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Assessment of optimum settlement of structure adjacent urban tunnel by using neural network methods

机译:用神经网络方法评估城市隧道附近结构的最佳沉降

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Currently with the spread of tunnel constructions in cities, the proximity of other structures being built close to these tunnels have now become an important subject. Studying the rate of settlement of structures built in the vicinity of these tunnels could be an importance as well. The distance between tunnels and buildings is an important factor which can also be taken to account. Considering some of the parameters in place, favorable results can be achieved in having tunnels and other structures in the close proximity of each other. In this paper, the settlement of structures with different scenarios has been studied. The proximity of structures and their orientation in comparison with the location of the tunnels has also been a part of this study. Through the Finite Element Method (FEM), and with the use of Neural Network (NN), a various settlement situations have been studied. Using NN on the analysis of the FEM outcome and consideration of the vertical and horizontal distances between the tunnels and constructions with the number of their stories and the diameter of tunnel, relation between the settlements of constructions in any given direction will be immerge. In the study of this matter, the use of methods such as NN and genetic algorithms has not been reported. Using NN to evaluate the results can help to optimize the construction and implementation of underground structures.
机译:当前,随着城市中隧道结构的普及,靠近这些隧道的其他建筑物的邻近性已成为重要的课题。研究在这些隧道附近建造的结构的沉降速率也可能很重要。隧道与建筑物之间的距离是一个重要因素,也可以考虑在内。考虑到适当的一些参数,在使隧道和其他结构彼此紧邻的情况下,可以获得良好的结果。本文研究了具有不同场景的结构沉降。与隧道位置相比,结构的邻近性及其方向也已成为这项研究的一部分。通过有限元方法(FEM),并使用神经网络(NN),研究了各种沉降情况。使用神经网络对有限元结果进行分析,并考虑隧道与建筑物之间的竖向和水平距离以及层数和隧道直径,将沉入任何给定方向的建筑物沉降之间的关系。在对此问题的研究中,尚未报道使用诸如NN和遗传算法之类的方法。使用NN评估结果可以帮助优化地下结构的施工和实施。

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