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Frontiers of Structural and Civil Engineering

ISSN 2095-2430

ISSN 2095-2449(Online)

CN 10-1023/X

Postal Subscription Code 80-968

2018 Impact Factor: 1.272

Front. Struct. Civ. Eng.    2023, Vol. 17 Issue (3) : 368-377    https://doi.org/10.1007/s11709-022-0926-x
RESEARCH ARTICLE
Crack detection for wading-concrete structures using water irrigation and electric heating
Jiang CHEN1,2,3, Zizhen ZENG3, Ying LUO3, Feng XIONG1,2,3, Fei CHENG4()
1. Failure Mechanics and Engineering Disaster Prevention Key Laboratory of Sichuan Province, Sichuan University, Chengdu 610065, China
2. MOE Key Laboratory of Deep Earth Science and Engineering, Sichuan University, Chengdu 610065, China
3. College of Architecture and Environment, Sichuan University, Chengdu 610065, China
4. College of Biomass Science and Engineering, Sichuan University, Chengdu 610065, China
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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.

Keywords structural health monitoring      underwater concrete structure      fiber Bragg grating      crack detection      temperature tracer method     
Corresponding Author(s): Fei CHENG   
Just Accepted Date: 25 December 2022   Online First Date: 07 April 2023    Issue Date: 24 May 2023
 Cite this article:   
Jiang CHEN,Zizhen ZENG,Ying LUO, et al. Crack detection for wading-concrete structures using water irrigation and electric heating[J]. Front. Struct. Civ. Eng., 2023, 17(3): 368-377.
 URL:  
https://academic.hep.com.cn/fsce/EN/10.1007/s11709-022-0926-x
https://academic.hep.com.cn/fsce/EN/Y2023/V17/I3/368
Fig.1  Scheme of a crack-monitoring system for underwater concrete structures: (a) uncracked; (b) cracked.
Fig.2  Scheme of the implementation of a constant-level water tank.
Fig.3  Sketch of the thermal effect near a crack.
Fig.4  Sketch of test specimen.
Fig.5  Test site in still water.
Fig.6  Sectional view of sensing-heating element.
Fig.7  Layout of measuring points.
Fig.8  Semilogarithmic graph of time–history curves of Γ.
Fig.9  Test site in flowing water.
Fig.10  Comparison of the time–history curves of lgΓ in still water and flowing water.
Fig.11  Distribution of ψ with different crack widths and leakage flows: (a) w = 0.2 mm; (b) w = 0.4 mm; (c) w = 0.6 mm; (d) w = 1.0 mm; (e) w = 1.6 mm.
Fig.12  Distribution of ψ under different crack widths and leakage flows.
Fig.13  Relationship between qw and ψ for measuring point #5.
Fig.14  Relationship between ψ and w for measuring point #5.
Fig.15  Relationship between qw and w.
1 M Saleem, H Gutierrez. 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
https://doi.org/10.1002/suco.202000767
2 A Hosoda, A Adnan, M Saleem, Y Yoshida. 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 C Huang, F Li, R Zhou. Inspection and treatment of underwater crack of upstream surface of first-stage project of Danjiangkou Dam. Yangze River, 2015, 46(6): 41–44
4 Q R Bei. Detecting technologies for underwater projects and its application. Large Dam and Safety, 2004, 1: 37−39 (in Chinese)
5 T Chowdhury, D Sathianarayanan, G Dharani, G A Ramadass. Failure analysis of fasteners in a remotely operated vehicle (ROV) system. Journal of Failure Analysis and Prevention, 2015, 15(6): 915–923
https://doi.org/10.1007/s11668-015-0034-5
6 M Jacobi. Autonomous inspection of underwater structures. Robotics and Autonomous Systems, 2015, 67: 80–86
https://doi.org/10.1016/j.robot.2014.10.006
7 X Wang, G Zhang, Y Sun, J Cao, L Wan, M Sheng, Y Liu. AUV near-wall-following control based on adaptive disturbance observer. Ocean Engineering, 2019, 190: 106429
https://doi.org/10.1016/j.oceaneng.2019.106429
8 P Shi, X Fan, J Ni, G Wang. A detection and classification approach for underwater dam cracks. Structural Health Monitoring, 2016, 15(5): 541–554
https://doi.org/10.1177/1475921716651039
9 P Savino, F Tondolo. Automated classification of civil structure defects based on convolutional neural network. Frontiers of Structural and Civil Engineering, 2021, 15(2): 305–317
https://doi.org/10.1007/s11709-021-0725-9
10 Y J Cha, W Choi, O Büyüköztürk. Deep learning-based crack damage detection using convolutional neural networks. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(5): 361–378
https://doi.org/10.1111/mice.12263
11 Y J Cha, W Choi, G Suh, S Mahmoudkhani, O Büyüköztürk. 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
https://doi.org/10.1111/mice.12334
12 D H Kang, Y J Cha. Efficient attention-based deep encoder and decoder for automatic crack segmentation. Structural Health Monitoring, 2022, 21(5): 2190–2205
https://doi.org/10.1177/14759217211053776
