journal article·2023-03-10Open access

A Review of Detection Technologies for Underwater Cracks on Concrete Dam Surfaces

Dong Chen, Ben Huang, Fei Kang

Applied Sciences· Volume 13· Issue 6

Abstract

Cracks seriously endanger the safe and stable operation of dams. It is important to detect surface cracks in a timely and accurate manner to ensure the safety and serviceability of a dam. The above-water crack detection technology of dams has been widely studied, but due to the complex underwater environment, above-water crack detection technology on dam surfaces cannot be directly applied to underwater crack detection. To adapt to the underwater detection environment and improve the efficiency and accuracy of underwater crack detection, many methods have been proposed for underwater crack detection, including sensor detection and image detection. This paper presents a systematic overview of the development and application practices of existing underwater crack detection technologies for concrete dams, focusing on methods that use underwater robots as underwater mobile carriers to acquire images that are combined with digital image processing algorithms to identify, locate, and quantify underwater cracks in dams. This method has been widely used for underwater crack detection on dam surfaces with the advantages of being non-contact, non-destructive, having high efficiency, and wide applicability. Finally, this paper looks further forward to the development trends and research challenges of detection technologies for underwater cracks on concrete dam surfaces, which will help researchers to complete further studies on underwater crack detection.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Infrastructure Maintenance and Monitoring · Dam Engineering and Safety · Hydrology and Sediment Transport Processes

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Hydraulic Engineering, Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian 116024, China

Funding

National Key R & D Program of China · National Natural Science Foundation of China · Fundamental Research Funds for Central Universities

References (85)

