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Influence of adjacent shield tunneling construction on existing tunnel settlement: field monitoring and intelligent prediction
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Long RAN, Yang DING, Qizhi CHEN, Baoping ZOU, and Xiaowei YE. Influence of adjacent shield tunneling construction on existing tunnel settlement: field monitoring and intelligent prediction[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .
@article{title="Influence of adjacent shield tunneling construction on existing tunnel settlement: field monitoring and intelligent prediction",
author="Long RAN, Yang DING, Qizhi CHEN, Baoping ZOU, and Xiaowei YE",
journal="Journal of Zhejiang University Science A",
volume="-1",
number="-1",
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year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2200573"
}
%0 Journal Article
%T Influence of adjacent shield tunneling construction on existing tunnel settlement: field monitoring and intelligent prediction
%A Long RAN
%A Yang DING
%A Qizhi CHEN
%A Baoping ZOU
%A and Xiaowei YE
%J Journal of Zhejiang University SCIENCE A
%V -1
%N -1
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%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2200573
TY - JOUR
T1 - Influence of adjacent shield tunneling construction on existing tunnel settlement: field monitoring and intelligent prediction
A1 - Long RAN
A1 - Yang DING
A1 - Qizhi CHEN
A1 - Baoping ZOU
A1 - and Xiaowei YE
J0 - Journal of Zhejiang University Science A
VL - -1
IS - -1
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%@ 1673-565X
Y1 - 1998
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2200573
Abstract: Urban subway tunnel construction inevitably disturbs the surrounding rock and causes the deformation of existing subway structures. Dynamic predictions of the tunnel horizontal displacement, tunnel ballast settlement, and tunnel differential settlement are important for ensuring the safety of buildings and tunnels. First, based on the Hangzhou metro project, we analyzed the influence of construction on the deformation of existing subway structures and the difficulties and key points in monitoring. Then a deformation prediction model, based on a back propagation (BP) neural network, was established with massive monitoring data. In particular, we analyzed the influence of four structures of the BP neural network on prediction performance, i.e., single input–single hidden layer–single output, multiple inputs–single hidden layer–single output, single input–double hidden layer–single output, and multiple input–double hidden layer–single output and verified them using measured data.
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