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Data based complex network modeling and analysis of shield tunneling performance in metro construction

机译:基于数据的复杂网络建模与地铁盾构施工性能分析

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Shield tunneling performance depends mainly on changes in geological conditions and machine working status. Understanding its characteristics is the key to operating and controlling shield machine during the metro construction. Despite the large set of shield tunneling data in having been a big challenge in interpreting the underlying meaning, a systematical view of the shield tunneling performance has not yet been identified. In this study, a methodology for the modeling and analysis of shield tunneling performance network is proposed which aims at integrating the high dimensional data mining and the complex network approaches for shield performance evaluation. It is tested by analyzing the heterogeneous data of shield tunneling performance acquired from in the first Yangtze river crossing metro tunnel project in China. Each segment ring tunneling cycle in the construction were considered to be nodes of the network mapped while edges are determined by nodes having the similarity greater than an optimal threshold value. The construct network exhibits high clustering coefficient combined with comparatively short path lengths, which demonstrates a small world topology feature. Communities in the performance network with different size based on the complex network are detected, which provides the vital decision information for geological conditions identification and shield tunneling performance risk evaluation.
机译:盾构的掘进性能主要取决于地质条件和机器工作状态的变化。了解其特性是在地铁施工期间操作和控制盾构机的关键。尽管在解释基本含义方面遇到了巨大挑战,但盾构隧道数据的数量很大,但尚未确定盾构隧道性能的系统视图。在这项研究中,提出了一种用于盾构隧道性能网络建模和分析的方法,旨在将高维数据挖掘与复杂的网络方法进行盾构性能评估。通过分析从中国第一个长江跨江地铁隧道项目中获得的盾构隧道掘进性能的非均质数据进行测试。该构造中的每个分段环隧穿循环被认为是所映射的网络的节点,而边缘由具有大于最佳阈值的相似性的节点确定。该构造网络具有较高的聚类系数和较短的路径长度,这表明其具有较小的世界拓扑特征。检测基于复杂网络的不同规模的性能网络中的社区,这为地质条件识别和盾构隧道性能风险评估提供了至关重要的决策信息。

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