Risk assessment of TBM jamming based on Bayesian networks

Lin, Peng; Xiong, Yujie; Xu, Zhenhao; Wang, Qianqian; Shao, Ruiqi

doi:10.1007/s10064-021-02511-z

Cited by 23 publications

(7 citation statements)

References 36 publications

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“…Some promising research domains for machine learning in tunnelling are the geological prognosis ahead of the face, the interpretation of monitoring results, automation and maintenance [32]. At present, however, research appears to be focussed on the following topics: prediction of TBM operational parameters [34,[39][40][41][42][43][44][45][46], penetration rate [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63], porewater pressure [64], ground settlement [65][66][67], disc cutter replacement [68][69][70], jamming risk [71,72] and geological classification [73][74][75][76]). Few authors estimated the face support pressure of TBMs with machine learning [35,52].…”

Section: Introductionmentioning

confidence: 99%

Reinforcement learning for the face support pressure of tunnel boring machines

Soranzo¹,

Guardiani²,

Wu³

2023

Preprint

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In tunnel excavation with boring machines, the tunnel face is supported to avoid collapse and minimise settlement. This article proposes the use of reinforcement learning, specifically the Deep Q-Network algorithm, to predict the face support pressure. The approach is tested both analytically and numerically. By using the soil properties ahead of the tunnel face and the overburden depth as the input, the algorithm is capable of predicting the optimal tunnel face support pressure, adapting to changes in geological and geometrical conditions.

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Section: Introductionmentioning

confidence: 99%

Reinforcement learning for the face support pressure of tunnel boring machines

Soranzo¹,

Guardiani²,

Wu³

2023

Preprint

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“…To improve the tunneling adaptability of TBM, relevant experts and scholars have also carried out extensive research [6][7][8][9]. For example, researchers at the University of Oviedo in Spain [10] have carried out analysis and research on methane emission in single-shield TBM tunneling in Carboniferous strata.…”

Section: The Introductionmentioning

confidence: 99%

Development and Application of Adaptive Evaluation System for TBM Tunneling Based on Case-Based Reasoning

et al. 2023

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The excavation adaptability evaluation decision process for the TBM (tunnel boring machine) in long and large tunnels under complex geological conditions is an uncertain and fuzzy problem affected by multiple factors. Aiming to shift the status quo of TBMs tunneling adaptability evaluation and the ineffective use of related accident cases, the TBM tunneling adaptability evaluation method based on case-based reasoning is proposed and researched. The case-based reasoning method can use existing engineering experience and knowledge to answer newly encountered problems, and can accurately and efficiently evaluate the adaptability of TBM tunneling. Based on the nearest neighbor method, this paper establishes the calculation formula of TBM tunneling adaptability similarity. Based on the statistical analysis method, the evaluation indicators that play a relatively important role in the system are selected, and the TBM tunneling adaptability evaluation index system is constructed. The analytic hierarchy process (AHP) is used to determine the weight of the evaluation indicators at each level. According to the characteristics of TBM tunneling adaptability evaluation, the overall design of the case-based reasoning-based TBM tunneling adaptability evaluation decision system CBR-TBMEAEDS (case-based reasoning-TBM excavation adaptive evaluation decision system) is proposed, and the TBM tunneling adaptability evaluation case is expressed The case-based reasoning method and modification method were designed, and CBR-TBMEAEDS was developed. The system can be used to evaluate the adaptability of TBM to the constructed case library, and the evaluation results are consistent with the actual situation.

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“…Balta et al 12 developed a risk identification software for tunnel engineering based on Bayesian theory and successfully applied it to engineering practice. Based on engineering cases and research, Lin et al 13 summarized and analyzed four influencing factors closely related to TBM jamming. Using ISM theory, a dynamic BN model was established to obtain the geological conditions in front of the tunnel face and achieve dynamic prediction of geological disasters and TBM jamming during tunnel construction.…”

Section: Introductionmentioning

confidence: 99%

Safety risk assessment of loess tunnel construction under complex environment based on game theory-cloud model

Han

Jia

Feng

et al. 2023

Sci Rep

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Due to the impact of the surrounding environment, the safety impact factors encountered during the construction of loess tunnels are complex and numerous, which causes frequent accidents, and there is a lack of safety risk assessment methods applicable to the construction of loess tunnels under complex environment. Based on the Luochuan tunnel project of the Xi’an–Yan’an High-Speed Railway, this paper analyzes the impact factors of loess tunnel construction risks, and 15 main impact factors involving subjective and objective factors are selected to establish the safety risk assessment system of loess tunnel construction under complex environment. To determine the weight of the impact factors, this paper introduces the combination weighting method based on game theory for the first time. Then, the risk assessment model of loess tunnel construction safety is established by using the conventional cloud model theory. Finally, the model is applied to the supporting project for verification. The results show that support and lining have the largest impact on tunnel construction safety, followed by construction management, surrounding rock grade, harmful engineering ground, monitoring measurement, forepoling, and construction method. The assessment result is consistent with the actual construction risk degree, which proves that the assessment result of the model is accurate and reliable, and the model has guiding significance for the safety risk assessment of loess tunnel construction under complex environment.

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Risk assessment of TBM jamming based on Bayesian networks

Cited by 23 publications

References 36 publications

Reinforcement learning for the face support pressure of tunnel boring machines

Reinforcement learning for the face support pressure of tunnel boring machines

Development and Application of Adaptive Evaluation System for TBM Tunneling Based on Case-Based Reasoning

Safety risk assessment of loess tunnel construction under complex environment based on game theory-cloud model

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