Show source-provided references
1. Balancing Social, Economic and Ecological Benefits of Reservoir Operation during the Flood Season: A Case Study of the Three Gorges Project, China10.1016/j.jhydrol.2019.03.009 ↗
2. Technical Progress on Researches for the Safety of High Concrete-Faced Rockfill Dams10.1016/J.ENG.2016.03.010 ↗
3. A Technical Review of Hydro-Project Development in China10.1016/J.ENG.2016.03.008 ↗
4. Fuzzy Hierarchy Comprehensive Evaluation on Dam-Break Risk Analysis10.4028/www.scientific.net/AMR.550-553.2151 ↗
5. An Approach Using Dempster-Shafer Evidence Theory to Fuse Multi-Source Observations for Dam Safety Estimation10.1007/s00500-018-3220-z ↗
6. Review on Hidden Trouble Detection and Health Diagnosis of Hydraulic Concrete Structures10.1007/s11431-007-6003-9 ↗
7. Horizontal Cracking and Crack Repair Analysis of a Super High Arch Dam Based on Fracture Toughness10.1016/j.engfailanal.2019.01.036 ↗
8. Hydraulic Fracture at the Dam-Foundation Interface Using the Scaled Boundary Finite Element Method Coupled with the Cohesive Crack Model10.1016/j.enganabound.2017.11.009 ↗
9. Snicker, R., Eisenbach, M., Sesselmann, M., Debes, K., and Gross, H.M. (2019, January 14–19). Improving Visual Road Condition Assessment by Extensive Experiments on the Extended GAPs Dataset. Proceedings of the 2019 International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary.10.1109/IJCNN.2019.8852257 ↗
10. Global Sensitivity Analysis of the Static Performance of Concrete Gravity Dam from the Viewpoint of Structural Health Monitoring10.1007/s11831-020-09434-0 ↗
11. Acoustic and Ultrasonic Techniques for Defect Detection and Condition Monitoring in Water and Sewerage Pipes: A Review10.1016/j.apacoust.2021.108282 ↗
12. Coleman, Z.W., and Schindler, A.K. (2022). Investigation of Ground-Penetrating Radar, Impact Echo, and Infrared Thermography Methods to Detect Defects in Concrete Bridge Decks. Transp. Res. Rec., Online First.10.1177/03611981221101027 ↗
13. Evolutionary Computation Modelling for Structural Health Monitoring of Critical Infrastructure
14. Monitoring and evaluation of the repair quality of concrete cracks using piezoelectric smart aggregates10.1016/j.conbuildmat.2021.125775 ↗
15. Real-Time Crack Assessment Using Deep Neural Networks with Wall-Climbing Unmanned Aerial System10.1111/mice.12519 ↗
16. Structural Health Monitoring and Inspection of Dams Based on UAV Photogrammetry with Image 3D Reconstruction10.1016/j.autcon.2021.103832 ↗
17. Image-Based Automatic Multiple-Damage Detection of Concrete Dams Using Region-Based Convolutional Neural Networks
18. Wang, L.L., Li, J.J., and Kang, F. (2022). Crack Location and Degree Detection Method Based on YOLOX Model. Appl. Sci., 12.10.3390/app122412572 ↗
19. Comprehensive Evaluation Methods for Dam Service Status10.1007/s11431-012-4907-5 ↗
20. Mucolli, L., Krupinski, S., Maurelli, F., Mehdi, S.A., and Mazhar, S. (2019, January 27–31). Detecting Cracks in Underwater Concrete Structures: An Unsupervised Learning Approach Based on Local Feature Clustering. Proceedings of the OCEANS 2019 MTS/IEEE SEATTLE, Seattle, WA, USA.10.23919/OCEANS40490.2019.8962401 ↗
21. Alexander, M.G.B.T.-M.C.S. (2016). 5—Deterioration of Concrete in the Marine Environment. In, Woodhead Publishing.
22. Durapgulf, a Local Service Life Model for the Durability of Concrete Structures in the South of Iran
23. Monitoring Depth and Width of Cracks in Underwater Concrete Structures Using Embedded Smart Aggregates10.1016/j.measurement.2022.112078 ↗
24. Liang, J.C., Chen, B., Shao, C.F., Li, J.M., and Wu, B.B. (2020). Time Reverse Modeling of Damage Detection in Underwater Concrete Beams Using Piezoelectric Intelligent Modules. Sensors, 20.10.3390/s20247318 ↗
25. A Detection and Classification Approach for Underwater Dam Cracks. Struct10.1177/1475921716651039 ↗
26. Detecting Large-Scale Underwater Cracks Based on Remote Operated Vehicle and Graph Convolutional Neural Network10.1007/s11709-022-0855-8 ↗
27. Machine vision-based surface crack analysis for transportation infrastructure10.1016/j.autcon.2021.103973 ↗
28. Crack detection and characterization techniques—An overview10.1002/stc.1655 ↗
