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清华大学学报(自然科学版)  2024, Vol. 64 Issue (4): 601-611    DOI: 10.16511/j.cnki.qhdxxb.2023.26.055
  水利水电工程 本期目录 | 过刊浏览 | 高级检索 |
锚碇大体积混凝土智能通水温控方法与系统
安瑞楠1,3, 林鹏1, 陈道想1, 安邦2, 高阳阳4
1. 清华大学 水利水电工程系, 北京 100084;
2. 中交路桥建设有限公司, 北京 100027;
3. 火箭军研究院, 北京 100011;
4. 中国三峡建工(集团)有限公司, 成都 610095
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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摘要 大型桥梁锚碇的锚块部分属于典型大体积混凝土结构, 施工期面临开裂风险, 开展适应性智能通水温控方法与系统研究对混凝土温控防裂及提高混凝土质量具有重要意义。 该文提出了锚碇真实温度场及应力计算、 温流耦合控制算法, 以及基于“端-边-云”控制模型的智能温度控制策略; 研发了适应锚碇的智能通水温控系统装备和平台, 包括供水、 换向、 控制和热交换系统, 以及基于微信移动、 Web客户端的多端软件平台, 实现了混凝土通水温控的远程实时在线感知、 真实分析、 反馈控制和诊断预警。 研究成果应用于龙门大桥西锚碇施工建设的全过程, 节约了人力和用水, 且未发现温度裂缝, 成果可供同类工程温控防裂设计和施工参考。
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安瑞楠
林鹏
陈道想
安邦
高阳阳
关键词 锚碇大体积混凝土温控防裂智能通水温控策略真实温度场    
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.
Key wordsanchorage mass concrete    thermal cracking control    intelligent pipe-cooling    temperature control strategy    real thermal field
收稿日期: 2023-05-27      出版日期: 2024-03-27
基金资助:中交路桥建设有限公司资助项目(LJLJHN-PJ2020005219-062622141); 中国三峡建工(集团)有限公司技术服务/咨询项目(WDD/0578); 中国水电赞比亚下凯富峡水电站资助项目(SH-KGL-SUB-2021003)
通讯作者: 林鹏,教授,E-mail:celinpe@tsinghua.edu.cn     E-mail: celinpe@tsinghua.edu.cn
作者简介: 安瑞楠(1988—),女,博士研究生。
引用本文:   
安瑞楠, 林鹏, 陈道想, 安邦, 高阳阳. 锚碇大体积混凝土智能通水温控方法与系统[J]. 清华大学学报(自然科学版), 2024, 64(4): 601-611.
AN Ruinan, LIN Peng, CHEN Daoxiang, AN Bang, GAO Yangyang. Intelligent pipe-cooling control method and system for anchorage mass concrete. Journal of Tsinghua University(Science and Technology), 2024, 64(4): 601-611.
链接本文:  
http://jst.tsinghuajournals.com/CN/10.16511/j.cnki.qhdxxb.2023.26.055  或          http://jst.tsinghuajournals.com/CN/Y2024/V64/I4/601
  
  
  
  
  
  
  
  
  
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