本数据集包含济南地铁R3线王舍人站—裴家营站区间,在920 ~1 400环范围内下穿的建(构)筑物群情况。该数据最小精度为0.01m,通过检测得到,主要在济南地铁R3线王舍人站—裴家营站区间920 ~1 400环范围内。数据集内包含一个excel文件。该表格型数据主要包括典型建筑物名称,所对的隧道环号及其距隧道的最下距离
采集时间 | 2020/01/01 - 2020/12/31 |
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采集地点 | 山东济南 |
数据量 | 26.8 KiB |
数据格式 | excel |
坐标系 |
观测监测得到。
观测监测得到。
数据质量良好。
# | 编号 | 名称 | 类型 |
1 | 2018YFC0809600 | 涉水重大基础设施安全保障技术研究与工程示范 | 国家重点研发计划 |
# | 标题 | 文件大小 |
---|---|---|
1 | 数据集说明文件.docx | 17.3 KiB |
2 | 盾构施工沉降多源数据实时交互平台开发.xlsx | 9.5 KiB |
# | 时间 | 姓名 | 用途 |
---|---|---|---|
1 | 2025/01/02 19:35 | 张* |
Paper title:Research on Settlement Prediction Based on a Diffusion Model
Paper abstract:This paper explores a novel approach to predict settlement by integrating diffusion-based modeling techniques within geotechnical and civil engineering contexts. The proposed framework treats the settlement process as a dynamic phenomenon, where diffusion equations capture the spatial and temporal evolution of settlement under various loading and environmental conditions. By systematically analyzing soil properties, ground water levels, and construction loads, the diffusion model is calibrated to reflect real-world behaviors. Through both numerical simulations and field studies, the model demonstrates high accuracy in forecasting settlement magnitudes and distribution patterns. These findings suggest that diffusion-based methods could significantly improve decision-making in construction design and project management by providing more reliable predictive capabilities. Furthermore, the paper discusses the limitations of the model and proposes future research directions aimed at enhancing the robustness and scalability of diffusion-driven settlement prediction methodologies.
Paper type:Research Articles
tutorAssociate Professor Zhao Liang
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