No abstract

Structural health monitoring (SHM) is essential when detecting damage in large and complex structures in order to provide a comprehensive assessment of the structural health state. Optimal sensor placement (OSP) is critical in the structural health monitoring system, which aims to use a limited number of sensors to obtain high-quality structural health diagnosis data. However, the current research mainly focuses on OSP for structures, without considering the values contributed by different modes to the bridge structure. In this article, an optimal sensor placement method based on initial sensor layout, using the dynamic adjustment of attenuation factor gravitational search algorithm (DGSA), is proposed. The effective modal mass participation ratio is introduced to ensure the validity of the initial data of optimal sensor placement. In view of the insufficient developmental ability of the gravitational search algorithm, the attenuation factor α adjusted dynamically aids the global search in the early iteration and the local fine search in the late iteration. The double coding method is used to apply the DGSA algorithm to OSP; taking cable-stayed bridges as an example, the feasibility of the algorithm is verified. The results show that the improved algorithm has a good optimization ability and can accurately and efficiently determine the optimal placement of sensors.

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View path design is used to generate proper animations for time-varying volume datasets, and it is crucial to show the evolution of features in the animation. In this paper, we present a novel view path design method to display the evolution of features with their topology. Firstly, feature extraction and tracking methods are employed to capture the temporal feature evolution. Then, the viewpoint quality is estimated by combining the visual information based on the viewpoint mutual information with the topology information based on the skeletons of features. Temporal viewpoint coherence is further proposed to partition the time range, and the volume datasets in each time segment share a fixed viewpoint. At last, the viewpoints in adjacent time segments are linked with a smooth view path, by means of which the user is able to explore the complex feature evolution in the time-varying volume dataset. Experimental results demonstrate the utility of the proposed topology aware view path design method.