. K., Ahmad, S., & Azher, S. A. (2007). Residual strength of corrosion-damaged reinforced concrete beams. ACI materials Journal, 104(1), Article 40. https://doi.org/10.14359/18493
Ballim, Y., Reid, J. C., & Kemp, A. R. (2001). Deflection of RC beams under simultaneous load and steel corrosion. Magazine of Concrete Research, 53(3), 171–181. https://doi.org/10.1680/macr.55.4.405.37588
Ballim, Y., & Reid, J. C. (2003). Reinforcement corrosion and the deflection of RC beams – an experimental critique of current test methods. Cement and Concrete Composites, 25(6), 625–632. https://doi.org/10.1016/S0958-9465(02)00076-8
Bartholmai, M., Johann, S., Kammermeier, M., Müller, M., & Strangfeld, C. (2016, October). Transmission characteristics of RFID sensor systems embedded in concrete. In 2016 IEEE Sensors. IEEE. https://doi.org/10.1109/ICSENS.2016.7808920
Behnia, A., Chai, H. K., Yorikawa, M., Momoki, S., Terazawa, M., & Shiotani, T. (2014). Integrated non-destructive assessment of concrete structures under flexure by acoustic emission and travel time tomography. Construction and Building Materials, 67, 202–215. https://doi.org/10.1016/j.conbuildmat.2014.05.011
Campione, G., Cannella, F., & Cavaleri, L. (2017). Shear and flexural strength prediction of corroded RC beams. Construction and Building Materials, 149, 395–405. https://doi.org/10.1016/j.conbuildmat.2017.05.125
Cavaleri, L., Barkhordari, M. S., Repapis, C. C., Armaghani, D. J., Ulrikh, D. V., & Asteris, P. G. (2022). Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete. Construction and Building Materials, 359, Article 129504. https://doi.org/10.1016/j.conbuildmat.2022.129504
Chalioris, C. E., Kytinou, V. K., Voutetaki, M. E., & Karayannis, C. G. (2021). Flexural damage diagnosis in reinforced concrete beams using a wireless admittance monitoring system – Tests and finite element analysis. Sensors, 21(3), Article 679. https://doi.org/10.3390/s21030679
Cha, Y.-J., Choi, W., & Büyüköztürk, O. (2017). Deep learning-based crack detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32(5), 361–378. https://doi.org/10.1111/mice.12263
Chiba, H., & Miyazaki, Y. (1998). Reflection and transmission characteristics of radio waves at a building site due to reinforced concrete slabs. Electronics and Communications in Japan (Part I: Communications), 81(8), 68–80. https://doi.org/10.1002/(SICI)1520-6424(199808)81:8%3C68::AID-ECJA8%3E3.0.CO;2-#
Coronelli, D., & Gambarova, P. (2004). Structural assessment of corroded reinforced concrete beams: Modeling guidelines. Journal of Structural Engineering, 130(8), 1214–1224. https://doi.org/10.1061/(ASCE)0733-9445(2004)130:8(1214)
Ramesht, M. H. (1995). Effect of corrosion on flexural behavior of ferrocement. Journal of Ferrocement, 25(2), 105–113.
Draudvilienė, L., Meškuotienė, A., Raišutis, R., Tumšys, O., & Surgautė, L. (2022). Accuracy assessment of the 2D-FFT method based on peak detection of the spectrum magnitude at the particular frequencies using the lamb wave signals. Sensors, 22(18), Article 6750. https://doi.org/10.3390/s22186750
Dogan, G., Arslan, M. H., & Ilki, A. (2023). Detection of damages caused by earthquake and reinforcement corrosion in RC buildings with deep transfer learning. Engineering Structures, 279, Article 115629. https://doi.org/10.1016/j.engstruct.2023.115629
Du, Y., Clark, L. A., & Chan, A. H. (2007). Impact of reinforcement corrosion on ductile behavior of reinforced concrete beams. ACI Structural Journal, 104(3), Article 285. https://doi.org/10.14359/18618
El Maaddawy, T., Soudki, K., & Topper, T. (2005). Long-term performance of corrosion-damaged reinforced concrete beams. ACI Structural Journal, 102(5), Article 649. https://doi.org/10.14359/14660
Fan, L., & Shi, X. (2022). Techniques of corrosion monitoring of steel rebar in reinforced concrete structures: A review. Structural Health Monitoring, 21(4), 1879–1905. https://doi.org/10.1177/14759217211030911
Fernandez, I., Herrador, M. F., Marí, A. R., & Bairán, J. M. (2018). Ultimate capacity of corroded statically indeterminate reinforced concrete members. International Journal of Concrete Structures and Materials, 12(1), Article 75. https://doi.org/10.1186/s40069-018-0297-9
Ferreira, P. M., Machado, M. A., Carvalho, M. S., & Vidal, C. (2022). Embedded sensors for structural health monitoring: methodologies and applications review. Sensors, 22(21), Article 8320. https://doi.org/10.3390/s22218320
Gonzalez, R. C., & Woods, R. E. (2018). Digital image processing (4th ed.). Pearson.
