Paper title：顾及地理要素的滑坡知识图谱构建与应用 Paper abstract： Paper type：硕士论文 Tutor 王波

科技项目中包含滑坡预测的研究内容，由于缺乏实际的观测数据，特此申请！

宁波大学岩石力学研究所关于滑坡研究科研的需要。

本人系中国地质大学学生，正在撰写毕业论文，研究方向为库岸滑坡，特此申请数据，便于论文撰写以及科学研究。 论文题目：库岸滑坡研究 论文摘要：The sliding zone has received a large number of laboratory investigations relating to permeability, yet little attention is paid to the particular sliding zone of the slide-prone Badong formation. Here, we experimentally investigate the permeability nature and the mechanism of seepage in the viscous sliding zone of the Huangtupo landslide. Saturated seepage tests were performed first with consideration of six dry densities and thirteen hydraulic gradients, in conjunction with the mercury intrusion porosimetry test and scanning electron microscopy test for the microstructure investigation after seepage. The results show that seepage in the sliding zone soil does not follow Darcy's law, since there is a threshold hydraulic gradient (io) below which no flow is observed and a critical hydraulic gradient (icr) over which the hydraulic conductivity (K) is tending to be stable. The percentage of bound water could be responsible for the occurrence of io and icr. Furthermore, pore size distributions (PSD) less than 0.6 µm and between 10 and 90 µm exhibit positive and negative correlations with the io, respectively, indicating that the io is related to the PSD. The mechanism accounting for this result is that pore water pressure forces fine clay particles into the surrounding large pores and converts arranged particles to discretely distributed ones, thereby weakening the connectivity of pores. The seepage in the sliding zone behaves differently from that in the sliding mass and sliding bed in response to its particular permeability. 论文类型：博士毕业论文 导师姓名：唐辉明

论文题目：基于边坡特征位移的土体参数反演与安全系数预测 论文摘要：根据现场实测数据结合岩土极限分析与有限元分析数据，构建边坡特征点位移与土体特征参数的数据库，选用合适的人工智能算法结合优化算法，探寻一种能够体现高效、非线性强、映射准确的表达过程，为边坡的即时状态做出反应，为下一步构建基于数据驱动的边坡数字孪生体做足预备工作。 论文类型：硕士论文 导师姓名：周航

论文题目：基于机器视觉的滑坡智能识别方法与监控系统研究 论文摘要： 论文类型：博士论文

论文题目：基于机器学习方法对三峡库区滑坡位移变形作预测分析 论文摘要：边坡位移预测对预警系统的发展至关重要，可以有效预防或减少滑坡对人民生命财产和当地环境造成的损害。这一问题在三峡库区尤为重要，因为三峡库区的地质和水文条件对滑坡的影响更为显著。一般认为，水库滑坡是一个动态的、相互关联的复杂非线性系统。本文将基于位移时间序列分解，应用机器学习方法预测及分析三峡库区滑坡的位移。 论文类型：期刊论文

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