Meso-mechanism of rolling dynamic compaction to reinforce loose landslide dam materialEI北大核心

为系统研究冲击碾压过程中松散堰塞坝料的细观密实机制,基于自行设计的可视化冲击碾压模型装置及粒子图像测速技术,研究了不同冲击碾压参数对堰塞坝料地基的表面变形、内部变形及颗粒位移规律的影响。试验结果表明,冲击碾压加固过程是冲击和碾压两者共同作用,由于水平冲击作用,冲击点下方地基的变形具有非对称性。“高速轻轮”的施工参数会强化冲击效果,弱化碾压效果,造成地基表面平整性差。堰塞坝料冲击轮加固过程中的最大位移发生在三边形冲击轮圆弧面较平滑处与土体接触时,随后由于模型冲击轮重心上升,地基出现部分弹性回弹。提高冲击轮的牵引速度能够促进冲击能量向深层传递,但水平影响宽度有限;提高冲击轮的质量则能促进能量向两侧水平方向传递,但影响深度有限。对于模型试验的易贡堰塞坝料地基,冲击碾压最佳牵引速度约为0.75 m/s。结果可为堰塞坝料地基的冲击碾压浅层加固提供理论依据。     

Graph distance and feature-guided multi-view clustering: A novel method for clustering urban buildings

Urban buildings are an integral component of urban space, and accurately identifying their spatial configurations and grouping them is vital for various urban applications. However, most existing building clustering methods only utilize the original spatial and nonspatial features of buildings, disregarding the potential value of complementary information from multiple perspectives. This limitation hinders their effectiveness in scenarios with intricate spatial configurations. To address this, this article proposes a novel multi-view building clustering method that captures cross-view information from spatial and nonspatial features. Drawing inspiration from both spatial proximity characteristics and nonspatial attributes, three views are established, including two spatial distance graphs (centroid distance graph and the nearest outlier distance graph) and a building attribute graph (multiple-attribute graph). The three graphs undergo iterative cross-diffusion processes to amplify similarities within each predefined graph view, culminating in their fusion into a unified graph. This fusion facilitates the comprehensive correlation and mutual enhancement of spatial and nonspatial information. Experiments were conducted using 10 real-world community-building datasets from Wuhan and Chengdu, China. The results demonstrate that our approach achieves 21.27% higher accuracy and 22.28% higher adjusted rand index in recognizing diverse complex arrangements compared to existing methods. These findings highlight the importance of leveraging complementary and consensus information across different feature dimensions for improving the performance of building clustering.