Halabe, U. B., Maser, K., & Kausel, E. (1989). Propagation characteristics of electromagnetic waves in concrete. US Army Research Office.
Jiang, S., & Georgakopoulos, S. V. (2011, April). Optimum wireless power transmission through reinforced concrete structure. In 2011 IEEE International Conference on RFID (pp. 50–56). IEEE. https://doi.org/10.1109/RFID.2011.5764636
Jiang, S., Georgakopoulos, S. V., & Jonah, O. (2012, July). Power transmission for sensors embedded in reinforced concrete structures. In Proceedings of the 2012 IEEE International Symposium on Antennas and Propagation. IEEE. https://doi.org/10.1109/APS.2012.6348425
Jnaid, F., & Aboutaha, R. S. (2016). Residual flexural strength of corroded reinforced concrete beams. Engineering Structures, 119, 198–216. https://doi.org/10.1016/j.engstruct.2016.04.018
Laxman, K. C., Tabassum, N., Ai, L., Cole, C., & Ziehl, P. (2023). Automated crack detection and crack depth prediction for reinforced concrete structures using deep learning. Construction and Building Materials, 370, Article 130709. https://doi.org/10.1016/j.conbuildmat.2023.130709
Li, Z., Jin, Z., Wang, P., & Zhao, T. (2021). Corrosion mechanism of reinforced bars inside concrete and relevant monitoring or detection apparatus: A review. Construction and Building Materials, 279, Article 122432. https://doi.org/10.1016/j.conbuildmat.2021.122432
Li, Q., Dong, Z., He, Q., Fu, C., & Jin, X. (2022). Effects of reinforcement corrosion and sustained load on mechanical behavior of reinforced concrete columns. Materials, 15(10), Article 3590. https://doi.org/10.3390/ma15103590
Lin, T. H., Putranto, A., & Wang, Y. T. (2021). Smart sensor tags for seepage sensing protected by 3D-printed case for embedding in concrete structures. Construction and Building Materials, 284, Article 122784. https://doi.org/10.1016/j.conbuildmat.2021.122784
Lin, T. H., Putranto, A., Wang, Y. T, Yang, Q. H, Wu, R. J., Liu, C. H., Lin, C. K., & Chavali, M. (2022). Enhancing smart sensor tag sensing performance-based on modified plasma-assisted electrochemical exfoliated graphite nanosheet. Polymers, 14(23), Article 5067. https://doi.org/10.3390/polym14235067
Lin, T. H., Chang, C. T., & Putranto, A (2024a). Real-time hollow defect detection in tiles using on-device tiny machine learning. Measurement Science and Technology, 35(5), Article 056006. https://doi.org/10.1088/1361-6501/ad2665
Lin, T. H., Chang, C. T., Zhuang, T. H., & Putranto, A (2024b). Tiny machine learning empowers climbing inspection robots for real-time multiobject bolt-defect detection. Engineering Applications of Artificial Intelligence, 133, Article 108618. https://doi.org/10.1016/j.engappai.2024.108618
Mehta, P. K., & Monteiro, P. J. (2014). Concrete: microstructure, properties, and materials. McGraw-Hill Education.