13 J Chakraborty, A Katunin, P Klikowicz, M Salamak. Early crack detection of reinforced concrete structure using embedded sensors. Sensors (Basel), 2019, 19(18): 1–22
https://doi.org/10.3390/s19183879
14 Y ZhangJ LiQ WangB Liu. 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 C B TianJ WangF ZhangQ M SuiB SunZ F WangY J Li. 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 T Jiang, Y Hong, J Zheng, L Wang, H Gu. Crack detection of FRP-reinforced concrete beam using embedded piezoceramic smart aggregates. Sensors (Basel), 2019, 19(9): 1–20
https://doi.org/10.3390/s19091979
17 F He, J Chen, C Li, F Xiong. Temperature tracer method in structural health monitoring: A review. Measurement, 2022, 200: 111608
https://doi.org/10.1016/j.measurement.2022.111608
18 T T Aralt, A R Nilsen. Automatic fire detection in road traffic tunnels. Tunnelling and Underground Space Technology, 2009, 24(1): 75–83
https://doi.org/10.1016/j.tust.2008.04.001
19 S S C Madabhushi, M Elshafie, S K Haigh. 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
https://doi.org/10.1061/(ASCE)PS.1949-1204.0000189
20 D F Cao, B Shi, H H Zhu, H I Inyang, G Q Wei, C Z Duan. A soil moisture estimation method using actively heated fiber Bragg grating sensors. Engineering Geology, 2018, 242: 142–149
https://doi.org/10.1016/j.enggeo.2018.05.024
21 M B de Morais Franca, F J O Morais, P Carvalhaes-Dias, L C Duarte, J A Siqueira Dias. A multiprobe heat pulse sensor for soil moisture measurement based on PCB technology. IEEE Transactions on Instrumentation and Measurement, 2019, 68(2): 606–613
https://doi.org/10.1109/TIM.2018.2843605
22 F He, C Zhang, J Chen, F Xiong. Study on the mobile PHS method for soil moisture monitoring based on thermal effect. IEEE Sensors Journal, 2021, 21(13): 15209–15217
https://doi.org/10.1109/JSEN.2021.3073143
23 A A Khan, V Vrabie, Y L Beck, J I Mars, G D’Urso. Monitoring and early detection of internal erosion: Distributed sensing and processing. Structural Health Monitoring, 2014, 13(5): 562–576
https://doi.org/10.1177/1475921714532994
24 T VogtP SchneiderL Hahn-WoernleO A Cirpka. 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 H Su, H Li, Y Kang, Z Wen. Experimental study on distributed optical fiber-based approach monitoring saturation line in levee engineering. Optics & Laser Technology, 2018, 99: 19–29
https://doi.org/10.1016/j.optlastec.2017.06.032
26 A Cote, B Carrier, J Leduc, P Noël, R Gervais. 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 J Chen, F Cheng, F Xiong, Q Ge, S Zhang. An experimental study: Fiber Bragg grating-hydrothermal cycling integration system for seepage monitoring of rockfill dams. Structural Health Monitoring, 2017, 16(1): 50–61
https://doi.org/10.1177/1475921716661874
28 J Chen, J Zheng, F Xiong, Q Ge, Q Yan, F Cheng. Experimental investigation of leak detection using mobile distributed monitoring system. Smart Materials and Structures, 2018, 27(1): 015025
https://doi.org/10.1088/1361-665X/aa9c78
29 J Chen, F Xiong, J Zheng, Q Ge, F Cheng. The influence of infiltration angle on the identification effect of seepage with linear heat source method. Measurement, 2019, 148: 106974
https://doi.org/10.1016/j.measurement.2019.106974
30 J Chen, X Fang, F Cheng, Q Ge, F Xiong. Sensitivity analysis and seepage/leakage monitoring using point heat source. Geotechnique, 2021, 71(10): 911–924
https://doi.org/10.1680/jgeot.19.P.245
31 Y Liu, H Xiao, S Huang, W Wu, Z Chen. Research on the layout of optical fibers applied for determining the integrity of cast-in-situ piles. Optical Fiber Technology, 2018, 45: 173–181
https://doi.org/10.1016/j.yofte.2018.07.008
32 X Zhao, Q Ba, L Li, P Gong, J Ou. 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
https://doi.org/10.1016/j.sna.2012.05.039
33 J Chen, Y Song, F Xiong, T Ai. A thermal effects-based method for void detection in concrete face rockfill dams. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1001307
https://doi.org/10.1109/TIM.2021.3127638
34 J Chen, Y Luo, J Xiong, S J Zhang, M Y Xia, H J Yang, Q Ge. A thermal-effect-based monitoring method for debris flow warning. Geomorphology, 2022, 400: 108097
https://doi.org/10.1016/j.geomorph.2021.108097
35 J Chen, F Xiong, Y Zhu, H Yan. A crack detection method for underwater concrete structures using sensing-heating system with porous casing. Measurement, 2021, 168: 108332
https://doi.org/10.1016/j.measurement.2020.108332
36 Y Zhu, J Chen, Y Zhang, F Xiong, F He, X Fang. Temperature tracer method for crack detection in underwater concrete structures. Structural Control and Health Monitoring, 2020, 27(9): e2595
https://doi.org/10.1002/stc.2595
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