Optimal Strategy to Mitigate Tunnel-Induced Settlement in Soft Soils: Simulation Approach
Publication: Journal of Performance of Constructed Facilities
Volume 33, Issue 5
Abstract
A simulation-hybrid approach that incorporates artificial intelligence (AI), simulation, and experiment components is proposed to cope with the excessive surface settlement induced by tunneling excavation for the discovery of the optimal strategy. The AI component aims to predict the tunnel-induced surface settlement to detect whether the estimated settlement exceeds the control standard. The simulation component aims to investigate the complex tunnel–soil interaction for benchmarking multiple response alternatives. The experiment component aims to validate the effectiveness of the identified optimal response strategy in a natural setting environment. A shallow tunnel case in China is used to testify the effectiveness of the proposed approach. Results indicate that (1) the continuous grouting scheme for ground treatment is identified as the optimal strategy to deal with the excessive settlement potential and (2) the implementation in the actual practice further confirms that the continuous grouting scheme can reduce the surface settlement by 48.75%. The novelty of this approach lies in (1) the body of knowledge by developing a proactive approach to simulate, predict, and mitigate tunnel-induced settlement under uncertainty and (2) the state of practice by providing new insight into the control of excessive settlement potential with high accuracy and reliability.
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Acknowledgments
The National Key Research and Development Project (Grant No. 2016YFC0800208), the National Natural Science Foundation of China (Grant Nos. 71571078, 71801101, 51778262, and 51708241), and the Postdoctoral Research Foundation of China (Grant No. 2018M632880) are acknowledged for their financial support of this research.
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Journal of Performance of Constructed Facilities
Volume 33 • Issue 5 • October 2019
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©2019 American Society of Civil Engineers.
History
Received: Jul 11, 2018
Accepted: Feb 13, 2019
Published online: Aug 2, 2019
Published in print: Oct 1, 2019
Discussion open until: Jan 2, 2020
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