Meng, Z., & Li, Z. (2016). RFID tag as a sensor-a review on the innovative designs and applications. Measurement Science Review, 16(6), Article 305. https://doi.org/10.1515/msr-2016-0039
Nguyen, N.-M., & Chou, J.-S. (2024). Forecasting mechanical properties of steel structures through dynamic metaheuristic optimization for adaptive machine learning. Journal of Civil Engineering and Management, 30(5), 414–436. https://doi.org/10.3846/jcem.2024.21356
Obunguta, F., Matsushima, K., & Susaki, J. (2024). Probabilistic management of pavement defects with image processing techniques. Journal of Civil Engineering and Management, 30(2), 114–132. https://doi.org/10.3846/jcem.2024.20401
Putranto, A., Lin, T. H., & Huang, B. X. (2023). Investigating the effect of corrosion in reinforced concrete structure with novel encoded electromagnetic wave imaging technique. In Taiwan Concrete Institute 2023 Conference on Concrete Engineering, Taipei, Taiwan. https://doi.org/10.5281/zenodo.11125725
Putranto, A., Lin, T. H., & Huang, B. X. (2024). Deep learning-integrated electromagnetic imaging for evaluating reinforced concrete structures in water-contact scenarios. Automation in Construction, 164, Article 105459. https://doi.org/10.1016/j.autcon.2024.105459
Rucka, M., & Wilde, K. (2015). Ultrasound monitoring for evaluation of damage in reinforced concrete. Bulletin of the Polish Academy of Sciences: Technical Sciences, 63(1), 65–75. https://doi.org/10.1515/bpasts-2015-0008
Senin, S. F., Hamid, R., Ahmad, J., Rosli, M. I. F., Yusuff, A., Rohim, R., Ghani, K. D. A., & Noor, S. M. (2019). Damage detection of artificial corroded rebars and quantification using non-destructive methods on reinforced concrete structure. Journal of Physics: Conference Series, 1349, Article 012044. https://doi.org/10.1088/1742-6596/1349/1/012044
Strangfeld, C., Johann, S., & Bartholmai, M. (2019). Smart RFID sensors embedded in building structures for early damage detection and long-term monitoring. Sensors, 19(24), Article 5514. https://doi.org/10.3390/s19245514
Taheri, S. (2019). A review on five key sensors for monitoring of concrete structures. Construction and Building Materials, 204, 492–509. https://doi.org/10.1016/j.conbuildmat.2019.01.172
Taiwan Transportation Safety Board. (2023). Final report released on Nanfangao sea-crossing bridge collapse. https://www.ttsb.gov.tw/english/16051/16113/16114/28249/post
Tao, L., & Xue, X. (2024). An improved random forest model to predict bond strength of FRP-to-concrete. Journal of Civil Engineering and Management, 30(6), 520–535. https://doi.org/10.3846/jcem.2024.21636
Val, D. V. (2007). Deterioration of strength of RC beams due to corrosion and its influence on beam reliability. Journal of Structural Engineering, 133(9), 1297–1306. https://doi.org/10.1061/(ASCE)0733-9445(2007)133:9(1297)
Yeum, C. M., & Dyke, S. J. (2015). Vision-based automated crack detection for bridge inspection. Computer-Aided Civil and Infrastructure Engineering, 30(10), 759–770. https://doi.org/10.1111/mice.12141
Zhang, J., Tian, G. Y., Marindra, A. M., Sunny, A. I., & Zhao, A. B. (2017). A review of passive RFID tag antenna-based sensors and systems for structural health monitoring applications. Sensors, 17(2), Article 265. https://doi.org/10.3390/s17020265
Zhang, W., Zhang, H., Gu, X., & Liu, W. (2018). Structural behavior of corroded reinforced concrete beams under sustained loading. Construction and Building Materials, 174, 675–683. https://doi.org/10.1016/j.conbuildmat.2018.04.145
