Citation

Guo Y, Zheng J, Liu H. Tunn. Undergr. Space Tech. 2023; 140: e105214.

Copyright

(Copyright © 2023, Elsevier Publishing)

DOI

10.1016/j.tust.2023.105214

PMID

unavailable

Abstract

Ground surface settlement is a key indicator of the environmental safety of tunneling. Traditional early warning methods for tunneling-induced ground surface settlement are mainly based on the comparison results of the accumulated settlement and the control threshold in the standard, which may omit precursor information and delay the timing of warnings. In actual tunneling projects, the occurrence of most accidents is not in isolation, but is the cumulative result of a sequence of accident precursors. Therefore, this paper presents an early warning method for tunneling-induced ground surface settlement, with the aim of comprehensively considering accident precursors and consequences in early warning. By applying the proposed early warning method, accident precursors of excessive ground settlement can be identified as early warning signals, and damage consequences of tunneling to the surrounding environment can be utilized for the classification of warning levels. Safety principles provide guidelines for the modeling of accident scenarios and the implement of targeted safety interventions. The complete accident scenario of excessive surface settlement is characterized by the bow-tie. Relevant data are obtained from expert knowledge, and fuzzy set theory is employed to cope with the uncertainty of expert knowledge. Then, the bow-tie is mapped into the Bayesian network formalism. Within the Bayesian network, the consequence can be updated according to the accident precursor observed in tunneling, and the dynamic early warning can be issued accordingly. Moreover, the truncated accident sequence can be extracted from the accident scenario to help provide targeted safety interventions. Finally, a case study demonstrates that the proposed early warning method can effectively reduce the frequency and consequences of tunneling-induced excessive ground surface settlement.

Language: en

Keywords

Accident precursor; Bayesian network; Bow-tie; Early warning; Ground surface settlement