On the mutual relations of drill monitoring variables and the drill control system in tunneling operations

Navarro, Justo García; Sanchidrián, José A.; Segarra, Pablo; Castedo, Ricardo; Paredes, Carlos; López, Lina M.

doi:10.1016/j.tust.2017.10.011

Cited by 26 publications

(13 citation statements)

References 9 publications

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“…This process is commonly used to remove variations in the data caused by differences in rig type, drilling conditions, and other factors, allowing for more accurate analysis and interpretation of the data. Accuracy improvement, providing comparable conditions, sensitivity analysis, and more visualized insights are some of the potential benefits of normalizing MWD data (e.g., [ 4 , 10 , 35 ]). The result of depth-based normalization for single and peer group holes in terms of raw records (black dots), normalized data after removing the hole depth dependency (green dots), and adopted regressions of each MWD parameter for each rod length (red lines) are presented in Figure 8 and Figure 9 .…”

Section: Methodsmentioning

confidence: 99%

“…However, the choice of filtering technique has a close dependency on the specific application and the characteristics of the MWD data being analyzed [40]. To avoid manual annotating and ensure sustaining the important data during the process, the recommended guidelines by [10] in terms of different combinations of MWD parameters were followed and programmed via Like any measurement system, MWD tools are not perfect and may have inherent measurement errors. Embedded sensors near the drill bit typically acquire MWD data, but records can be noisy due to various factors like drilling environment, tool wear, and signal interference.…”

Section: Normalizing Procedures In Block Bmentioning

confidence: 99%

“…Currently, organization and interpretation of the collected MWD data have successfully been applied on geo-infrastructures in several countries like Sweden [ 4 , 6 ], USA [ 7 ], Norway [ 8 , 9 ], Spain [ 10 ], Canada [ 11 , 12 ], and Russia [ 13 ]. Figure 1 shows the increased cumulative trend of the geoengineering application of MWD data in recent years.…”

Section: Introductionmentioning

confidence: 99%

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Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database

Abbaszadeh Shahri,

Shan,

Larsson

et al. 2024

Sensors

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In the context of geo-infrastructures and specifically tunneling projects, analyzing the large-scale sensor-based measurement-while-drilling (MWD) data plays a pivotal role in assessing rock engineering conditions. However, handling the big MWD data due to multiform stacking is a time-consuming and challenging task. Extracting valuable insights and improving the accuracy of geoengineering interpretations from MWD data necessitates a combination of domain expertise and data science skills in an iterative process. To address these challenges and efficiently normalize and filter out noisy data, an automated processing approach integrating the stepwise technique, mode, and percentile gate bands for both single and peer group-based holes was developed. Subsequently, the mathematical concept of a novel normalizing index for classifying such big datasets was also presented. The visualized results from different geo-infrastructure datasets in Sweden indicated that outliers and noisy data can more efficiently be eliminated using single hole-based normalizing. Additionally, a relational unified PostgreSQL database was created to store and automatically transfer the processed and raw MWD as well as real time grouting data that offers a cost effective and efficient data extraction tool. The generated database is expected to facilitate in-depth investigations and enable application of the artificial intelligence (AI) techniques to predict rock quality conditions and design appropriate support systems based on MWD data.

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Section: Methodsmentioning

confidence: 99%

Section: Normalizing Procedures In Block Bmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database

Abbaszadeh Shahri,

Shan,

Larsson

et al. 2024

Sensors

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“…The geological conditions can be characterized by surrounding rock classification [ 4 ]. The driving parameters include impact pressure, feed pressure, rotational pressure, water pressure, and water flow [ 5 ]. The influence of driving parameters on feed speed is reflected in two aspects.…”

Section: Introductionmentioning

confidence: 99%

“…The solution of the evaluation function is mainly based on the model-driven method. Concretely, a mathematical model was constructed according to prior knowledge, such as the principles of the engineering problem and data distribution characteristics, and experimental data were used to fit the coefficients [ 5 ]. However, the difficulty of constructing a mathematical model lies in the fact that the considerable and interrelated domain knowledge of engineering problems makes it hard to determine the main parameters and the form of the model [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Driving Parameters of the Jumbo Drill Efficiently with XGBoost-DRWIACO Framework: Applied to Increase the Feed Speed

Guo,

Lin,

et al. 2024

Sensors

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The jumbo drill is a commonly used driving equipment in tunnel engineering. One of the key decision-making issues for reducing tunnel construction costs is to optimize the main driving parameters to increase the feed speed of the jumbo drill. The optimization of the driving parameters is supposed to meet the requirements of high reliability and efficiency due to the high risk and complex working conditions in tunnel engineering. The flaws of the existing optimization algorithms for driving parameter optimization lie in the low accuracy of the evaluation functions under complex working conditions and the low efficiency of the algorithms. To address the above problems, a driving parameter optimization method based on the XGBoost-DRWIACO framework with high accuracy and efficiency is proposed. A data-driven prediction model for feed speed based on XGBoost is established as the evaluation function, which has high accuracy under complex working conditions and ensures the high reliability of the optimized results. Meanwhile, an improved ant colony algorithm based on dimension reduction while iterating strategy (DRWIACO) is proposed. DRWIACO is supposed to improve efficiency by resolving inefficient iterations of the ant colony algorithm (ACO), which is manifested as falling into local optimum, converging slowly and converging with a slight fluctuation in a certain dimension. Experimental results show that the error by the proposed framework is less than 10%, and the efficiency is increased by over 30% compared with the comparison methods, which meets the requirements of high reliability and efficiency for tunnel construction. More importantly, the construction cost is reduced by 19% compared with the actual feed speed, which improves the economic benefits.

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Estimation of tunnel support pattern selection using artificial neural network

et al. 2020

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On the mutual relations of drill monitoring variables and the drill control system in tunneling operations

Cited by 26 publications

References 9 publications

Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database

Normalizing Large Scale Sensor-Based MWD Data: An Automated Method toward A Unified Database

Optimizing Driving Parameters of the Jumbo Drill Efficiently with XGBoost-DRWIACO Framework: Applied to Increase the Feed Speed

Estimation of tunnel support pattern selection using artificial neural network

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