Zhang, J., Cao, Y., Xia, L., Zhang, D., Xu, W., & Liu, Y. (2023). Intelligent prediction of the frost resistance of high-performance concrete: a machine learning method. Journal of Civil Engineering and Management, 29(6), 516–529. https://doi.org/10.3846/jcem.2023.19226
Zhu, W., François, R., Coronelli, D., & Cleland, D. (2013). Effect of corrosion of reinforcement on the mechanical behaviour of highly corroded RC beams. Engineering Structures, 56, 544–554. https://doi.org/10.1016/j.engstruct.2013.04.017
Atha, D. J., & Jahanshahi, M. R. (2018). Evaluation of deep learning approaches based on convolutional neural networks for corrosion detection. Structural Health Monitoring, 17(5), 1110–1128. https://doi.org/10.1177/1475921717737051
Azad, A. K., Ahmad, S., & Azher, S. A. (2007). Residual strength of corrosion-damaged reinforced concrete beams. ACI materials Journal, 104(1), Article 40. https://doi.org/10.14359/18493
Ballim, Y., Reid, J. C., & Kemp, A. R. (2001). Deflection of RC beams under simultaneous load and steel corrosion. Magazine of Concrete Research, 53(3), 171–181. https://doi.org/10.1680/macr.55.4.405.37588
Ballim, Y., & Reid, J. C. (2003). Reinforcement corrosion and the deflection of RC beams – an experimental critique of current test methods. Cement and Concrete Composites, 25(6), 625–632. https://doi.org/10.1016/S0958-9465(02)00076-8
Bartholmai, M., Johann, S., Kammermeier, M., Müller, M., & Strangfeld, C. (2016, October). Transmission characteristics of RFID sensor systems embedded in concrete. In 2016 IEEE Sensors. IEEE. https://doi.org/10.1109/ICSENS.2016.7808920
Behnia, A., Chai, H. K., Yorikawa, M., Momoki, S., Terazawa, M., & Shiotani, T. (2014). Integrated non-destructive assessment of concrete structures under flexure by acoustic emission and travel time tomography. Construction and Building Materials, 67, 202–215. https://doi.org/10.1016/j.conbuildmat.2014.05.011
Campione, G., Cannella, F., & Cavaleri, L. (2017). Shear and flexural strength prediction of corroded RC beams. Construction and Building Materials, 149, 395–405. https://doi.org/10.1016/j.conbuildmat.2017.05.125
Cavaleri, L., Barkhordari, M. S., Repapis, C. C., Armaghani, D. J., Ulrikh, D. V., & Asteris, P. G. (2022). Convolution-based ensemble learning algorithms to estimate the bond strength of the corroded reinforced concrete. Construction and Building Materials, 359, Article 129504. https://doi.org/10.1016/j.conbuildmat.2022.129504
Chalioris, C. E., Kytinou, V. K., Voutetaki, M. E., & Karayannis, C. G. (2021). Flexural damage diagnosis in reinforced concrete beams using a wireless admittance monitoring system – Tests and finite element analysis. Sensors, 21(3), Article 679. https://doi.org/10.3390/s21030679
Cha, Y.-J., Choi, W., & Büyüköztürk, O. (2017). Deep learning-based crack detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 32(5), 361–378. https://doi.org/10.1111/mice.12263
Chiba, H., & Miyazaki, Y. (1998). Reflection and transmission characteristics of radio waves at a building site due to reinforced concrete slabs. Electronics and Communications in Japan (Part I: Communications), 81(8), 68–80. https://doi.org/10.1002/(SICI)1520-6424(199808)81:8%3C68::AID-ECJA8%3E3.0.CO;2-#
Coronelli, D., & Gambarova, P. (2004). Structural assessment of corroded reinforced concrete beams: Modeling guidelines. Journal of Structural Engineering, 130(8), 1214–1224. https://doi.org/10.1061/(ASCE)0733-9445(2004)130:8(1214)
Ramesht, M. H. (1995). Effect of corrosion on flexural behavior of ferrocement. Journal of Ferrocement, 25(2), 105–113.
