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Journal of Tsinghua University(Science and Technology)    2024, Vol. 64 Issue (4) : 601-611     DOI: 10.16511/j.cnki.qhdxxb.2023.26.055
HYDRAULIC ENGINEERING |
Intelligent pipe-cooling control method and system for anchorage mass concrete
AN Ruinan1,3, LIN Peng1, CHEN Daoxiang1, AN Bang2, GAO Yangyang4
1. Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China;
2. Road & Bridge International Co., Ltd., Beijing 100027, China;
3. Rocket Force Academy, Beijing 100011, China;
4. China Three Gorges Construction Engineering Corporation Co., Ltd., Chengdu 610095, China
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Abstract  [Objective] Anchors are typical mass concrete structures found in large bridges, characterized by large structural sizes, complex boundary conditions with irregular shapes, low reinforcement ratios, high crack resistance requirements, and challenges in temperature control and crack prevention. The development of an adaptive, intelligent cooling control method and system is crucial for crack prevention and improving concrete pouring quality. [Methods] This paper proposes an intelligent cooling control method for bridge anchorage, including: (1) The basic control principles of heat balance of supply and use, accurate control, and online warning. (2) A fundamental intelligent control strategy involving real thermal field simulation and a temperature-flow coupling control algorithm. The combined influence of temperature and flow is considered when predicting the cooling system parameters. This study uses a hybrid approach involving a long short-term memory neural network (LSTM) and proportional integral derivative (PID) control algorithms to predict the future water flow rate based on the current concrete and cooling system state parameters, facilitating the temperature-to-flow mapping. (3) A “multiple terminal-edge computing-cloud storage” control model is implemented, which incorporates edge computing within the control cabinet, providing localized endpoint services to improve data transmission performance, ensure real-time processing, and reduce latency. Cloud computing uses machine learning to provide instructions for adjusting temperature and flow rates based on the deviations between the actual and target temperature control curves. Furthermore, fault recognition and rapid diagnosis functions are also implemented. Intelligent cooling control equipments and code platforms are developed for realizing online perception, real analysis, feedback control, remote diagnostics, and early warning systems for the cooling process. The system comprises water supply, reversing, control and heat exchange subsystems, and a multiterminal software platform based on WeChat and the web. [Results] This paper adopted simulation, equipment development, and field application methods based on the Longmen Bridge project. Real temperature field simulation calculations were conducted, the temperature distribution during the cooling process was analyzed, and the impact of heat transfer from the upper layer of concrete, as well as the design of cooling pipes, was optimized. Parameters such as water temperature, water flow, concrete temperature, and temperature gradient were analyzed. Furthermore, as part of a long-term temperature monitoring process, the impact of heat transfer from the upper layer of concrete was assessed to reduce the temperature difference between layers. A personalized water-cooling strategy was proposed, and the timing of the water supply was adjusted. [Conclusions] The established temperature-flow coupling control algorithm, model, equipment, and platform achieve real-time monitoring, analysis, control, continuous optimization, and early warning of water-cooling information online and remotely. The study results are successfully applied to the west anchorage of the Longmen Bridge. No temperature cracks are observed on the bridge site, which reduce manpower and water consumption. The results can be used as a design and construction reference for thermal cracking control in similar projects.
Keywords anchorage mass concrete      thermal cracking control      intelligent pipe-cooling      temperature control strategy      real thermal field     
Issue Date: 27 March 2024
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AN Ruinan
LIN Peng
CHEN Daoxiang
AN Bang
GAO Yangyang
Cite this article:   
AN Ruinan,LIN Peng,CHEN Daoxiang, et al. Intelligent pipe-cooling control method and system for anchorage mass concrete[J]. Journal of Tsinghua University(Science and Technology), 2024, 64(4): 601-611.
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http://jst.tsinghuajournals.com/EN/10.16511/j.cnki.qhdxxb.2023.26.055     OR     http://jst.tsinghuajournals.com/EN/Y2024/V64/I4/601
  
  
  
  
  
  
  
  
  
