Evaluation of the geological condition ahead of the tunnel face by geostatistical techniques using TBM driving data

Yamamoto, Takuji; Shirasagi, Suguru; Yamamoto, Shinya; Mito, Yoshitada; Aoki, Kenji

doi:10.1016/s0886-7798(03)00030-0

Cited by 42 publications

(3 citation statements)

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“…Over the past two decades, the development of data-driven geologic inference systems has made significant progress. Initially, geostatistical methods were utilized to convert drill energy coefficients into rock mass strength and model spatial distribution (Yamamoto et al, 2003). The subsequent development involved probabilistic graph modeling for the classification of ahead geologic conditions, including rock, soil, or mixed, based on limited TBM operation features (Sousa and Einstein, 2012).…”

Section: Perceivingmentioning

confidence: 99%

Shaping the future of tunneling with data and emerging technologies

Apoji,

Sheil,

Soga

2023

DCE

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The increase in global population and urbanization is presenting significant challenges to society: space is becoming increasingly scarce, demand is exceeding capacity for deteriorating infrastructure, transportation is fraught with congestion, and environmental impacts are accelerating. Underground space, and particularly tunnels, has a key role to play in tackling these challenges. However, the cost, risk, uncertainty, and complexity of the tunneling process have impeded its growth. In this paper, we envision several technological advancements that can potentially innovate and transform the mechanized tunneling industry, including artificial intelligence (AI), autonomous, and bio-inspired systems. The proliferation of AI may assist human engineers and operators in making informed decisions systematically and quantitatively based on massive real-time data during tunneling. Autonomous tunneling systems may enable precise and predictable tunneling operations with minimal human intervention and facilitate the construction of massive and large-scale underground infrastructure projects that were previously challenging or unfeasible using conventional methods. Bio-inspired systems may provide valuable references and strategies for more efficient tunneling design and construction concepts. While these technological advancements can offer great promise, they also face considerable challenges, such as improving accessibility to and shareability of tunneling data, developing robust, reliable, and explainable machine learning systems, as well as scaling the mechanics and ensuring the applicability of bio-inspired systems from the prototype level to real-world applications. Addressing these challenges is imperative to ensure the successful implementation of these innovations for future tunneling.

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Section: Perceivingmentioning

confidence: 99%

Shaping the future of tunneling with data and emerging technologies

Apoji,

Sheil,

Soga

2023

DCE

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“…In order to explore the surrounding rock conditions, the current main methods is the physical survey method, Yokota, Y., et al [7] using geological radar to detect the broken strata in front of the palm face, Liu, B., et al [8] used the excitation source deployed in the shield and cutter head to carry out the induced polarization, Yamamoto, T., et al [9] used the geological advanced drilling rig deployed behind the shield to carry out the advanced drilling, so as to achieve the detection of geological characteristics in the front section of the tunnel. In practical engineering, the most important method is to divide the rock mass into grade I to grade V [10] from good to bad based on the integrity, strength, structural plane state, groundwater and other factors of the exposed surrounding rock.…”

Section: Introductionmentioning

confidence: 99%

Prediction of incompetent rock mass based on characteristic parameters and neural network

Gong,

Zhang,

Liu

et al. 2023

Fourth International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2023)

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TBM is famous for its safety and efficiency of tunnel boring, but TBM is also easy to encounter geological disasters in incompetent rock mass with its poor geological adaptability. Unfortunately, due to the shelter from cutterhead and shield, it’s hard to diagnose and identify the quality of rock mass. Based on this, this paper uses the torque penetration index TPI to predict incompetent rock mass, the main steps can be summarized as follows: firstly, we use linear fitting to calculate TPI, secondly, build the prediction model respectively with II-III grade of surrounding rocks and IV-V grade of surrounding rocks based on neural network, thirdly, predict the current TPI by the TPI of last boring cycles, finally, calculate the predicted torque based on the penetration and predicted TPI, then the incompetent rock mass is quickly identified through the error analysis between actual torque and predicted torque

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“…But for fine washout zone survey, especially for the long-distance measuring the occurrence and the effect of the coal seam washout zone, there is no real method. Now the multi-wave seismograph technology [5,6,7] is a new geological method for detecting the washout zone as its successful application in coal mines [8,9].…”

Section: Introductionmentioning

confidence: 99%

Application of the Mine Multi-Wave Seismograph in the Measurement of Coal Seam Washout Zone

Kang

et al. 2011

AMM

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In order to avoid the useless tunneling and the low mining rate, the mine seismograph measurement new technology is applied to measure the coal seam washout zone with long distance and high resolution. Based on the analyzing geophysics characteristic of the coal seam washout zone, this paper investigates the characteristics of time-distance curve of the measurement technology, and studies the application example of detecting the development and boundary scope of the old river-seam coal seam washout zone long-distance ahead of tunnel. The result shows that the measurement of the development and the boundary scope of the coal seam washout zone by using the mine multi-wave seismograph meet the practical requirement with small error and being consistent with the actual conditions during the process of mining.

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Evaluation of the geological condition ahead of the tunnel face by geostatistical techniques using TBM driving data

Cited by 42 publications

References 1 publication

Shaping the future of tunneling with data and emerging technologies

Shaping the future of tunneling with data and emerging technologies

Prediction of incompetent rock mass based on characteristic parameters and neural network

Application of the Mine Multi-Wave Seismograph in the Measurement of Coal Seam Washout Zone

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