Hard-rock TBM jamming subject to adverse geological conditions: Influencing factor, hazard mode and a case study of Gaoligongshan Tunnel

Xu, Zhenhao; Wang, W.Y.; Lin, Peng; Nie, Lei; Wu, Jing; Li, Z.M.

doi:10.1016/j.tust.2020.103683

Cited by 60 publications

(10 citation statements)

References 20 publications

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“…The tunnel design and construction methods are based on the results of the geological survey. The geological exploration for tunnel construction is limited by the technology, cost and natural conditions, and there are problems such as lack of detail and lowexploration accuracy [ 27 ]. The geological survey results significantly impact the design and construction links and strongly support the construction project [ 28 ].…”

Section: Methodsmentioning

confidence: 99%

Risk factors influencing tunnel construction safety: Structural equation model approach

Yang

Wang

Easa

et al. 2023

Heliyon

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Section: Methodsmentioning

confidence: 99%

Risk factors influencing tunnel construction safety: Structural equation model approach

Yang

Wang

Easa

et al. 2023

Heliyon

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“…where: μ is tunnel convergence deformation; D is tunnel diameter. The occurrence of squeezing is closely related to geological structure, ground stress, rock mass conditions, and excavation disturbances (Hasanpour et al, 2020b;Xu et al, 2021). The selection of squeezing prediction indicators should meet: (1) they are easy to obtain; (2) they have strong representativeness; (3) they can reflect the characteristics of the squeezing from different aspects.…”

Section: Database Descriptionmentioning

confidence: 99%

A Gray Wolf Optimization-Based Improved Probabilistic Neural Network Algorithm for Surrounding Rock Squeezing Classification in Tunnel Engineering

et al. 2022

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Surrounding rock squeezing deformation is a common and prominent hazard in tunnel engineering projects, which often induces the shield jamming disaster during the TBM tunneling process. Based on the 139 groups of historical squeezing deformation cases, this study developed a hybrid PCA-IWGO-PNN model for squeezing classification. According to the influencing factors and characteristics of squeezing deformation, the strength-stress ratio, tunnel burial depth, tunnel equivalent diameter, rock mass quality index, and support stiffness were selected to establish the prediction index system of squeezing level. Because the probabilistic neural network (PNN) requires that the input variables are independent, principal component analysis (PCA) was used to preprocess the original data to eliminate the correlation between prediction indexes and achieve dimensionality reduction. The spread coefficient was the critical hyper-parameter in the PNN, and the improved gray wolf optimization (IGWO) algorithm was used to realize its efficient automatic optimization. Then, the PNN model was applied to engineering practice. Only 1 of 20 test samples was misjudged, achieving the 95% prediction accuracy. Finally, the comparison analysis with the artificial neural network (ANN) model, support vector machine (SVM) model, and random forest (RF) model was conducted. Among them, the PNN model achieved the highest prediction accuracy, followed by the artificial neural network (85%), RF (85%), and SVM (80%). In addition, the PNN model had the fastest running speed, which only consumed 5.6350 s, while the running time of ANN, SVM, and RF was 8.8340, 6.2290, and 6.9260 s, respectively. The hybrid PCA-IWGO-PNN model developed in this research provides an effective method for surrounding rock squeezing classification, and it has superiorities in both prediction accuracy and running speed.

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“…Active faults can cause partial or total collapse of tunnels. More specifically, some parts of tunnels can be collapsed when deformations produced by TBM excavation are enormous in faults areas [101]. As shown in Tabs.…”

Section: Consideration On Adverse Geological Conditionsmentioning

confidence: 99%

Long-term degradation, damage and fracture in deep rock tunnels: A review on the effect of excavation methods

Frenelus

Peng

Zhang

2021

Frattura Ed Integrità Strutturale

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Rocks are frequently host materials for underground structures, particularly for deep Tunnels. Their behavior plays a fundamental role in the overall stability of these structures. In fact, the erection of deep tunnels imposes rocks excavations around the defined routes. These excavations are generally carried out by various methods of which the most used are Drill-and-Blast (DB) and Tunnel Boring Machine (TBM). However, regardless of the tunnelling method used, the impacts such as the perturbation of the initial stress field in rocks and the release of the stored energy are always significant. The impacts produce damage, fractures and deformations which are generally time-dependent and influence the long-term stability of deep tunnels built in rocks. Thus, by considering the aforementioned excavation methods, this paper identifies, reviews and describes the relevant factors generated during and after rock excavations. Interestingly, such factors directly or indirectly influence the long-term stability and therefore the structural integrity of deep rock tunnels. In addition, some recommendations and proposals for future works are presented. This paper can provide useful references in understanding the degradations, damage and fractures generated by tunnelling methods and facilitate suitable actions to ensure long-term stability of deep underground structures.

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Hard-rock TBM jamming subject to adverse geological conditions: Influencing factor, hazard mode and a case study of Gaoligongshan Tunnel

Cited by 60 publications

References 20 publications

Risk factors influencing tunnel construction safety: Structural equation model approach

Risk factors influencing tunnel construction safety: Structural equation model approach

A Gray Wolf Optimization-Based Improved Probabilistic Neural Network Algorithm for Surrounding Rock Squeezing Classification in Tunnel Engineering

Long-term degradation, damage and fracture in deep rock tunnels: A review on the effect of excavation methods

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