Abstract
China’s many dams are increasingly facing safety problems resulting from the natural aging of the dam hydraulic structure and geological disasters such as earthquakes under the operating condition of holding a vast amount of water. Problems such as dam leakage and concrete cracking and erosion seriously affect the normal operation and effectiveness of a reservior. It is impossible to empty a large reservoir with a high dam for many reasons, such as serious environmental consequences, and it is thus difficult to detect the working conditions of the underwater part of the facility. There is an increasingly urgent demand for detection and repair technologies that can be used in the deepwater environment. Indeed, there are cutting-edge deepwater detection and processing technologies for hidden dangers in dam engineering. The core technologies include underwater detection equipment, diving technologies, and underwater operation technologies. This paper analyzes safety-oriented detection equipment recently used in unmanned detection, manned detection, and deepwater diver detection from the viewpoint of practical application. The paper further reviews 100-m-depth-class technologies and applications of safety inspection, repair, and reinforcement equiment in the deepwater environment of reservoir dams. An outlook is provided on technology trends of deepwater detection, repair, and reinforcement and critical problems.
Similar content being viewed by others
References
Ma H, Chi F. Technical progress on researches for the safety of high concrete-faced rockfill dams. Engineering, 2016, 2: 332–339
Jia J. A technical review of hydro-project development in China. Engineering, 2016, 2: 302–312
Wu Z R, Peng Y, Li Z C, et al. Commentary of research situation and innovation frontier in hydro-structure engineering science. Sci China Tech Sci, 2011, 54: 767–780
Wang J T, Jin F, Zhang C H. Seismic safety of arch dams with aging effects. Sci China Tech Sci, 2011, 54: 522–530
Wang S W, Gu C S, Bao T F. Observed displacement data-based identification method of deformation time-varying effect of high concrete dams. Sci China Tech Sci, 2018, 61: 906–915
Liu Z P, Guo X L, Zhou X B, et al. Cascading dam breach process simulation using a coupled modeling platform. Sci China Tech Sci, 2019, 62: 1455–1466
Zhong G, Peng X. Transport and accumulation of plastic litter in submarine canyons—The role of gravity flows. Geology, 2021, 49: 581–586
Yan C, Xie H, Yang D, et al. Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans Intell Transp Syst, 2018, 19: 284–295
Pang Y J, Cao J, Yang Z Y, et al. Design Theory and Optimization Methods of Autonomous Underwater Vehicles (in Chinese). Beijing: Science Press, 2020
Tan J X, Wang M X, Cai W. Reservoir Dam Underwater Reinforcement Technology (in Chinese). Wuhan: Yangtze River Press, 2015
Cai Y B. Scientific and technology report on dam deepwater detection, emergency monitoring, early warning, and emergency treatment of major water conservancy programs (in Chinese). Technical Report. Nanjing: Nanjing Hydraulic Research Institute, 2021
Global Engineering Frontier Project Team of Chinese Academy of Engineering. Frontiers of Global Engineering 2020 (in Chinese). Beijing: Higher Education Press, 2020
Yang B, Liu Y Y, Liao J W. Manned submersibles—Deep-sea scientific research and exploitation of marine resources (in Chinese). Bull Chin Acad Sci, 2021, 36: 622–631
Han M, Lyu Z, Qiu T, et al. A review on intelligence dehazing and color restoration for underwater images. IEEE Trans Syst Man Cybern Syst, 2020, 50: 1820–1832
