Journal of Information Systems Engineering and Management

Journal of Information Systems Engineering and Management, 2024 - Volume 9 Issue 4, Article No: 27811
https://doi.org/10.55267/iadt.07.15152

Published Online: 11 Sep 2024

Views: 236 | Downloads: 97

Tunnel misalignments compromise safety and efficiency in transportation and utilities. Visual inspection is imprecise, such as laser scanning and digital image correlation are required that lacks efficacy and stakeholder perception study like stakeholder perceptions. Check out these techniques towards stakeholder perspectives for project-specific features and user experiences, research can help improve tunnel engineering project decision-making, detection accuracy, and operational efficiency, hence ensuring tunnel infrastructure network reliability and safety. Tunnel segment misalignment detection, a major tunnel engineering difficulty, is researched to improve accuracy and efficiency. The main goals are detection method evaluation, stakeholder perspectives, and tunnel engineering insights. Mixed methods are employed for quantitative testing with different misalignment levels and qualitative tunnel builder interviews. Quantitative analysis examines visual inspection, laser scanning, total station, ultrasonic testing (UT), and digital image correlation (DIC). Low experimental % errors help laser scanning and DIC discover misalignments. UT is large, but total station and eye exam can detect smaller misalignments. The longest procedure studied is DIC. Qualitative stakeholder interviews enhance findings. Laser scanning is promising due to its accuracy and simplicity, yet cost and complexity persist. Visual inspection is simple yet subjective and error-prone. Qualitative insights help tunnel engineering project decision-making by revealing stakeholders' preferences and concerns as per stakeholder perspectives. The research has many effects that help to choose misalignment detection methods based on accuracy, usability, and cost. Qualitative stakeholder interviews inform training and equipment procurement for detection. This study exhibits misalignment detecting devices' performance and tunnel engineering benefits, offering practical applications for improving tunnel infrastructure detection accuracy, efficiency, safety, reliability, and user-friendly field technologies through qualitative analysis.

Tunnel Infrastructure Misalignments Detection Techniques Safety Operational Efficiency

