Probabilistic Analysis of Tunnel Roof Deflection under Sequential Excavation Using ANN-Based Monte Carlo Simulation and Simplified Reliability Approach
Publication: ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7, Issue 4
Abstract
Tunnel roof deflection is an important measure to control the safety of excavation activity, especially for large-section tunnels using the sequential construction method. A three-dimensional (3D) model of a sequential excavation project in the Lijiaping metro tunnel, Chongqing, China, was developed and the maximum tunnel roof deflection was calculated deterministically at the end of each excavation stage. By defining the characteristics of the soft rock layers surrounding the tunnel section as the random variables, a probabilistic analysis was conducted and the problem was formulated as a reliability model. Two different approaches were used to solve the established reliability problem. One entails an artificial neural network (ANN) trained by a large data set obtained from numerical simulations and then accompanied by the Monte Carlo (MC) sampling method to calculate the probability. The other is a simplified reliability approach using a small data set to approximate the exceedance probability via a regression-based algorithm. The proposed ANN-based metamodel used in the first method could accurately predict tunnel roof deflection and replace the software simulator. Subsequently, the probabilistic results obtained from this method following MC sampling could well converge and provide the probability with enough accuracy. Interestingly, the simplified approach with more than 40 random samples can also provide acceptable results, which provides an economical approach to estimate the exceedance probability.
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Data Availability Statement
All data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
The work presented in this study is supported by the Natural Science Foundation Committee Program of China (Nos. 52078377 and 51908324), National Key Research and Development Program of China (No. SQ2019YFB1600700), and the Key innovation team program of innovation talents promotion plan by MOST of China (No. 2016RA4059).
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ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
Volume 7 • Issue 4 • December 2021
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© 2021 American Society of Civil Engineers.
History
Received: Dec 2, 2020
Accepted: May 2, 2021
Published online: Jul 27, 2021
Published in print: Dec 1, 2021
Discussion open until: Dec 27, 2021
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