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