[1] 美国内务部垦务局. 混凝土坝的冷却[M]. 侯建功, 译. 北京: 中国水利电力出版社, 1958. Bureau of Reclamation of United States Department of Interior. Cooling of concrete dams[M]. HOU J G, trans. Beijing: China Water Power Press, 1958. (in Chinese)
[2] 林鹏, 李庆斌, 周绍武, 等. 大体积混凝土通水冷却智能温度控制方法与系统[J]. 水利学报, 2013, 44(8): 950-957. LIN P, LI Q B, ZHOU S W, et al. Intelligent cooling control method and system for mass concrete[J]. Journal of Hydraulic Engineering, 2013, 44(8): 950-957. (in Chinese)
[3] GAJDA J, VANGEEM M. Controlling temperatures in mass concrete[J]. Concrete International, 2002, 24(1): 58-62.
[4] NING Z Y, LIN P, OUYANG J S, et al. Intelligent cooling control for mass concrete relating to spiral case structure[J]. Advances in Concrete Construction, 2022, 14(1): 57-70.
[5] LIN P, LI Q B, HU H. A flexible network structure for temperature monitoring of a super high arch dam[J]. International Journal of Distributed Sensor Networks, 2012, 8(11): 917849.
[6] LI M, LIN P, CHEN D X, et al. An ANN-based short-term temperature forecast model for mass concrete cooling control[J]. Tsinghua Science and Technology, 2023, 28(3): 511-524.
[7] 刘江, 刘永健, 白永新, 等. 混凝土箱梁温度梯度模式的地域差异性及分区研究[J]. 中国公路学报, 2020, 33(3): 73-84. LIU J, LIU Y J, BAI Y X, et al. Regional variation and zoning of temperature gradient pattern of concrete box girder[J]. China Journal of Highway and Transport, 2020, 33(3): 73-84. (in Chinese)
[8] 王琼, 陈昌哲, 胡志坚, 等. 梁式承台大体积混凝土水化热温度场工程实测与数值仿真[J]. 混凝土, 2020(9): 139-143, 147. WANG Q, CHEN C Z, HU Z J, et al. Engineering measurement and numerical simulation of hydration heat temperature field of mass concrete for beam bearing platform[J]. Concrete, 2020(9): 139-143, 147. (in Chinese)
[9] 陈峰, 韩林, 涂晴云, 等. 明挖下沉式高速公路隧道混凝土施工仿真与温控方案研究[J]. 混凝土, 2022(10): 184-187. CHEN F, HAN L, TU Q Y, et al. Research on construction simulation and temperature control scheme of open cut and sinking expressway tunnel concrete[J]. Concrete, 2022(10): 184-187. (in Chinese)
[10] SONG C J, ZHANG G, WEN H, et al. Effect of pipe-cooling system on thermal-mechanical behaviour of PC box bridge girders at hydration age[J]. Advances in Bridge Engineering, 2020, 1: 9(2020).
[11] SARGAM Y, FAYTAROUNI M, RIDING K, et al. Predicting thermal performance of a mass concrete foundation: A field monitoring case study[J]. Case Studies in Construction Materials, 2019, 11: e00289.
[12] LIU D W, ZHANG W M, TANG Y, et al. Prediction of hydration heat of mass concrete based on the SVR model[J]. IEEE Access, 2021, 9: 62935-62945.
[13] YU X Z, CHEN J Y, XU Q, et al. Research on the influence factors of thermal cracking in mass concrete by model experiments[J]. KSCE Journal of Civil Engineering, 2018, 22(8): 2906-2915.
[14] YANG J, HU Y, ZUO Z, et al. Thermal analysis of mass concrete embedded with double-layer staggered heterogeneous cooling water pipes[J]. Applied Thermal Engineering, 2012, 35: 145-156.
[15] 郑华凯, 刘钊, 唐俊. 悬索桥承台大体积混凝土温控及抗裂技术应用[J]. 施工技术(中英文), 2022, 51(2): 58-61. ZHENG H K, LIU Z, TANG J. Application of temperature control and crack resistance technology of mass concrete for suspension bridge foundation slab[J]. Construction Technology, 2022, 51(2): 58-61. (in Chinese)