Draudvilienė, L., Meškuotienė, A., Raišutis, R., Tumšys, O., & Surgautė, L. (2022). Accuracy assessment of the 2D-FFT method based on peak detection of the spectrum magnitude at the particular frequencies using the lamb wave signals. Sensors, 22(18), Article 6750. https://doi.org/10.3390/s22186750
Dogan, G., Arslan, M. H., & Ilki, A. (2023). Detection of damages caused by earthquake and reinforcement corrosion in RC buildings with deep transfer learning. Engineering Structures, 279, Article 115629. https://doi.org/10.1016/j.engstruct.2023.115629
Du, Y., Clark, L. A., & Chan, A. H. (2007). Impact of reinforcement corrosion on ductile behavior of reinforced concrete beams. ACI Structural Journal, 104(3), Article 285. https://doi.org/10.14359/18618
El Maaddawy, T., Soudki, K., & Topper, T. (2005). Long-term performance of corrosion-damaged reinforced concrete beams. ACI Structural Journal, 102(5), Article 649. https://doi.org/10.14359/14660
Fan, L., & Shi, X. (2022). Techniques of corrosion monitoring of steel rebar in reinforced concrete structures: A review. Structural Health Monitoring, 21(4), 1879–1905. https://doi.org/10.1177/14759217211030911
Fernandez, I., Herrador, M. F., Marí, A. R., & Bairán, J. M. (2018). Ultimate capacity of corroded statically indeterminate reinforced concrete members. International Journal of Concrete Structures and Materials, 12(1), Article 75. https://doi.org/10.1186/s40069-018-0297-9
Ferreira, P. M., Machado, M. A., Carvalho, M. S., & Vidal, C. (2022). Embedded sensors for structural health monitoring: methodologies and applications review. Sensors, 22(21), Article 8320. https://doi.org/10.3390/s22218320
Gonzalez, R. C., & Woods, R. E. (2018). Digital image processing (4th ed.). Pearson.
Halabe, U. B., Maser, K., & Kausel, E. (1989). Propagation characteristics of electromagnetic waves in concrete. US Army Research Office.
Jiang, S., & Georgakopoulos, S. V. (2011, April). Optimum wireless power transmission through reinforced concrete structure. In 2011 IEEE International Conference on RFID (pp. 50–56). IEEE. https://doi.org/10.1109/RFID.2011.5764636
Jiang, S., Georgakopoulos, S. V., & Jonah, O. (2012, July). Power transmission for sensors embedded in reinforced concrete structures. In Proceedings of the 2012 IEEE International Symposium on Antennas and Propagation. IEEE. https://doi.org/10.1109/APS.2012.6348425
Jnaid, F., & Aboutaha, R. S. (2016). Residual flexural strength of corroded reinforced concrete beams. Engineering Structures, 119, 198–216. https://doi.org/10.1016/j.engstruct.2016.04.018
Laxman, K. C., Tabassum, N., Ai, L., Cole, C., & Ziehl, P. (2023). Automated crack detection and crack depth prediction for reinforced concrete structures using deep learning. Construction and Building Materials, 370, Article 130709. https://doi.org/10.1016/j.conbuildmat.2023.130709
Li, Z., Jin, Z., Wang, P., & Zhao, T. (2021). Corrosion mechanism of reinforced bars inside concrete and relevant monitoring or detection apparatus: A review. Construction and Building Materials, 279, Article 122432. https://doi.org/10.1016/j.conbuildmat.2021.122432
Li, Q., Dong, Z., He, Q., Fu, C., & Jin, X. (2022). Effects of reinforcement corrosion and sustained load on mechanical behavior of reinforced concrete columns. Materials, 15(10), Article 3590. https://doi.org/10.3390/ma15103590
Lin, T. H., Putranto, A., & Wang, Y. T. (2021). Smart sensor tags for seepage sensing protected by 3D-printed case for embedding in concrete structures. Construction and Building Materials, 284, Article 122784. https://doi.org/10.1016/j.conbuildmat.2021.122784
Lin, T. H., Putranto, A., Wang, Y. T, Yang, Q. H, Wu, R. J., Liu, C. H., Lin, C. K., & Chavali, M. (2022). Enhancing smart sensor tag sensing performance-based on modified plasma-assisted electrochemical exfoliated graphite nanosheet. Polymers, 14(23), Article 5067. https://doi.org/10.3390/polym14235067
Lin, T. H., Chang, C. T., & Putranto, A (2024a). Real-time hollow defect detection in tiles using on-device tiny machine learning. Measurement Science and Technology, 35(5), Article 056006. https://doi.org/10.1088/1361-6501/ad2665
Lin, T. H., Chang, C. T., Zhuang, T. H., & Putranto, A (2024b). Tiny machine learning empowers climbing inspection robots for real-time multiobject bolt-defect detection. Engineering Applications of Artificial Intelligence, 133, Article 108618. https://doi.org/10.1016/j.engappai.2024.108618
Mehta, P. K., & Monteiro, P. J. (2014). Concrete: microstructure, properties, and materials. McGraw-Hill Education.
