High-speed visualization of time-varying data in large-scale structural dynamic analyses with a GPU

• High-speed visualizations for large-scale structural dynamic analyses are achieved.
• A key frame extraction algorithm specific to the GPU-based rendering is proposed.
• A GPU-based frame interpolation algorithm is proposed for a complete visualization.
• Two analyses of a bridge and a high-rise building are visualized efficiently.
• Effects of physical activity on BMD were partially attenuated by lean mass.

Large-scale structural dynamic analyses generally produce massive amount of time-varying data. Inefficient rendering of these data seriously affects the quality of display of and user interaction with the analysis results. A high-speed visualization solution using a GPU (graphics processing unit) is thus developed in this study. Based on the clustering concept, a key frame extraction algorithm specific to the GPU-based rendering is proposed, which significantly reduces the data size to satisfy the GPU memory requirement. Using the key frames, a GPU-based parallel frame interpolation algorithm is also proposed to reconstruct the complete structural dynamic process. Particularly, a novel data access model considering the features of time-varying data and GPU memory is designed to improve the interpolation efficiency. Two case studies including an arch bridge and a high-rise building are presented, confirming the ability of the proposed solution to provide a high-speed and interactive visualization environment for large-scale structural dynamic analyses.