Prediction and mitigation of train-induced vibrations of large-scale building constructed on subway tunnel.

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ID: 17889
2019
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Abstract
Among all the recent improvements in the railway industry, ground vibration remains an important showstopper in metropolitan cities. In some particular cases, significant levels of vibration are felt by residents. The role of engineers is to propose mitigation solutions and to ensure that they are efficient in the long-term. This paper presents a numerical study of a large-scale building close to underground networks. A two-step time-frequency prediction method for train-induced vibrations of a superstructure is proposed in this work. In the first step, the spatial train-track coupled dynamic model in time domain is established and then simulated to obtain the vertical and lateral rail supporting forces (fastener forces). In the second step, the discrete Fourier Transform (DFT) of fastener forces are taken as the external loads of a finite element (FE) model of the track-tunnel-soil-building system to solve the building vibrations. On this basis, train-induced vibrations of the large-scale building are predicted under different train operation conditions, and two relevant standards are adopted to evaluate the building vibrations. Further, a base isolation measure, that consists in installing steel springs between the superstructure and the base, is employed to mitigate excessive building vibration. Results show that the underground train and track interaction could result in over-limit building vibrations. The train moving with a higher speed will deteriorate track vibration level and leads to more serious extent of over-limit vibrations of the larger-scale building. The base isolation measure can effectively reduce the excessive building vibrations, and also ensures the train-induced vibrations of the building to satisfy the relevant standard requirements under the worst train operation conditions.
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Authors Yang, Jianjin;Zhu, Shengyang;Zhai, Wanming;Kouroussis, Georges;Wang, Yue;Wang, Kaiyun;Lan, Kai;Xu, Fangzheng;
Journal The Science of the total environment
Year 2019
DOI
S0048-9697(19)30905-2
URL
Keywords Keywords not found

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