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Suspended and high-rise soil blocks of damaged rammed earth city wall generally reinforced by anchorage technology. According to the cultural relic protection principle, the primary task is to evaluate the anchoring force demands. Based on the analysis of formation mechanism and failure modes of dangerous soil blocks under earthquake, the disaster formation process caused by the cumulative section cracking is revealed, and the basic conditions for maintaining stability of the anchored city walls are analyzed. Then, a simplified mechanical model of cantilever beam was proposed for two typical failure modes. With the combination of energy conversion principle, the quasi-static method was used to derive the calculation formulas for the critical horizontal earthquake acceleration and the anchoring force demands. The advantage and disadvantage factors for the stability of sites were clarified with corresponding anchorage design suggestions. Finally, the influence of earthquake acceleration and existing cracks on anchoring force demands and critical earthquake acceleration are discussed through practical cases. The results showed that the optimal anchoring angles of sites under different failure modes are quite different; the increase of earthquake acceleration and cumulative cracking of the sections will increase the anchoring force demands significantly.

International audience; As Unmanned Aerial Vehicle's (UAV) battery life and stability develop, multiple UAVs are having more and more applications in the uninterrupted patrol and security. Thus UAV's searching, tracking and trajectory planning become important issues. This paper proposes an online distributed algorithm used in UAV's tracking and searching, with the consideration of UAV's practical need to recharge under limited power. We propose a Quantum Probability Model to describe the partially observable target positions, and we use Upper Confidence Tree (UCT) algorithm to find out the best searching and tracking route based on this model. We also introduce the Teammate Learning Model to handle the nonstationary problems in distributed reinforcement learning.