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| 基坑的双排桩支护设计及变形规律 | |
| 引用本文： | 孙宇清,赵锐,姚青,史斌,刘佳. 基坑的双排桩支护设计及变形规律[J]. 山东大学学报(工学版), 2006, 36(3): 86-94. DOI: 10.6040/j.issn.1672-3961.0.2019.019 |
| 作者姓名： | 孙宇清 赵锐 姚青 史斌 刘佳 |
| 作者单位： | 1. 重庆市轨道交通(集团)有限公司, 重庆 401120 2. 河北省大型结构健康诊断与控制重点实验室, 河北 石家庄 050043 3. 中国矿业大学(徐州)深部岩土力学与地下工程国家重点实验室, 江苏 徐州 221116 |
| 基金项目： | 山东省科技计划;山东省教育厅资助项目 |
| 摘 要： | 以济南市槐荫区某基坑工程进行双排桩支护设计为例,分别采用有限差分数值软件中弹塑性实体单元和线性桩单元模拟开挖-支护-施工全过程,且考虑桩土相互作用并进行三维动态分析。对基坑坡面水平位移、桩身弯矩、桩身剪力等变化规律进行研究,分析双排桩支护体系的受力机理。通过土体力学参数、桩径、桩长、桩距、连梁等设计参数对基坑变形影响的敏感性进行对比,并就“实体”和“结构”单元桩的计算结果进行比较。研究表明,模拟过程能较好展示双排支护桩施工过程的受力机理,且计算精度较高;不可控参数中土体的黏聚力、摩擦角以及可控因素中的桩身长度、排桩距离等对支护效果影响较大,计算结果可为双排支护桩设计参数选取提供参考。 |
| 关 键 词： | 双排支护桩 桩土相互作用 敏感性分析 实体单元桩 结构单元桩 |
| 文章编号： | 1672-3961(2006)03-0086-05 |
| 收稿时间： | 2006-01-12 |
| 修稿时间： | 2006-01-12 |
A mesh-based clustering algorithm in the presence of obstacles |
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| SUN Yu-qing,ZHAO Rui,YAO Qing,SHI Bin,LIU Jia. A mesh-based clustering algorithm in the presence of obstacles[J]. Journal of Shandong University of Technology, 2006, 36(3): 86-94. DOI: 10.6040/j.issn.1672-3961.0.2019.019 | |
| Authors: | SUN Yu-qing ZHAO Rui YAO Qing SHI Bin LIU Jia |
| Affiliation: | 1. Chongqing Rail Transit(Group) Co.Ltd., Chongqing 401120, Chongqing, China 2. Hebei Province Key Lab of Structural Health Monitoring and Control, Shijiazhuang 050043, Hebei, China 3. State Key Laboratory for Geomechanics and Deep Underground Engineering, School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou 221116, Jiangsu, China |
| Abstract: | Clustering of spatial data in the presence of obstacles has a wide application,and is an important research issue recently.A novel mesh-based clustering algorithm in the presence of obstacles is presented.Which introduces the concept of obstacle-mesh into the algorithm of CLICQU.By decomposing obstacles into appropriate meshes,the algorithm can deal with random-shape obstacles.Compared with the existing similar algorithms on the aspects of theory analysis and experiment,this algorithm has a preferable time complexity and clustering results on the same data objects. |
| Keywords: | spatial data mining clustering obstacle constraints mesh |
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