Gravity retaining wall with geogrids has showed excellent seismic performance from Wenchuan great earthquake. However, seismic damage mechanism of this kind of wall is not sufficiently clear. In view of this, a large shaking table test of the gravity retaining wall with geogrids to reinforce the subgrade slope was carried out, and based on the Hilbert-Huang transform and the marginal spectrum theory, the energy identification method of the slope dynamic failure mode was studied. The results show that the geogrids can effectively reduce displacement and rotation of the retaining wall, and it can effectively absorb the energy of the ground movement when combined with the surrounding soil. In addition, it also reveals the failure development of the gravity retaining wall with geogrids to reinforce the subgrade slope. The damage started in the deep zone near the geogrids, and then gradually extended to the surface of the subgrade slope and other zones, finally formed a continuous failure surface along the geogrids. The analysis results of the failure mode identified by the Hilbert marginal spectrum are in good consistency with the experimental results, which prove that the Hilbert marginal spectrum can be applied to obtain the seismic damage mechanism of slope.
In the map of geo-referenced population and cases, the detection of the most likely cluster (MLC), which is made up of many connected polygons (e.g., the boundaries of census tracts), may face two difficulties. One is the irregularity of the shape of the cluster and the other is the heterogeneity of the cluster. A heterogeneous cluster is referred to as the cluster containing depression links (a polygon is a depression link if it satisfies two conditions: (1) the ratio between the case number and the population in the polygon is below the average ratio of the whole map; (2) the removal of the polygon will disconnect the cluster). Previous studies have successfully solved the problem of detecting arbitrarily shaped clusters not containing depression links. However, for a heterogeneous cluster, existing methods may generate mistakes, for example, missing some parts of the cluster. In this article, a spatial scanning method based on the ant colony optimization (AntScan) is proposed to improve the detection power. If a polygon can be simplified as a node, the research area consisting of many polygons then can be seen as a graph. So the detection of the MLC can be seen as the search of the best subgraph (with the largest likelihood value) in the graph. The comparison between AntScan, GAScan (the spatial scan method based on the genetic optimization), and SAScan (the spatial scan method based on the simulated annealing optimization) indicates that (1) the performance of GAScan and SAScan is significantly influenced by the parameter of the fraction value (the maximum allowed size of the detected cluster), which can only be estimated by multiple trials, while no such parameter is needed in AntScan; (2) AntScan shows superior power over GAScan and SAScan in detecting heterogeneous clusters. The case study on esophageal cancer in North China demonstrates that the cluster identified by AntScan has the larger likelihood value than that detected by SAScan and covers all high-risk regions of esophageal cancer whereas SAScan misses some high-risk regions (the region in the southwest of Shandong province, eastern China) due to the existence of a depression link. 相似文献