• Ahmad, M., Hu, J. L., Hadzima-Nyarko, M., Ahmad, F., Tang, X. W., Rahman, Z. U., . . . Abrar, M. (2021). Rockburst hazard prediction in underground projects using two intelligent classification techniques: A comparative study. Symmetry, 13(4), 632.
• Aldibaja, M., Suganuma, N., Yoneda, K., & Yanase, R. (2022). Challenging environments for precise mapping using GNSS/INS-RTK systems: Reasons and analysis. Remote Sensing, 14(16). https://doi.org/10.3390/rs14164058
• Ali, L., Nawaz, A., Bai, Y., Raza, A., Anwar, M. K., Raheel Shah, S. A., & Raza, S. S. (2020). Numerical simulations of GFRP-Reinforced columns having polypropylene and polyvinyl alcohol fibers. Complexity, 2020. https://doi.org/10.1155/2020/8841795
• Al-khafaji, R. B. Y. (2024). Using Technology and Algorithms for Face Detection and Recognition Using Digital Image Processing and Relying on a Computer Vision Sensor. Journal of Information Systems Engineering and Management, 9(1), 25018. https://doi.org/10.55267/iadt.07.14328
• Ashcroft, B., Børresen, B., Mamia, F., & Labe, L. (2023). Condition assessment survey and repair work of the headrace tunnel for the Warangoi HPP. In Expanding underground-knowledge and passion to make a positive impact on the world (pp. 1745-1752). https://doi.org/10.1201/9781003348030-209
• Beirne, C., Jayakumaran, R., & Lee, L. S. (2023, November). Design considerations for an innovative precast lining for a mined roadheader tunnel in rock. In ATC 2023: Australian Tunnelling Conference: Conference Proceedings: Trends and Transitions in Tunnelling (pp. 477-486). Auckland, New Zealand: Engineers Australia.
• Braga, V. B. M., Zapico, J. C. S., Tyagi, V., & Bono, R. (2023). Challenges experienced during simultaneous backfill grouting using single shield large diameter TBM driven in a sharp negative slope. In Expanding underground-knowledge and passion to make a positive impact on the world (pp. 1175-1182). https://doi.org/10.1201/9781003348030-139
• Chai, S., Yan, Y., Hu, B., Wang, H., Hu, J., Chen, J., . . . Zhou, Y. (2023). The optimization of secondary lining construction time for shield tunnels based on longitudinal mechanical properties. Applied Sciences (Switzerland), 13(19). https://doi.org/10.3390/app131910772
• Chen, K. H., Zhang, Z., Liao, S. M., & Peng, F. L. (2016). Durability of joint components of shield tunnel under high water pressure in erosion environment. Procedia Engineering, 165, 282-289.
• Daoust, T., Pomerleau, F., & Barfoot, T. D. (2016, June). Light at the end of the tunnel: High-speed lidar-based train localization in challenging underground environments. In 2016 13th Conference on Computer and Robot Vision (CRV) (pp. 93-100). Piscataway, NJ: IEEE.
• Helmers, H., Wagner, L., Garza, C. E., Reichmuth, S. K., Oliva, E., Philipps, S. P., . . . Bett, A. W. (2015, May). Photovoltaic cells with increased voltage output for optical power supply of sensor electronics. In Proceedings of the AMA Conferences (Vol. 2015, pp. 519-524). https://doi.org/10.5162/sensor2015/d1.4
• Hu, M., Sun, J., Wu, B., Wu, H., & Xu, Z. (2024). Shield tunnel (segment) uplift prediction and control based on interpretable machine learning. Sustainability, 16(2), 910.
• Hu, M., Zhang, H., Wu, B., Li, G., & Zhou, L. (2022). Interpretable predictive model for shield attitude control performance based on XGboost and SHAP. Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-22948-w
• Huang, H., Cheng, W., Zhou, M., Chen, J., & Zhao, S. (2020). Towards automated 3d inspection of water leakages in shield tunnel linings using mobile laser scanning data. Sensors (Switzerland), 20(22). https://doi.org/10.3390/s20226669
• Ji, C., Su, X., Qin, Z., & Nawaz, A. (2022). Probability analysis of construction risk based on noisy-or gate bayesian networks. Reliability Engineering & System Safety, 217, 107974.
• Keil, J. (2019). Lattice design, commissioning and operation challenges for PETRA IV. Retrieved from https://bib-pubdb1.desy.de/record/452030/files/Workshop_Beam_Test_And_Comissioning_Proceedings-Keil.pdf
• Khokhar, C. S., Bansal, V., & Mishra, E. (2020). Cross passage construction methods for twin-tube tunnel projects in urban areas. Journal of Rock Mechanics and Tunnelling Technology (JRMTT), 26(1), 27-40.
• Koch, C., Georgieva, K., Kasireddy, V., Akinci, B., & Fieguth, P. (2015). A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure. Advanced Engineering Informatics, 29(2), 196-210.
• Liu, Y., Chen, H., Zhang, L., & Wang, X. (2021). Risk prediction and diagnosis of water seepage in operational shield tunnels based on random forest. Journal of Civil Engineering and Management, 27(7), 539-552.
• Ma, S. Y., Ye, X. W., Liu, Z. X., Ding, Y., Zhang, D., & Sun, F. (2024). Mechanical behavior monitoring and load inversion analysis of large-diameter underwater shield tunnel during construction. Sensors, 24(4). https://doi.org/10.3390/s24041310
• Nawaz, A., Su, X., & Nasir, I. M. (2021). BIM adoption and its impact on planning and scheduling influencing mega plan projects-(CPEC-) quantitative approach. Complexity, 2021. https://doi.org/10.1155/2021/8818296
• Qiu, D., Liang, H., Wang, Z., Tong, Y., & Wan, S. (2022). Hybrid-supervised-learning-based automatic image segmentation for water leakage in subway tunnels. Applied Sciences (Switzerland), 12(22). https://doi.org/10.3390/app122211799
• Singh, P., Mittal, R., Gupta, S. K., & Khan, A. H. (2023). Construction of platform tunnel by conventional tunneling below heritage buildings. In Expanding underground-knowledge and passion to make a positive impact on the world (pp. 2253-2259). https://doi.org/10.1201/9781003348030-271
• Tian, L., Li, Q., He, L., & Zhang, D. (2023). Image-range stitching and semantic-based crack detection methods for tunnel inspection vehicles. Remote Sensing, 15(21). https://doi.org/10.3390/rs15215158
• Valiante, N., Elgarhi, H., Lazzarin, F., Crapp, R., & Stucchi, R. (2023). Development of a tension-resistant single pass segmental lining in high pressure tunnels. The experience of Snowy 2.0 (Australia). In Expanding underground-knowledge and passion to make a positive impact on the world (pp. 1477-1485). https://doi.org/10.1201/9781003348030-176
• Wang, K., Li, P., Liu, Y., Li, H., Men, Y., & Huang, Y. (2021). Research on the present situation and development trend of subway tunnel inspection vehicle. IOP Conference Series: Materials Science and Engineering, 1203(2), 022126.
• Xiao, Z., Ma, C., Huang, J., Liang, L., Lu, W., Hong, K., . . . Bernholc, J. (2019). Design of atomically precise nanoscale negative differential resistance devices. Advanced Theory and Simulations, 2(2), 1800172.
• Xue, Y., Shi, P., Jia, F., & Huang, H. (2022). 3D reconstruction and automatic leakage defect quantification of metro tunnel based on SfM-Deep learning method. Underground Space (China), 7(3), 311-323.
• Zhang, Z., Zhu, X., & Wei, R. (2022). A model test on an open-cut tunnel structure under the effect of a stick-slip normal fault. Railway Sciences, 1(2), 169-192.
• Zhao, S., Shadabfar, M., Zhang, D., Chen, J., & Huang, H. (2021). Deep learning-based classification and instance segmentation of leakage-area and scaling images of shield tunnel linings. Structural Control and Health Monitoring, 28(6), e2732.
• Zhao, X., Han, K., Ju, J. W., Chen, X., Chen, W., & Xiong, H. (2023). Numerical analysis of size effect on the deformation behavior and damage evolution mechanism of segmental tunnel lining rings. International Journal of Damage Mechanics, 32(4), 600-622.

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