Smart Structures and Systems

  • 제34권1호
  • /
  • Pages.17-23
  • /
  • 2024
  • /
  • 1738-1584(pISSN)
  • /
  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Prediction of longitudinal wave speed in rock bolt coupled with Multilayer Neural Network (MNN) algorithm

  • Jung-Doung Yu (Department of Civil Engineering, Joongbu University) ;
  • Geunwoo Park (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Dong-Ju Kim (School of Civil, Environmental and Architectural Engineering, Korea University) ;
  • Hyung-Koo Yoon (Department of Construction and Disaster Prevention Engineering, Daejeon University)
  • 투고 : 2024.05.07
  • 심사 : 2024.08.20
  • 발행 : 2024.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

Non-destructive methods are extensively utilized for assessing the integrity of rock bolts, with longitudinal wave speed being a crucial property for evaluating rock bolt quality. This research aims to propose a method for predicting reliable longitudinal wave velocities by leveraging various properties of the rock surrounding the rock bolt. The prediction algorithm employed is the Multilayer Neural Network (MNN), and the input properties includes elastic modulus, shear wave speed, compressive strength, compressional wave speed, mass density, porosity, and Poisson's ratio, totaling seven. The implementation of the MNN demonstrates high reliability, achieving a coefficient of determination of 0.996. To assess the impact of each input property on longitudinal wave speed, an importance score is derived using the random forest algorithm, with the elastic modulus identified as having the most significant influence. When the elastic modulus is the sole input parameter, the coefficient of determination for predicting the longitudinal wave speed is observed to be 0.967. The findings of this study underscore the reliability of selecting specific properties for predicting longitudinal wave speed and suggest that these insights can assist in identifying relevant input properties for rock bolt integrity assessments in future construction site experiments.

키워드

과제정보

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2020R1A2C2012113 and 2021R1C1C2005687).

참고문헌

  1. Al-Swaidani, A.M., Meziab, A., Khwies, W.T., Al-Bali, M. and Lala, T. (2024), "Building MLR, ANN and FL models to predict the strength of problematic clayey soil stabilized with a combination of nano lime and nano pozzolan of natural sources for pavement construction", Int. J. Geo-Eng., 15(1), 2. https://doi.org/10.1186/s40703-023-00201-1
  2. Aparna, R.P. and Bindu, J. (2023), "Utilization of waste materials as a substitute for the sand drain in clayey soil", Int. J. Geo-Eng., 14(1), 2. https://doi.org/10.1186/s40703-022-00180-9
  3. Chen, P.C. and Lai, K.X. (2023), "Servo control strategy for uniaxial shake tables using long short-term memory networks", Smart Struct. Syst., Int. J., 32(6), 359-369. https://doi.org/10.12989/sss.2024.32.6.359
  4. Guzman, I.L. and Payano Jr, C. (2023), "Use of repurposed whole textile for enhancement of pavement soils", Int. J. Geo-Eng., 14(1), 12. https://doi.org/10.1186/s40703-023-00190-1
  5. Hong, W.T., Lee, J.S., Lee, D. and Yoon, H.K. (2022), "Estimation of bulk electrical conductivity in saline medium with contaminated lead solution through TDR coupled with machine learning", Process Safe. Environ. Protect., 161, 58-66. https://doi.org/10.1016/j.psep.2022.03.018
  6. Kim, S. and Yoon, H.K. (2023), "Application of classification coupled with PCA and SMOTE, for obtaining safety factor of landslide based on HRA", Bull. Eng. Geol. Environ., 82(10), 381. https://doi.org/10.1007/s10064-023-03403-0
  7. Lee, J.S., Park, J., Kim, J. and Yoon, H.K. (2022), "Study of oversampling algorithms for soil classifications by field velocity resistivity probe", Geomech. Eng., Int. J., 30(3), 247-258. https://doi.org/10.12989/gae.2022.30.3.247
  8. Liu, J., Tang, L., Li, D. and Shen, W. (2024), "Experimental and numerical validation of guided wave based on time-reversal for evaluating grouting defects of multi-interface sleeve", Smart Struct. Syst., Int. J., 33(1), 41-53. https://doi.org/10.12989/sss.2024.33.1.041
  9. Min, D.H. and Yoon, H.K. (2021), "Suggestion for a new deterministic model coupled with machine learning techniques for landslide susceptibility mapping", Scientif. Reports, 11(1), 1-24. https://doi.org/10.1038/s41598-021-86137-x
  10. Min, D.H., Kim, Y., Kim, S. and Yoon, H.K. (2023a), "Strategy of oversampling geotechnical parameters through geostatistical, SMOTE, and CTGAN methods for assessing susceptibility of landslide", Landslides, 1-17. https://doi.org/10.1007/s10346-023-02166-9
  11. Min, D.H., Taiwo, S.M., Park, J., Kim, S. and Yoon, H.K. (2023b), "Application of deterministic models for obtaining groundwater level distributions through outlier analysis", Geomech. Eng., Int. J., 35(5), 499-509. https://doi.org/10.12989/gae.2023.35.5.499
  12. Park, J., Lee, J.S. and Yoon, H.K. (2023), "Geoacoustic and geophysical data-driven seafloor sediment classification through machine learning algorithms with property-centered oversampling techniques", Comput.-Aided Civil Infrastr. Eng., 39(14), 2105-2121. https://doi.org/10.1111/mice.13126
  13. Samadi, H., Hassanpour, J. and Rostami, J. (2023), "Prediction of earth pressure balance for EPB-TBM using machine learning algorithms", Int. J. Geo-Eng., 14(1), 21. https://doi.org/10.1186/s40703-023-00198-7
  14. Tacim, G., Posluk, E. and Gokceoglu, C. (2023), "Importance of grouting for tunneling in karstic and complex environment (a case study from Turkiye)", Int. J. Geo-Eng., 14(1), 6. https://doi.org/10.1186/s40703-023-00183-0
  15. Yoon, H.K., Lee, J.S. and Yu, J.D. (2022), "Correlation of granite rock properties with longitudinal wave velocity in rock bolt", Int. J. Rock Mech. Min. Sci., 159, 105200. https://doi.org/10.1016/j.ijrmms.2022.105200
  16. Yu, J.D. and Yoon, H.K. (2024), "A study on influential rock properties for predicting the longitudinal wave velocity in a rock bolt: Numerical and machine learning approaches", Int. J. Rock Mech. Min. Sci., 179, 105788. https://doi.org/10.1016/j.ijrmms.2024.10578
  17. Yu, J.D., Lee, J.S. and Yoon, H.K. (2021), "Effects of rock weathering on guided wave propagation in rock bolts", Tunnell. Undergr. Space Technol., 115, 104069. https://doi.org/10.1016/j.tust.2021.104069
  18. Zar, A., Hussain, Z., Akbar, M., Tayeh, B.A. and Lin, Z. (2023), "A vibration-based approach for detecting arch dam damage using RBF neural networks and Jaya algorithms", Smart Struct. Syst., Int. J., 32(5), 319-338. https://doi.org/10.12989/sss.2023.32.5.319
  19. Zhao, Y., Moayedi, H., Foong, L.K. and Thi, Q.T. (2024), "Slime mold and four other nature-inspired optimization algorithms in analyzing the concrete compressive strength", Smart Struct. Syst., Int. J., 33(1), 65-91. https://doi.org/10.12989/sss.2024.33.1.065