Shen S, Yang H, Li J, et al. Auditory inspired convolutional neural networks for ship type classification with raw hydrophone Data. Entropy, 2018, 20: 990
Kildow J T, McIlgorm A. The importance of estimating the contribution of the oceans to national economies. Mar Policy, 2010, 34: 367–374
Cui W. Development of the Jiaolong deep manned submersible. Mar Technol Soc J, 2013, 47: 37–54
Wang L, Jiang L, Ma L B, et al. Key technologies of manned submersible applications for high dam reservoirs (in Chinese). China Water Transport (second half month), 2019, 19: 23–24, 116
Zhang J Y, Xiang Y. Analysis on the impact of climate change on the water conservancy project safety. Sci Sin Tech, 2018, 48: 1031–1039
Kelasidi E, Liljeback P, Pettersen K Y, et al. Innovation in underwater robots: Biologically inspired swimming snake robots. IEEE Robot Automat Mag, 2016, 23: 44–62
Petrioli C, Petroccia R, Potter J R, et al. The sunset framework for simulation, emulation and at-sea testing of underwater wireless sensor networks. Ad Hoc Networks, 2015, 34: 224–238
Harari I. A survey of finite element methods for time-harmonic acoustics. Comput Methods Appl Mech Eng, 2006, 195: 1594–1607
He C F, Wang Y Y, Chen C, et al. Underwater acoustic localization with uncertainties in propagation speed and time synchronization. In: OCEANS 2016 MTS/IEEE Monterey. Monterey: IEEE, 2016
Liu J, Wang Z, Cui J H, et al. A joint time synchronization and localization design for mobile underwater sensor networks. IEEE Trans Mobile Comput, 2016, 15: 530–543
Sun D, Zheng C, Cui H, et al. Developing status and some cutting-edge issues of underwater sensor network localization technology. Sci Sin Inf, 2018, 48: 1121–1136
Li S, Tang Y G, Huang Y, et al. Review and prospect for Chinese deep-sea technology and equipment (in Chinese). Bull Chin Acad Sci, 2016, 31: 1316–1325
Hardy K, Rosenthal B J. Special issue: Celebrating the golden anniversary of man’s deepest dive. Mar Technol Soc J, 2009, 43: 211–219
Kyo M, Hiyazaki E, Tsukioka S, et al. The Sea Trial of “KAIKO”, the full ocean depth research ROV. In: “Challenges of Our Changing Global Environment”. Conference Proceedings. OCEANS ‘95 MTS/IEEE. San Diego, 1995
Hashimoto K, Watanabe M, Tashiro S, et al. Missing of the ROV Kaiko vehicle-problem on the secondary cable. In: Oceans’04 MTS/IEEE Techno-Ocean’04. Kobe, 2004
Nakajoh H, Miyazaki T, Sawa T, et al. Development of 7000m Work Class ROV “KAIKO Mk-IV”. In: OCEANS 2016 MTS/IEEE Monterey. Monterey, 2016
Purcell M, Gallo D, Sherrell A, et al. Use of REMUS 6000 AUVs in the search for the Air France Flight 447. In: OCEANS’11 MTS/IEEE KONA. Waikoloa: IEEE, 2011
Yan M, Zhu D, Yang S X. A novel 3-D bio-inspired neural network model for the path planning of an AUV in underwater environments. Intell Autom Soft Co, 2013, 19: 555–566
Li J, Chen W, Fu Z, et al. Explicit empirical formula evaluating original intensity factors of singular boundary method for potential and helmholtz problems. Eng Anal Bound Elem, 2016, 73: 161–169
Li J, Fu Z, Chen W. Numerical investigation on the obliquely incident water wave passing through the submerged breakwater by singular boundary method. Comput Math Appl, 2016, 71: 381–390
Fu M Y, Zhang A H, Xu J L. Research on path control of cable laying vessels during laying and burying process (in Chinese). J Harbin Eng Univ, 2012, 33: 1254–1258
Pontbriand C, Farr N, Hansen J, et al. Wireless data harvesting using the AUV sentry and WHOI optical modem. In: Proceedings of OCEANS. Washington, 2015
Zhang H W, Hao L, Wang Y H, et al. The general design of a seaoor surveying AUV system. In: Proceedings of OCEANS. San Diego, 2014
