Crack detection for wading-concrete structures using water irrigation and electric heating

Abstract

Cracking in wading-concrete structures has a worse impact on structural safety compared with conventional concrete structures. The accurate and timely monitoring of crack development plays a significant role in the safety of wading-concrete engineering. The heat-transfer rate near a crack is related to the flow velocity of the fluid in the crack. Based on this, a novel crack-identification method for underwater concrete structures is presented. This method uses water irrigation to generate seepage at the interface of a crack; then, the heat-dissipation rate in the crack area will increase because of the convective heat-transfer effect near the crack. Crack information can be identified by monitoring the cooling law and leakage flow near cracks. The proposed mobile crack-monitoring system consists of a heating system, temperature-measurement system, and irrigation system. A series of tests was conducted on a reinforced-concrete beam using this system. The crack-discrimination index ψ was defined, according to the subsection characteristics of the heat-source cooling curve. The effects of the crack width, leakage flow, and relative positions of the heat source and crack on ψ were studied. The results showed that the distribution characteristics of ψ along the monitoring line could accurately locate the crack, but not quantify the crack width. However, the leakage flow is sensitive to the crack width and can be used to identify it.

Graphical abstract

Keywords

structural health monitoring / underwater concrete structure / fiber Bragg grating / crack detection / temperature tracer method

References

[1] Saleem M, Gutierrez H. Using artificial neural network and non-destructive test for crack detection in concrete surrounding the embedded steel reinforcement. Structural Concrete, 2021, 22(5): 2849–2867

[2] Hosoda A, Adnan A, Saleem M, Yoshida Y. Improvement of artificial neural network model for thermal crack width in RC abutments using actual construction data. Proceedings of Japanese Concrete Institute—JCI Annual Proceedings, 2022, 44(1): 970–975

[3] Huang C, Li F, Zhou R. Inspection and treatment of underwater crack of upstream surface of first-stage project of Danjiangkou Dam. Yangze River, 2015, 46(6): 41–44

[4] BeiQ R. Detecting technologies for underwater projects and its application. Large Dam and Safety, 2004, 1: 37−39 (in Chinese)

[5] Chowdhury T, Sathianarayanan D, Dharani G, Ramadass G A. Failure analysis of fasteners in a remotely operated vehicle (ROV) system. Journal of Failure Analysis and Prevention, 2015, 15(6): 915–923

[6] Jacobi M. Autonomous inspection of underwater structures. Robotics and Autonomous Systems, 2015, 67: 80–86

[7] Wang X, Zhang G, Sun Y, Cao J, Wan L, Sheng M, Liu Y. AUV near-wall-following control based on adaptive disturbance observer. Ocean Engineering, 2019, 190: 106429

[8] Shi P, Fan X, Ni J, Wang G. A detection and classification approach for underwater dam cracks. Structural Health Monitoring, 2016, 15(5): 541–554

[9] Savino P, Tondolo F. Automated classification of civil structure defects based on convolutional neural network. Frontiers of Structural and Civil Engineering, 2021, 15(2): 305–317

[10] Cha Y J, Choi W, Büyüköztürk O. Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(5): 361–378

[11] Cha Y J, Choi W, Suh G, Mahmoudkhani S, Büyüköztürk O. Autonomous structural visual inspection using region-based deep learning for detecting multiple damage types. Computer-Aided Civil and Infrastructure Engineering, 2018, 33(9): 731–747

[12] Kang D H, Cha Y J. Efficient attention-based deep encoder and decoder for automatic crack segmentation. Structural Health Monitoring, 2022, 21(5): 2190–2205

[13] Chakraborty J, Katunin A, Klikowicz P, Salamak M. Early crack detection of reinforced concrete structure using embedded sensors. Sensors (Basel), 2019, 19(18): 1–22

[14] ZhangYLiJWangQLiuB. Mechanism and experimental study on crack monitoring and repair of shape memory alloy intelligent concrete. Acta Mechanica Solida Sinica, 2020, 41(2): 170−181 (in Chinese)

[15] TianC BWangJZhangFSuiQ MSunBWangZ FLiY J. Study of fiber bragg grating sensor for monitoring of concrete cracks in bridge steel tube. Instrument Technique and Sensor, 2017, 9: 20−23 (in Chinese)

[16] Jiang T, Hong Y, Zheng J, Wang L, Gu H. Crack detection of FRP-reinforced concrete beam using embedded piezoceramic smart aggregates. Sensors (Basel), 2019, 19(9): 1–20