29. Image Based Techniques for Crack Detection, Classification and Quantification in Asphalt Pavement: A Review10.1007/s11831-016-9194-z ↗
30. Crack detection using image processing: A critical review and analysis10.1016/j.aej.2017.01.020 ↗
31. Machine Learning for Crack Detection: Review and Model Performance Comparison10.1061/(ASCE)CP.1943-5487.0000918 ↗
32. Ma, Y.P., Wu, Y., Li, Q.W., Zhou, Y.Q., and Yu, D.B. (2022). ROV-Based Binocular Vision System for Underwater Structure Crack Detection and Width Measurement. Multimed. Tools Appl., 1–25.10.1007/s11042-022-14168-1 ↗
33. Case Study: Application of GPR to Detection of Hidden Dangers to Underwater Hydraulic Structures10.1061/(ASCE)0733-9429(2006)132:1(12) ↗
34. Continuous Strain Monitoring in the Rock Foundation of a Large Gravity Dam10.1007/BF01033277 ↗
35. Research Progresses on Equipment Technologies Used in Safety Inspection, Repair, and Reinforcement for Deepwater Dams10.1007/s11431-021-1958-y ↗
36. Efficient Identification of Water Conveyance Tunnels Siltation Based on Ensemble Deep Learning10.1007/s11709-022-0829-x ↗
37. Underwater Crack Pixel-Wise Identification and Quantification for Dams via Lightweight Semantic Segmentation and Transfer Learning10.1016/j.autcon.2022.104600 ↗
38. Design of Motion Control of Dam Safety Inspection Underwater Vehicle10.1007/s11771-012-1171-6 ↗
39. Depth Detection of Subsurface Voids in Concrete-Filled Steel Tubular (CFST) Structure Using Percussion and Decision Tree10.1016/j.measurement.2020.107869 ↗
40. Experimental Evaluation of Crack Effects on the Dynamic Characteristics of a Prototype Arch Dam Using Ambient Vibration Tests10.12989/cac.2012.10.3.277 ↗
41. A summary review and advantages of vibration-based damage identification methods in structural health monitoring
42. Abbas, M., and Shafiee, M. (2018). Structural Health Monitoring (SHM) and Determination of Surface Defects in Large Metallic Structures Using Ultrasonic Guided Waves. Sensors, 18.10.3390/s18113958 ↗
43. NDE Application of Ultrasonic Tomography to a Full-Scale Concrete Structure10.1109/TUFFC.2014.006962 ↗
44. Venkatesh, V., Kodoth, K., Jacob, A.A., Upadhyay, V., Ravichandran, S., Rajagopal, P., and Balasubramaniam, K. (2022, January 21–24). Assessment of Structural Integrity of Submerged Concrete Structures Using Quantitative Non-Destructive Techniques Deployed from Remotely Operated Underwater Vehicles (ROV). Proceedings of the OCEANS 2022—Chennai, Chennai, India.10.1109/OCEANSChennai45887.2022.9775418 ↗
45. Feasibility Study of Percussion-Acoustic-Based Void Identification for Underwater Concrete10.1002/stc.3101 ↗
46. A Fine Extraction Algorithm for Image-Based Surface Cracks in Underwater Dams10.1088/1361-6501/ac9db2 ↗
47. A Crack Detection Method for Underwater Concrete Structures Using Sensing-Heating System with Porous Casing10.1016/j.measurement.2020.108332 ↗
48. Temperature Tracer Method for Crack Detection in Underwater Concrete Structures10.1002/stc.2595 ↗
49. Crack Width Identification for Underwater Concrete Structures Using Temperature Tracer Method10.1088/1361-6501/ac20b5 ↗
50. Encoder-Decoder Network for Pixel-Level Road Crack Detection in Black-Box Images10.1111/mice.12440 ↗
51. A UAV-Based Machine Vision Method for Bridge Crack Recognition and Width Quantification through Hybrid Feature Learning10.1016/j.conbuildmat.2021.123896 ↗
52. Zhang, Z.G., Bian, H.Y., and Song, Z.Q. (2016, January 9–11). A Multi-View Sonar Image Fusion Method Based on the Morphological Wavelet and Directional Filters. Proceedings of the 2016 IEEE/OES China Ocean Acoustics (COA), Harbin, China.10.1109/COA.2016.7535779 ↗
53. Shi, P.F., Fan, X.N., Ni, J.J., Khan, Z., and Li, M. (2017). A Novel Underwater Dam Crack Detection and Classification Approach Based on Sonar Images. PLoS ONE, 12.10.1371/journal.pone.0179627 ↗
54. Pang, Y.J., Cao, J., and Yang, Z.Y. (2020). Design Theory and Optimization Methods of Autonomous Underwater Vehicles, Science Press. (In Chinese).
55. Jiao, Y. (2021). Seventy Years of Chinese Dams, China Three Gorges Publishing House. (In Chinese).