Wang Y Q, Xu C H, Xu H X, et al. An integrated navigation algorithm for AUV based on Pseudo-range measurements and error estimation. In: 2016 IEEE International Conference on Robotics and Biomimetics (ROBIO). Qingdao: IEEE, 2016. 1625–1630
Chen S H. Issues and countermeasures of safety management of reservoir dams under new situation in China (in Chinese). China Water Resour, 2020, 22: 1–3
Gong X N, Jia J S, Zhang C S. Dam Hazard Assessment and Hazard Removal and Reinforcement Technologies (in Chinese). Beijing: China Architecture and Building Press, 2021
Jiao Y. Seventy Years of Chinese Dams (in Chinese). Beijing: China Three Gorges Publishing House, 2021
Gu L, Song Q, Yin H, et al. An overview of the underwater search and salvage process based on ROV. Sci Sin Inf, 2018, 48: 1137–1151
Wang R, Yang W, Li C, et al. Research progress of mooring buoy system for sea surface and seafloor observation. Chin Sci Bull, 2019, 64: 2963–2973
Tan J X. Technical report on dam deepwater leakage detection technologies and equipment (in Chinese). Technical Report. Wuhan: Yangtze River Survey, Planning, Design and Research Co., Ltd., 2021
Peng Z H, Wang G X. Airborne source localization in shallow water. AIP Conf Proc, 2012, 1495: 345–352
Li H S, Xu C, Zhou T. High-resolution integrated detection of underwater topography and geomorphology based on multibeam inter-ferometric echo sounder. Appl Mech Mater, 2012, 212–213: 345–350
Cai Y B. Technical report on submersible and reinforcement platform for dam deepwater detection and repair (in Chinese). Technical Report. Nanjing: Nanjing Hydraulic Research Institute, 2021
Liu Y Y. Real-time diagnosis of dam hazard in deepwater environment: The “Yulong” Manned Submersible and a Breakthrough from 0 to 1 (in Chinese). China Water Resour, 2020, 23: 70
Carroll P, Mahmood K, Zhou S, et al. On-demand asynchronous localization for underwater sensor networks. IEEE Trans Signal Process, 2014, 62: 3337–3348
Chandrasekhar V, Seah W. An area localization scheme for underwater sensor networks. In: Proceedings of OCEANS 2006, Singapore, 2006
Zhou S L, Giannakis G B. Finite-alphabet based channel estimation for OFDM and related multicarrier systems. IEEE Trans Commun, 2001, 49: 1402–1414
Sharma G, Kumar A. Dynamic range normal bisector localization algorithm for wireless sensor networks. Wireless Pers Commun, 2017, 97: 4529–4549
Hajihoseini G A, Shahbazian R, Ghorashi S A. Decentralized consensus based target localization in wireless sensor networks. Wireless Pers Commun, 2017, 97: 3587–3599
Grønning M, Aarli J A. Neurological effects of deep diving. J Neurol Sci, 2011, 304: 17–21
Łuczyński D, Lautridou J, Hjelde A, et al. Hemoglobin during and following a 4-week commercial saturation dive to 200 m. Front Physiol, 2019, 10: 1494
Rosén A, Oscarsson N, Kvarnström A, et al. Serum tau concentration after diving: An observational pilot study. Diving Hyperb Med, 2019, 49: 88–95
Author information
Authors and Affiliations
Corresponding author
Additional information
This work was supported by the National Natural Science Foundation of China (Grant No. 51979176), the Water Conservancy Technology Demonstration Program of the Ministry of Water Resources (Grant No. SF202108), and the Fundamental Research Fund Program of the National Research Institutes for Public Welfare (Grant No. Y720001).
Rights and permissions
About this article
Cite this article
Xiang, Y., Sheng, J., Wang, L. et al. Research progresses on equipment technologies used in safety inspection, repair, and reinforcement for deepwater dams. Sci. China Technol. Sci. 65, 1059–1071 (2022). https://doi.org/10.1007/s11431-021-1958-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11431-021-1958-y