Tunneling and deep excavations in spatially variable soil and rock masses: A short review

Zhang, Wengang; Han, Liang; Gu, Xiaoqiang; Wang, Lin; Chen, Fuyong; Liu, Hanlong

doi:10.1016/j.undsp.2020.03.003

Cited by 56 publications

(11 citation statements)

References 137 publications

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“…However, the geological condition in the geological investigation report is sparse density and always different from the actual rock mass types 2 . Suppose the wrong judge of the geological condition during TBM excavation, it will undoubtedly introduce serious severe challenges to the tunnel construction, such as low TBM utilization, high additional cost and even safety problems 3 , 4 . Therefore, to make sure about the efficient and safe tunneling process of TBM, it is very important to ensure the rock mass online assessment 5 .…”

Section: Introductionmentioning

confidence: 99%

Automated rock mass condition assessment during TBM tunnel excavation using deep learning

Chen

Liu

et al. 2022

Sci Rep

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Rock mass condition assessment during tunnel excavation is a critical step for the intelligent control of tunnel boring machine (TBM). To address this and achieve automatic detection, a visual assessment system is installed to the TBM and a lager in-situ rock mass image dataset is collected from the water conveyance channel project. The rock mass condition assessment task is transformed into a fine-grain classification task. To fulfill the task, a self-convolution based attention fusion network (SAFN) is designed in this paper. The core of our method is the discovery and fusion of the object attention map within a deep neural network. The network consists of two novel modules, the self-convolution based attention extractor (SAE) module and the self-convolution based attention pooling algorithm (SAP) module. The former is designed to detect the intact rock regions generating the attention map, and the latter is designed to improve the performance of classifier by fusing the attention map that focuses on the intact rock regions. The results of SAFN are evaluated from aspects of interpretability, ablation, accuracy and cross-validation, and it outperforms state-of-the-art models in the rock mass assessment dataset. Furthermore, the dynamic filed test show that our assessment system based on the SAFN model is accurate and efficient for automated classification of rock mass.

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Section: Introductionmentioning

confidence: 99%

Automated rock mass condition assessment during TBM tunnel excavation using deep learning

Chen

Liu

et al. 2022

Sci Rep

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“…The uncertainty can be mainly divided into two categories [10]: spatial variability of soil properties within one nominally homogeneous layer [21,26] and geological uncertainty in heterogeneous layer [8,34]. In past decades, much attention has been attracted to analyse the influence of spatial variability of soil properties on the performance of geotechnical systems such as tunnels & Dong-Ming Zhang 09zhang@tongji.edu.cn [3,11,20,31,32,47,48]. On the other hand, geological uncertainty also exists in reality and plays a significant role in geo-structure performance [10,14,25].…”

Section: Introductionmentioning

confidence: 99%

Quantitative evaluation of geological uncertainty and its influence on tunnel structural performance using improved coupled Markov chain

et al. 2021

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The geo-structures embedded in the multiple variable strata could be significantly affected by the geological uncertainty. The quantitative evaluation of geological uncertainty and its influence on the structural safety of embedded tunnels are seldom studied in the past. This paper aims to analyse the effect of geological uncertainty on the structural performance of tunnel using the proposed stochastic geological modelling framework. The geological uncertainty is characterized using an improved coupled Markov chain model based on sparse limited boreholes. A mapping approach is presented to solve the mesh asymmetry problem between the simulated strata and the numerical tunnel model. The tunnel structural performance analysis is then conducted based on the combined model considering the geological uncertainty and tunnel structure. A geological uncertainty index (GUI) is proposed to quantitatively evaluate the level of uncertainty of each borehole and the whole site. The effect of the borehole layout scheme on uncertainty evaluation of factor of safety of tunnel structure is investigated by a large number of stratigraphic realizations. Boreholes collected from Norway with relatively more considerable variability and from Shanghai with relatively more minor variability are adopted as case studies to illustrate the proposed probabilistic analysis framework. The results show that the boreholes with larger GUI values and closer to tunnel locations have a greater weight to affect the embedded tunnel structural performance in uncertain geological strata.

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“…Generally, the hydraulic parameters play an indispensable role in the seepage analysis and slope stability analysis, and thus it is necessary to take the hydraulic parameters into account in the slope stability prediction of embankments. Benefited from the rapid development of artificial intelligence, many machine learning algorithms have been proposed, and they are served as a promising tool for tackling geotechnical-related topics, such as tunnels (Zheng et al, 2019;Zhang et al, 2020;Zhu et al, 2021), embankments (Wang et al, 2020a,b), landslides (Huang et al, 2020;Wang H. et al, 2021;Liu et al, 2021;Xiao et al, 2021), and other issues (Atangana Njock et al, 2021;Jamei et al, 2021;Shen et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Efficient Seismic Stability Analysis of Embankment Slopes Subjected to Water Level Changes Using Gradient Boosting Algorithms

Zhang

Wang

et al. 2021

Front. Earth Sci.

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Embankments are widespread throughout the world and their safety under seismic conditions is a primary concern in the geotechnical engineering community since the failure events may lead to disastrous consequences. This study proposes an efficient seismic slope stability analysis approach by introducing advanced gradient boosting algorithms, namely Categorical Boosting (CatBoost), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). A database consisting of 600 datasets is prepared for model calibration and evaluation, where the factor of safety (FS) is regarded as the output and four influential factors are selected as the inputs. For each dataset, the FS corresponding to the four inputs is evaluated using the commercial geotechnical software of Slide2. As an illustration, the proposed approach is applied to the seismic stability analysis of a hypothetical embankment example subjected to water level changes. For comparison, the predictive performance of CatBoost, LightGBM, and XGBoost is investigated. Moreover, the Shapley additive explanations (SHAP) method is used in this study to explore the relative importance of the four features. Results show that all the three gradient boosting algorithms (i.e., CatBoost, LightGBM, and XGBoost) perform well in the prediction of FS for both the training dataset and testing dataset. Among the four influencing factors, the friction angle φ is the most important feature variable, followed by horizontal seismic coefficient Kh, cohesion c, and saturated permeability ks.

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Tunneling and deep excavations in spatially variable soil and rock masses: A short review

Cited by 56 publications

References 137 publications

Automated rock mass condition assessment during TBM tunnel excavation using deep learning

Automated rock mass condition assessment during TBM tunnel excavation using deep learning

Quantitative evaluation of geological uncertainty and its influence on tunnel structural performance using improved coupled Markov chain

Efficient Seismic Stability Analysis of Embankment Slopes Subjected to Water Level Changes Using Gradient Boosting Algorithms

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