[17] He F, Chen J, Li C, Xiong F. Temperature tracer method in structural health monitoring: A review. Measurement, 2022, 200: 111608

[18] Aralt T T, Nilsen A R. Automatic fire detection in road traffic tunnels. Tunnelling and Underground Space Technology, 2009, 24(1): 75–83

[19] Madabhushi S S C, Elshafie M, Haigh S K. Accuracy of distributed optical fiber temperature sensing for use in leak detection of subsea pipelines. Journal of Pipeline System Engineering and Practice, 2015, 6(2): 04014014

[20] Cao D F, Shi B, Zhu H H, Inyang H I, Wei G Q, Duan C Z. A soil moisture estimation method using actively heated fiber Bragg grating sensors. Engineering Geology, 2018, 242: 142–149

[21] de Morais Franca M B, Morais F J O, Carvalhaes-Dias P, Duarte L C, Siqueira Dias J A. A multiprobe heat pulse sensor for soil moisture measurement based on PCB technology. IEEE Transactions on Instrumentation and Measurement, 2019, 68(2): 606–613

[22] He F, Zhang C, Chen J, Xiong F. Study on the mobile PHS method for soil moisture monitoring based on thermal effect. IEEE Sensors Journal, 2021, 21(13): 15209–15217

[23] Khan A A, Vrabie V, Beck Y L, Mars J I, D’Urso G. Monitoring and early detection of internal erosion: Distributed sensing and processing. Structural Health Monitoring, 2014, 13(5): 562–576

[24] VogtTSchneiderPHahn-WoernleLCirpkaO A. Estimation of seepage rates in a losing stream by means of fiber-optic high-resolution vertical temperature profiling. Journal of Hydrology (Amsterdam), 2010, 380(1−2): 154−164

[25] Su H, Li H, Kang Y, Wen Z. Experimental study on distributed optical fiber-based approach monitoring saturation line in levee engineering. Optics & Laser Technology, 2018, 99: 19–29

[26] Cote A, Carrier B, Leduc J, Noël P, Gervais R. Water leakage detection using optical fiber at the Peribonka dam. In: Proceedings of the 7th International Symposium on Field Measurments in Geomechanics. Boston, MA: ASCE, 2007, 1–12

[27] Chen J, Cheng F, Xiong F, Ge Q, Zhang S. An experimental study: Fiber Bragg grating-hydrothermal cycling integration system for seepage monitoring of rockfill dams. Structural Health Monitoring, 2017, 16(1): 50–61

[28] Chen J, Zheng J, Xiong F, Ge Q, Yan Q, Cheng F. Experimental investigation of leak detection using mobile distributed monitoring system. Smart Materials and Structures, 2018, 27(1): 015025

[29] Chen J, Xiong F, Zheng J, Ge Q, Cheng F. The influence of infiltration angle on the identification effect of seepage with linear heat source method. Measurement, 2019, 148: 106974

[30] Chen J, Fang X, Cheng F, Ge Q, Xiong F. Sensitivity analysis and seepage/leakage monitoring using point heat source. Geotechnique, 2021, 71(10): 911–924

[31] Liu Y, Xiao H, Huang S, Wu W, Chen Z. Research on the layout of optical fibers applied for determining the integrity of cast-in-situ piles. Optical Fiber Technology, 2018, 45: 173–181

[32] Zhao X, Ba Q, Li L, Gong P, Ou J. A three-index estimator based on active thermometry and a novel monitoring system of scour under submarine pipelines. Sensors and Actuators. A, Physical, 2012, 183: 115–122

[33] Chen J, Song Y, Xiong F, Ai T. A thermal effects-based method for void detection in concrete face rockfill dams. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1001307

[34] Chen J, Luo Y, Xiong J, Zhang S J, Xia M Y, Yang H J, Ge Q. A thermal-effect-based monitoring method for debris flow warning. Geomorphology, 2022, 400: 108097

[35] Chen J, Xiong F, Zhu Y, Yan H. A crack detection method for underwater concrete structures using sensing-heating system with porous casing. Measurement, 2021, 168: 108332

[36] Zhu Y, Chen J, Zhang Y, Xiong F, He F, Fang X. Temperature tracer method for crack detection in underwater concrete structures. Structural Control and Health Monitoring, 2020, 27(9): e2595

Acknowledgements

This work was supported by the Natural Science Foundation of Sichuan Province (No. 2022NSFSC0422), China and the Fundamental Research Funds for the Central Universities, China.

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2023 Higher Education Press