56. Elvander, J., and Hawkes, G. (2012, January 14–19). ROVs and AUVs in Support of Marine Renewable Technologies. Proceedings of the 2012 Oceans, Hampton Roads, VA, USA.10.1109/OCEANS.2012.6405139 ↗
57. Application status and development trend of underwater robot in water and hydropower engineering detection
58. Liu, Y., Xue, J., Yang, B., Zhu, M., Guo, W., Pan, F., Ye, C., Wang, W., Liang, T., and Li, X. (2021). The Acoustic System of the Fendouzhe HOV. Sensors, 21.10.3390/s21227478 ↗
59. Lan, Y., Hu, Z., Shen, D., Wang, L., and Yang, S.S. (2020, January 6–8). Design and Implementation of DMS Control System for Dam Defects Inspection and Identification. Proceedings of the 2020 Chinese Automation Congress (CAC), Shanghai, China.10.1109/CAC51589.2020.9326628 ↗
60. Visual Inspection of Hydroelectric Dams Using an Autonomous Underwater Vehicle10.1002/rob.20351 ↗
61. Autonomous underwater vehicle to inspect hydroelectric dams
62. Cruz, N.A., Matos, A.C., Almeida, R.M., Ferreira, B.M., and Abreu, N. (2011, January 19–22). TriMARES—A Hybrid AUV/ROV for Dam Inspection. Proceedings of the OCEANS’11 MTS/IEEE KONA, Waikoloa, HI, USA.10.23919/OCEANS.2011.6107314 ↗
63. Development of a stable localized visual inspection system for underwater structures10.1080/01691864.2016.1218794 ↗
64. Shimono, S., Toyama, S., and Nishizawa, U. (2016, January 19–23). Development of Underwater Inspection System for Dam Inspection: Results of Field Tests. Proceedings of the OCEANS 2016 MTS/IEEE Monterey, Monterey, CA, USA.10.1109/OCEANS.2016.7761224 ↗
65. Yu, C.Y., Xiang, X.B., Zhang, J.L., Zhao, R., and Zhou, C.K. (2017, January 18–21). Complete Coverage Tracking and Inspection for Sloping Dam Wall by Remotely Operated Vehicles. Proceedings of the OCEANS 2017—Anchorage, Anchorage, AK, USA.
66. Underwater robotic system for reservoir maintenance10.21595/jve.2016.17364 ↗
67. Development of Dam Inspection Underwater Robot10.2991/jrnal.k.190531.004 ↗
68. Detect Concrete Cracks Based on OTSU Algorithm with Differential Image10.1049/joe.2018.9191 ↗
69. Fan, X.N., and Shi, P.F. (2019). Underwater Optical Image Enhancement and Restoration Methods and Applications, Beijing Science Press.
70. Automatic Road Crack Detection and Characterization10.1109/TITS.2012.2208630 ↗
71. A Novel Automatic Dam Crack Detection Algorithm Based on Local-Global Clustering10.1007/s11042-018-5880-1 ↗
72. An Underwater Dam Crack Image Segmentation Method Based on Multi-Level Adversarial Transfer Learning10.1016/j.neucom.2022.07.036 ↗
73. Pixel-Level Bridge Crack Detection Using a Deep Fusion about Recurrent Residual Convolution and Context Encoder Network10.1016/j.measurement.2021.109171 ↗
74. An Edge Detection Method Based Artificial Bee Colony for Underwater Dam Crack Image
75. Underwater Dam Crack Image Enhancement Algorithm Based on Improved Dark Channel Prior
76. Bionic Binocular Vision Measurement of Surface Defects of Underwater Structures
77. Underwater Dam Image Crack Segmentation Based on Mathematical Morpholog10.4028/www.scientific.net/AMM.220-223.1315 ↗
78. Chen, C., Wang, J., Zou, L., Fu, J., and Ma, C. (2012, January 19–20). A Novel Crack Detection Algorithm of Underwater Dam Image. Proceedings of the 2012 International Conference on Systems and Informatics (ICSAI2012), Yantai, China.10.1109/ICSAI.2012.6223399 ↗
79. An Improved Nondestructive Semantic Segmentation Method for Concrete Dam Surface Crack Images with High Resolution10.1155/2020/5054740 ↗
80. A Deep Residual Neural Network Framework with Transfer Learning for Concrete Dams Patch-Level Crack Classification and Weakly-Supervised Localization10.1016/j.measurement.2021.110641 ↗
81. A Survey on Transfer Learning10.1109/TKDE.2009.191 ↗
82. A method of underwater bridge structure damage detection method based on a lightweight deep convolutional network10.1049/ipr2.12602 ↗
83. Micro-Concrete Crack Detection of Underwater Structures Based on Convolutional Neural Network10.1007/s00138-022-01327-5 ↗
84. Chen, Z., Zhang, Z., Dai, F.Z., Bu, Y., and Wang, H.B. (2017). Monocular Vision-Based Underwater Object Detection. Sensors, 17.10.3390/s17081784 ↗
85. Multibeam Sonar (DIDSON) Assessment of American Shad (Alosa Sapidissima) Approaching a Hydroelectric Dam10.1139/cjfas-2013-0308 ↗