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Informatization construction method for long, large and deep foundation pit close to subway tunnel
The invention provides an informatization construction method for a long, large and deep foundation pit adjacent to a subway tunnel, which comprises the following steps: adopting bin division excavation, performing bin division excavation on the foundation pit by combining a supporting wall with a separating wall, excavating the foundation pit to a designed elevation under the action of an inner support and angle support supporting structure, and enabling the supporting wall and the separating wall to be a wall body formed by combining a secant pile with a top beam and a waist beam; after the previous foundation pit is excavated, deformation monitoring is conducted on the excavated foundation pit; on the basis of different control points, on-site construction monitoring data of the top horizontal displacement, the deep soil displacement, the ground surface settlement and the deep horizontal displacement of the enclosure structure are measured respectively; in the whole excavation process of the foundation pit, construction data are correspondingly recorded; the method comprises the following steps: carrying out back analysis on construction data and monitoring data obtained in the previous order in an established and trained artificial neural network to obtain mechanical parameters fitting the actual site construction; and analyzing actual soil mechanical parameters fitting site construction, and carrying out numerical simulation calculation on subsequent foundation pit excavation in the finite element model.
本发明提供了一种紧邻地铁隧道长大深基坑信息化施工方法,采用分仓开挖,基坑通过支护墙结合隔离墙进行基坑的分仓开挖,并在内撑、角撑支护结构作用下,将基坑开挖至设计高程,支护墙、隔离墙为咬合桩结合冠梁、腰梁形成的墙体;在前序基坑开挖后,对已开挖基坑进行形变监测;基于不同控制点,分别测出围护结构顶水平位移、深层土体位移、地表沉降、围护结构深层水平位移现场施工监测数据;并且在整个基坑开挖过程中,也相应记录施工数据;通过前序所得施工数据、监测数据,在已建立并经过训练的人工神经网络中,反分析得出贴合现场施工实际的力学参数;分析贴合现场施工实际的土体力学参数,在有限元模型中,对后续基坑开挖进行数值模拟计算。
Informatization construction method for long, large and deep foundation pit close to subway tunnel
The invention provides an informatization construction method for a long, large and deep foundation pit adjacent to a subway tunnel, which comprises the following steps: adopting bin division excavation, performing bin division excavation on the foundation pit by combining a supporting wall with a separating wall, excavating the foundation pit to a designed elevation under the action of an inner support and angle support supporting structure, and enabling the supporting wall and the separating wall to be a wall body formed by combining a secant pile with a top beam and a waist beam; after the previous foundation pit is excavated, deformation monitoring is conducted on the excavated foundation pit; on the basis of different control points, on-site construction monitoring data of the top horizontal displacement, the deep soil displacement, the ground surface settlement and the deep horizontal displacement of the enclosure structure are measured respectively; in the whole excavation process of the foundation pit, construction data are correspondingly recorded; the method comprises the following steps: carrying out back analysis on construction data and monitoring data obtained in the previous order in an established and trained artificial neural network to obtain mechanical parameters fitting the actual site construction; and analyzing actual soil mechanical parameters fitting site construction, and carrying out numerical simulation calculation on subsequent foundation pit excavation in the finite element model.
本发明提供了一种紧邻地铁隧道长大深基坑信息化施工方法,采用分仓开挖,基坑通过支护墙结合隔离墙进行基坑的分仓开挖,并在内撑、角撑支护结构作用下,将基坑开挖至设计高程,支护墙、隔离墙为咬合桩结合冠梁、腰梁形成的墙体;在前序基坑开挖后,对已开挖基坑进行形变监测;基于不同控制点,分别测出围护结构顶水平位移、深层土体位移、地表沉降、围护结构深层水平位移现场施工监测数据;并且在整个基坑开挖过程中,也相应记录施工数据;通过前序所得施工数据、监测数据,在已建立并经过训练的人工神经网络中,反分析得出贴合现场施工实际的力学参数;分析贴合现场施工实际的土体力学参数,在有限元模型中,对后续基坑开挖进行数值模拟计算。
Informatization construction method for long, large and deep foundation pit close to subway tunnel
一种紧邻地铁隧道长大深基坑信息化施工方法
CHENG GUIZHI (Autor:in) / LI ZHANFENG (Autor:in) / KANG LIN (Autor:in) / BI YANZHE (Autor:in) / WU DIANWEI (Autor:in) / GUO YUANBO (Autor:in) / QIAN QILONG (Autor:in)
10.09.2024
Patent
Elektronische Ressource
Chinesisch
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