[16] 乔明. 某特大桥承台大体积混凝土施工温控关键技术研究及应用[J]. 公路工程, 2019, 44(5): 135-141. QIAO M. Research and application of key technologies of temperature control in mass concrete construction of pile cap of a super-large bridge[J]. Highway Engineering, 2019, 44(5): 135-141. (in Chinese)
[17] 张庆龙, 马睿, 胡昱, 等. 大体积混凝土结构温度应力智能控制理论[J]. 水力发电学报, 2021, 40(5): 11-21. ZHANG Q L, MA R, HU Y, et al. Intelligent control theory of thermal stress in mass concrete structures[J]. Journal of Hydroelectric Engineering, 2021, 40(5): 11-21. (in Chinese)
[18] 刘晓东, 任永苹, 朱晶, 等. 智能温控系统对大体积混凝土施工的影响[J]. 混凝土, 2022(6): 179-184. LIU X D, REN Y P, ZHU J, et al. Influence of intelligent temperature control system on mass concrete construction[J]. Concrete, 2022(6): 179-184. (in Chinese)
[19] 杜小凯, 孙保平, 张国新, 等. 大体积混凝土防裂动态智能温控系统应用与监测分析[J]. 水力发电, 2015, 41(1): 46-49. DU X K, SUN B P, ZHANG G X, et al. Functions and application of dynamic intelligent temperature control system of mass concrete anti-cracking[J]. Water Power, 2015, 41(1): 46-49. (in Chinese)
[20] 林鹏, 李明, 刘科, 等. 低热水泥碾压混凝土坝适应性智能通水策略研究[J]. 水利学报, 2022, 53(9): 1028-1038. LIN P, LI M, LIU K, et al. Study on adaptive intelligent cooling strategy for low-heat cement RCC[J]. Journal of Hydraulic Engineering, 2022, 53(9): 1028-1038. (in Chinese)
[21] 林鹏, 樊启祥, 汪志林, 等. 一种介质换热智能控制系统及方法: 110006284B[P]. 2020-05-15. LIN P, FAN Q X, WANG Z L, et al. An intelligent cooling control system and method for medium heat transfer: 110006284B[P]. 2020-05-15. (in Chinese)
[22] 朱伯芳. 大体积混凝土温度应力与温度控制[M]. 2版. 北京: 中国水利水电出版社, 2012. ZHU B F. Thermal stress and temperature control of mass concrete[M]. 2nd ed. Beijing: China Water Power Press, 2012. (in Chinese)
[23] KIM J K, KIM K H, YANG J K. Thermal analysis of hydration heat in concrete structures with pipe-cooling system[J]. Computers & Structures, 2001, 79(2): 163-171.
[24] 程井, 孔垂穗, 邹科辉. 基于LSTM的泵闸工程混凝土施工期温度场预测[J]. 水利水电科技进展, 2023, 43(2): 76-81. CHENG J, KONG C S, ZOU K H. Temperature field prediction during concrete construction period of pump and sluice project based on LSTM[J]. Advances in Science and Technology of Water Resources, 2023, 43(2): 76-81. (in Chinese)
[25] 樊启祥, 林鹏, 魏鹏程, 等. 智能建造闭环控制理论[J]. 清华大学学报(自然科学版), 2021, 61(7): 660-670. FAN Q X, LIN P, WEI P C, et al. Closed-loop control theory of intelligent construction[J]. Journal of Tsinghua University (Science and Technology), 2021, 61(7): 660-670. (in Chinese)
[26] 安瑞楠, 林鹏, 陈道想, 等. 超大混凝土结构温度梯度监测与温度场演化[J]. 清华大学学报(自然科学版), 2023, 63(7): 1050-1059. AN R N, LIN P, CHEN D X, et al. Temperature gradient monitoring and thermal evolution of a super mass concrete structure[J]. Journal of Tsinghua University (Science and Technology), 2023, 63(7): 1050-1059. (in Chinese)
[1] AN Ruinan, LIN Peng, CHEN Daoxiang, AN Bang, LU Guannan, LIN Zhitao. Temperature gradient monitoring and thermal evolution of a super mass concrete structure[J]. Journal of Tsinghua University(Science and Technology), 2023, 63(7): 1050-1059.
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