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Performance Comparison of Traffic-Dependent Displacement Estimation Model of Gwangan Bridge by Improvement Technique

개선 기법에 따른 광안대교의 교통량 의존 변위 추정 모델 성능 비교

  • 김수용 (부경대학교 토목공학과) ;
  • 신성우 (부경대학교 안전공학과) ;
  • 박지현 (부산시설공단 기술혁신팀)
  • Received : 2019.03.25
  • Accepted : 2019.05.27
  • Published : 2019.07.01

Abstract

In this study, based on the correlation between traffic volume data and vertical displacement data developed in previous research using the bridge maintenance big data of 2006, the vertical displacement estimation model using the traffic volume data of Gwangan Bridge for 10 years A comparison of the performance of the developed model with the current applicability is presented. The present applicability of the developed model is analyzed that the estimated displacement is similar to the actual displacement and that the displacement estimation performance of the model based on the structured regression analysis and the principal component analysis is not significantly different from each other. In conclusion, the vertical displacement estimation model using the traffic volume data developed by this study can be effectively used for the analysis of the behavior according to the traffic load of Gwangan Bridge.

Acknowledgement

Supported by : 부경대학교

References

  1. Bae, D. B., and Hwang, E. S. (2004), Fatigue Load Model for the Design of Steel Bridges, Journal of the Korean Society of Civil Engineers, 24(1A), 225-232(in Korean).
  2. Chang, S. J., and Kim, N. S. (2010), Applications of Displacement Response Estimation Algorithm Using Mode Decomposition Technique to Existing Bridges. Journal of the Korean Society of Civil Engineers, 30(3A), 257-264(in Korean).
  3. Chi, S. H. (2016), Big Data Analysis of Unstructured Documents and Video Images in the Construction Industry, Magazine of Korean Society of Civil Engineers, 64(8), 15-18 (in Korean).
  4. Friedman, T. L., and Mandelbaum, M. (2011), That Used to Be Us, 21C Book Inc., Korea (in Korean).
  5. Gonzalez, A. (2010), Development of a Bridge Weigh-In-Motion System: A technology to convert the bridge response to the passage of traffic into data on vehicle configurations, speeds, times of travel and weights, Lambert Academic Publishing.
  6. Huh, M. H. (2008), SPSS statistics data validation, neural networks & PLS regression, Datasolution Inc., Korea (in Korean).
  7. Kaiser, H. F. (1974), An index of factorial simplicity, Psychometrika, 39(1), 31-36. https://doi.org/10.1007/BF02291575
  8. Khanh, H. D. (2014), Waste Problems in Vietnam Construction Industry Based on Lean Philosophy, Doctoral Thesis, Pukyong National University.
  9. Kim, D. C. (2016), Big Data Reference Case of Civil and Construction Industry, Magazine of Korean Society of Civil Engineers, 64(8), 19-23(in Korean).
  10. Kim, H. J., Yoon, J. G., Lee, J. H., and Chang, S. P. (2005), Analysis of Long-term Monitoring Results of a Cable-Stayed Bridge using ARX model, Proceedings of KSCE 2005 Annual Conference, 928-931(in Korean).
  11. Kim, Y. C., Yoo, W. S., and Shin, Y. S. (2017), Application of Artificial Neural Networks to Prediction of Construction Safety Accidents, Journal of the Korean Society of Hazard Mitigation, 17(1), 7-14(in Korean). https://doi.org/10.9798/KOSHAM.2017.17.1.7
  12. Oh, S. H. (2009), An Analysis of Noise Robustness for Multilayer Perceptrons and Its Improvements, Journal of the Korea Contents Association, 9(1), 159-166(in Korean). https://doi.org/10.5392/JKCA.2009.9.1.159
  13. Park, J. C. (2015), Evaluation of Thermal Movements of a Cable-Stayed Bridge Using Temperatures and Displacements Data, Journal of the Korean Society of Civil Engineers, 35(4), 779-789(in Korean). https://doi.org/10.12652/Ksce.2015.35.4.0779
  14. Park, J. C., Park, C. M., and Song, P. Y. (2004), Evaluation of Structural Behaviors Using Full Scale Measurements on the Seo Hae Cable-Stayed Bridge, Journal of the Korean Society of Civil Engineers, 24(2A), 249-257(in Korean).
  15. Park, J. H. (2015), The optimum design of expansion joints by long-term monitoring data for the Diamond Bridge, Master Thesis, Pukyong National University(in Korean).
  16. Park, J. H., and Kim. S. Y. (2017), Analysis of Suspension Bridge Reinforced Truss Strain by Traffic, 2017 Proceedings of KSMI Annual Conference, 357-358(in Korean).
  17. Park, J. H., Shin, S. W., and Kim, S. Y. (2018), Modeling on Expansion Behavior of Gwangan Bridge using Machine Learning Techniques and Structural Monitoring Data, Journal of the Korean Society of Safety, 33(6), 42-49(in Korean). https://doi.org/10.14346/JKOSOS.2018.33.6.42
  18. Park, J. H., Shin, S. W. and Kim, S. Y. (2018), Traffic Volume Dependent Displacement Estimation Model for Gwangan Bridge Using Monitoring Big Data, Journal of the Korean Society of Civil Engineers, 38(2), 183-191(in Korean). https://doi.org/10.12652/Ksce.2018.38.2.0183
  19. Park, J. S., Ro, S. K., Park, J. H., Nam, S. S., and Moon, D. J. (2013), Correlation Analysis between Deflection and Temperature in suspension bridge using GNSS and Laser Displacement Sensor, Proceedings of KSMI 2013 Spring Conference, 375-379(in Korean).
  20. Shin, D. H. (2016), How the Construction Industry Should Leap in the Big Data Era, Magazine of Korean Society of Civil Engineers, 64(8), 10-14(in Korean).
  21. Song, J. J. (2016), SPSS/AMOS statistical analysis method for paper writing, 21C Book Inc., Korea(in Korean).
  22. Sousa, H., Zavitsas, K., Polak, J., and Chryssanthopoulos, M. (2014), Inferring Asset Live Load Distributions from Traffic Flow Data: A New SHM Opportunity?, EWSHM-7th European Workshop on Structural Health Monitoring., Nantes, France, 435-442
  23. Won, T. Y., and Jeong, S. W. (2015), Statistical Analysis - SPSS 18.0, Hannarae Book Inc., Korea(in Korean).
  24. Yang, Y. B., Yau, J. D. and Wu, Y. S. (2004), Vehicle-Bridge Interaction Dynamics, World Scientific Publishing Co., New Jersey, USA.
  25. Yoo, J. I. (2008), Constructs and Application of Artificial Neural Network, 2008 Annual Conference of Korean Psychological Association, 2-3(in Korean).
  26. Zhou, Y., and Chen, S. (2017), Dynamic Assessment of Bridge Deck Performance Considering Realistic Bridge-Traffic Interaction, No. MPC 17-333, North Dakota State University - Upper Great Plains Transportation Institute, Fargo: Mountain-Plains Consortium, North Dakota, USA.