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水力发电学报 ›› 2019, Vol. 38 ›› Issue (6): 29-40.doi: 10.11660/slfdxb.20190604

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碾压混凝土坝仓面压实质量5D可视化馈控研究

  

  • 出版日期:2019-06-25 发布日期:2019-06-25

5D visual feedback and control of compaction quality of working units of RCC dam

  • Online:2019-06-25 Published:2019-06-25

摘要: 为满足碾压混凝土坝施工质量精细化控制要求,研发了碾压混凝土坝仓面压实质量5D(三维位置、时间和质量参数等5维)可视化馈控系统。首先,提出基于AutoCAD/OpenGL的施工期3D快速建模方法,方便施工一线技术人员构建坝体施工单元3D信息模型,可精确表现随仓面施工进度推移而叠加演变的坝体形态,并有效显示施工阶段坝体实体信息;其次,基于仓面实时获取的碾压工艺参数,建立了基于BP-ANN的碾压层压实度计算模型,给出碾压混凝土坝压实质量全仓面智能评价方法,远程3D模型上可实时显示碾压层施工质量数字可视化云图,并形成Web在线碾压质量报告,实时指导施工人员针对欠碾区域及时补碾修复和再评价,实现了智慧施工。研发系统应用于工程实际,验证了其可靠实用性。

关键词: 碾压混凝土坝, 施工质量控制, 压实度, 3D建模, 5D可视化, 智慧施工

Abstract: A five-dimensional (5D, i.e. 3D space plus time and quality parameters) dynamic visual feedback-control system is developed for the compaction quality of roller compacted concrete (RCC) dam working units to meet the requirement for accurate control. First, we develop an accelerated, AutoCAD/OpenGL-based 3D modeling method of dam construction stage for frontline technicians to build 3D information models of dam working units. This method can accurately visualize the dam shape evolved as the construction of a working unit progresses, and display effectively the information of dam body under construction. Then, using real-time compaction parameters collected from concrete placing layers, we construct a compaction degree calculation model of rolling-compaction hot layers based on a back propagation-artificial neural network (BP-ANN), and formulate an intelligent method for evaluating the compaction quality of the entire working area. Thus, digital visualization cloud maps of compaction quality can be displayed in real time on a remote 3D model of compaction layers, and a Web-based online compaction quality report can be generated, guiding the construction workers to remedy those areas insufficiently compacted or conduct a real-time re-evaluation and thereby achieving smart construction. Finally, the applicability and reliability of our system are verified through a construction project.

Key words: RCC dam, construction quality control, compaction degree, 3D modeling, 5D visualization, smart construction

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