Meng, Z., & Li, Z. (2016). RFID tag as a sensor-a review on the innovative designs and applications. Measurement Science Review, 16(6), Article 305. https://doi.org/10.1515/msr-2016-0039
Nguyen, N.-M., & Chou, J.-S. (2024). Forecasting mechanical properties of steel structures through dynamic metaheuristic optimization for adaptive machine learning. Journal of Civil Engineering and Management, 30(5), 414–436. https://doi.org/10.3846/jcem.2024.21356
Obunguta, F., Matsushima, K., & Susaki, J. (2024). Probabilistic management of pavement defects with image processing techniques. Journal of Civil Engineering and Management, 30(2), 114–132. https://doi.org/10.3846/jcem.2024.20401
Putranto, A., Lin, T. H., & Huang, B. X. (2023). Investigating the effect of corrosion in reinforced concrete structure with novel encoded electromagnetic wave imaging technique. In Taiwan Concrete Institute 2023 Conference on Concrete Engineering, Taipei, Taiwan. https://doi.org/10.5281/zenodo.11125725
Putranto, A., Lin, T. H., & Huang, B. X. (2024). Deep learning-integrated electromagnetic imaging for evaluating reinforced concrete structures in water-contact scenarios. Automation in Construction, 164, Article 105459. https://doi.org/10.1016/j.autcon.2024.105459
Rucka, M., & Wilde, K. (2015). Ultrasound monitoring for evaluation of damage in reinforced concrete. Bulletin of the Polish Academy of Sciences: Technical Sciences, 63(1), 65–75. https://doi.org/10.1515/bpasts-2015-0008
Senin, S. F., Hamid, R., Ahmad, J., Rosli, M. I. F., Yusuff, A., Rohim, R., Ghani, K. D. A., & Noor, S. M. (2019). Damage detection of artificial corroded rebars and quantification using non-destructive methods on reinforced concrete structure. Journal of Physics: Conference Series, 1349, Article 012044. https://doi.org/10.1088/1742-6596/1349/1/012044
Strangfeld, C., Johann, S., & Bartholmai, M. (2019). Smart RFID sensors embedded in building structures for early damage detection and long-term monitoring. Sensors, 19(24), Article 5514. https://doi.org/10.3390/s19245514
Taheri, S. (2019). A review on five key sensors for monitoring of concrete structures. Construction and Building Materials, 204, 492–509. https://doi.org/10.1016/j.conbuildmat.2019.01.172
Taiwan Transportation Safety Board. (2023). Final report released on Nanfangao sea-crossing bridge collapse. https://www.ttsb.gov.tw/english/16051/16113/16114/28249/post
Tao, L., & Xue, X. (2024). An improved random forest model to predict bond strength of FRP-to-concrete. Journal of Civil Engineering and Management, 30(6), 520–535. https://doi.org/10.3846/jcem.2024.21636
Val, D. V. (2007). Deterioration of strength of RC beams due to corrosion and its influence on beam reliability. Journal of Structural Engineering, 133(9), 1297–1306. https://doi.org/10.1061/(ASCE)0733-9445(2007)133:9(1297)
Yeum, C. M., & Dyke, S. J. (2015). Vision-based automated crack detection for bridge inspection. Computer-Aided Civil and Infrastructure Engineering, 30(10), 759–770. https://doi.org/10.1111/mice.12141
Zhang, J., Tian, G. Y., Marindra, A. M., Sunny, A. I., & Zhao, A. B. (2017). A review of passive RFID tag antenna-based sensors and systems for structural health monitoring applications. Sensors, 17(2), Article 265. https://doi.org/10.3390/s17020265
Zhang, W., Zhang, H., Gu, X., & Liu, W. (2018). Structural behavior of corroded reinforced concrete beams under sustained loading. Construction and Building Materials, 174, 675–683. https://doi.org/10.1016/j.conbuildmat.2018.04.145
Zhang, J., Cao, Y., Xia, L., Zhang, D., Xu, W., & Liu, Y. (2023). Intelligent prediction of the frost resistance of high-performance concrete: a machine learning method. Journal of Civil Engineering and Management, 29(6), 516–529. https://doi.org/10.3846/jcem.2023.19226
Zhu, W., François, R., Coronelli, D., & Cleland, D. (2013). Effect of corrosion of reinforcement on the mechanical behaviour of highly corroded RC beams. Engineering Structures, 56, 544–554. https://doi.org/10.1016/j.engstruct.2013.04.017