Feedback on a shared big dataset for intelligent TBM Part I: Feature extraction and machine learning methods

Li, Jianbin; Chen, Zuyu; Xu, Li Da; Jing, Lei; Zhangf, Yun-Pei; 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.2023.01.001

Cited by 36 publications

(6 citation statements)

References 78 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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“…Secondly, the high integration level of TBM processes necessitates advanced technical skills and experience from operators, where improper handling could not only impair boring efficiency but also risk mechanical failures or accidents. Consequently, many researchers are now turning their attention to the development of automated and intelligent technologies for TBM operations [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Semi-Symmetrical, Fully Convolutional Masked Autoencoder for TBM Muck Image Segmentation

Lei,

Tan,

Wang

et al. 2024

Symmetry

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Deep neural networks are effectively utilized for the instance segmentation of muck images from tunnel boring machines (TBMs), providing real-time insights into the surrounding rock condition. However, the high cost of obtaining quality labeled data limits the widespread application of this method. Addressing this challenge, this study presents a semi-symmetrical, fully convolutional masked autoencoder designed for self-supervised pre-training on extensive unlabeled muck image datasets. The model features a four-tier sparse encoder for down-sampling and a two-tier sparse decoder for up-sampling, connected via a conventional convolutional neck, forming a semi-symmetrical structure. This design enhances the model’s ability to capture essential low-level features, including geometric shapes and object boundaries. Additionally, to circumvent the trivial solutions in pixel regression that the original masked autoencoder faced, Histogram of Oriented Gradients (HOG) descriptors and Laplacian features have been integrated as novel self-supervision targets. Testing shows that the proposed model can effectively discern essential features of muck images in self-supervised training. When applied to subsequent end-to-end training tasks, it enhances the model’s performance, increasing the prediction accuracy of Intersection over Union (IoU) for muck boundaries and regions by 5.9% and 2.4%, respectively, outperforming the enhancements made by the original masked autoencoder.

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Feedback on a shared big dataset for intelligent TBM Part I: Feature extraction and machine learning methods

Cited by 36 publications

References 78 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

Semi-Symmetrical, Fully Convolutional Masked Autoencoder for TBM Muck Image Segmentation

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