Feedback on a shared big dataset for intelligent TBM Part II: Application and forward look

Li, Jianbin; Chen, Zuyu; Li, Xu; Jing, Lei; Zhang, Yunpei; Xiao, Hao-Han; Wang, Shuang-Jing; Yang, Wenyu; Wu, Lei-jie; Li, Pengyu; Li, Haibo; Yao, Min; Fan, Li-Tao

doi:10.1016/j.undsp.2023.01.002

Cited by 22 publications

(3 citation statements)

References 55 publications

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“…The average overburden depth of the tunnel is between 50 and 100 m, with a maximum overburden depth of 260 m. From the Fengman reservoir to the Yinma River, three river valleys divide the 72.1 km line into three mountain sections of nearly equal length. Each mountain section was excavated by a separating TBM 39 , 40 .…”

Section: Tbm Construction Data In Yinsong Diversion Project (Ysp)mentioning

confidence: 99%

Rock fragmentation indexes reflecting rock mass quality based on real-time data of TBM tunnelling

Wang

et al. 2023

Sci Rep

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Perception of rock condition (RC) is a challenge in tunnel boring machine (TBM) construction due to lack of space and time to observe and detect RC. To overcome this problem, this study aims to extract a new rock fragmentation index (RFI) that can reflect RC from real-time rock fragmentation data of the TBM. First, a comprehensive review of existing rock fragmentation models is conducted, which leads to some candidate RFIs that can reflect RC. Next, these candidate RFIs are investigated using data from 12,237 samples from a well-monitored tunnel boring process of the TBM in a 20,198 m tunnel. Further, a new RFI system is recommended as the parameter involving the optimal models. Finally, a preliminary study of the relationship between these RFIs and RC is carried out, and it is shown that these RFIs can reflect RC to a large extent. In the TBM boring process, these RFIs can be extracted from real-time TBM fragmentation data and used to predict the RC in the field. Therefore, the challenge of RC perception is solved with this new RFI system. The new RFI system offers significant potential for the real-time rock classification, prediction of the surrounding rock collapse potential, and selection of control parameters or support measures during TBM construction. This will be the key to improving TBM construction performance.

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Section: Tbm Construction Data In Yinsong Diversion Project (Ysp)mentioning

confidence: 99%

Rock fragmentation indexes reflecting rock mass quality based on real-time data of TBM tunnelling

Wang

et al. 2023

Sci Rep

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“…Consequently, DCS prediction for rocks under freeze-thaw cycles still necessitates time-consuming indoor experiments.Currently, the advent of artificial intelligence has revolutionized the data analysis for DCS of rocks under freeze-thaw cycles. With its formidable self-learning and data processing capabilities, it effectively compensates for the inherent limitations of traditional technologies when solving rock mechanics problems and employing numerical methods [31,32] . Machine learning has found widespread application in rock mechanics engineering [33,34] , encompassing rock mechanics property prediction [35] , surrounding rock classification [36] , flying rock prediction [37] , rock burst prediction [38] , rock fracture extraction [39] , and more.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles

Lv,

Zhang,

Zhang

et al. 2024

IOP Conf. Ser.: Earth Environ. Sci.

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The combined impact of freeze-thaw cycles and dynamic loads significantly influences the long-term durability of rock engineering in high-cold regions. Consequently, investigating the dynamic compressive strength (DCS) of rocks subjected to freeze-thaw cycles has emerged as a crucial area of scientific research to advance rock engineering construction in cold regions. Presently, the determination of the DCS of rocks under freeze-thaw cycles primarily relies on indoor experiments. However, this approach has faced criticism due to its drawbacks, including prolonged duration, high costs, and reliance on rock samples. To address these limitations, the exploration of using artificial intelligence technology to develop more accurate and convenient DCS prediction models for rocks under freeze-thaw cycles is a promising attempt. In this context, this paper introduces a DCS prediction model for rocks under freeze-thaw cycles, which integrates the Sparrow Search Algorithm (SSA) with Random Forest (RF). Firstly, employing a dataset of 216 samples, Principal Component Analysis (PCA) is utilized to reduce the dimensionality of ten influential factors. Subsequently, five optimization algorithms are employed to optimize the hyperparameters of both the BP and RF algorithms. Finally, a comprehensive evaluation and comparative analysis are carried out to assess the predictive performance of the optimized model, using evaluation metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R2).The research findings demonstrate that the SSA-RF model exhibits the best predictive performance, surpassing the other nine models in terms of generalization. The prediction model proposed in this study has good applicability for predicting DCS of freeze-thaw rock in cold regions, and also provides new ideas for the combination of machine learning and rock mass engineering in cold regions.

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“…The total thrust is another key indicator that directly reflects the shield movement performance [24][25][26][27][28][29]. Gao et al [30] used LSTM and GRU to predict six parameters of tunnel boring machines, including torque, thrust, etc.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Multi-Ring Shield Movement Performance Prediction and Control Method

Zeng,

Yu,

Chen

et al. 2024

Applied Sciences

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Accurate control of the shield attitude can ensure precise tunnel excavation and minimize impact on the surrounding areas. However, neglecting the total thrust force may cause excessive disturbance to the strata, leading to collapse. This study proposes a Bayesian optimization-based temporal attention long short-term memory model (BOTA-LSTM) for multi-objective prediction and control of shield tunneling, including shield attitude and total thrust. The model can achieve multi-ring predictions of shield attitude and total thrust by allocating larger weights to significant moments through a temporal attention mechanism. The hyperparameters of the proposed model are automatically selected through Bayesian hyperparameter optimization, which can effectively address the issue of complex parameter selection and optimization difficulties in multi-ring, multi-objective tasks. Based on the predictive results of the optimal model, an intelligent control method that considers both shield attitude and total thrust is proposed. Compared to a method that solely predicts and corrects for the next ring, the proposed multi-ring correction method provides the opportunity for further adjustments, if the initial correction falls short of expectations. A shield tunneling project in Hangzhou is used to demonstrate the effectiveness of the proposed model. The results show that the BOTA-LSTM model outperforms the models without the integration of a temporal attention mechanism and Bayesian hyperparameter optimization. The proposed multi-ring intelligent correction method can adjust the shield attitude and total thrust to a reasonable range, providing references for practical engineering applications.

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Feedback on a shared big dataset for intelligent TBM Part II: Application and forward look

Cited by 22 publications

References 55 publications

Rock fragmentation indexes reflecting rock mass quality based on real-time data of TBM tunnelling

Rock fragmentation indexes reflecting rock mass quality based on real-time data of TBM tunnelling

Machine Learning Algorithms in Rock Strength Prediction: A Novel Method for Evaluating Dynamic Compressive Strength of Rocks Under Freeze-Thaw Cycles

An Intelligent Multi-Ring Shield Movement Performance Prediction and Control Method

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