International Scientific Journal

Thermal Science - Online First

Authors of this Paper

External Links

online first only

Damage detection of long-span bridge structures based on response surface model

In order to solve the problem of high risk and low precision of existing damage detection methods for long-span Bridges, a new method based on fourth-order polynomial response surface model is proposed. Response surface model is constructed by using fourth order polynomial function. The parameters of the finite element model of the bridge are modified according to the response surface model. Based on the finite element model, the modal strain energy before and after the damage of the element was calculated, and the damage index of the element was obtained, so as to realize the damage detection of the long-span bridge structure. Experimental results show that the proposed method can accurately detect the damage location of long-span Bridges under different damage conditions, and the detection error of damage degree is less than 1%, which has a broad application prospect.
PAPER REVISED: 2019-08-30
PAPER ACCEPTED: 2019-09-03
  1. Song G Q, Liao Q, Zhang C, et al. Damage Identification of High Arch Dam Model Modification Based on Response Surface Theory. Journal of Hydropower, 2016, 35 (9): 87-94.
  2. Li L F, Li H H, Xu K D, et al. Bridge Seismic Vulnerability Analysis Based on Uniform Design Response Surface Method. Highway Transportation Science and Technology, 2017, 34 (11): 100-109.
  3. Yu, D., Zhu, H., Han, W., et al. Dynamic Multi Agent-based Management and Load Frequency Control of PV/Fuel cell/ Wind turbine/ CHP in Autonomous Microgrid System. Energy, 2019, 173: 554-568.
  4. Yang Y, Xiang C, Jiang M Z, et al. Bridge Damage Identification Method Considering Road Surface Roughness by Using Indirect Measurement Technique. China Journal of Highway and Transport, 019, 32 (01): 103-110+130.
  5. Liu H Q, Cao X Y, Fang Y W, et al. Finite Element Model Modification of Steel Box Girder Bridge Based on Response Surface Method. Journal of Wuhan University of Technology, 2016, 38 (7): 69-75.
  6. Han Y, Li J, Yang X, et al. Dynamic Prediction Research of Silicon Content in Hot Metal Driven by Big Data in Blast Furnace Smelting Process under Hadoop Cloud Platform. Complexity, 2018: 8079697.
  7. Liu K Y, Li H B. Reliability Prediction Simulation of Building Structure Damage Caused by Earthquake. Computer Simulation, 2017, 34 (1): 423-426.
  8. Guo Y D, Shi Y C, Xu Y J. Reliability and Risk Probability Prediction of Bridge Structures Based on Response Surface Method. Steel Structures, 2016, 31 (12): 33-36.
  9. Sohn H, Law K H. Bayesian Probabilistic Damage Detection of a Reinforced‐concrete Bridge Column. Earthquake Engineering & Structural Dynamics, 2015, 29 (8):1131-1152.
  10. Zhao B, Wu H. Pharmacological characteristics analysis of two molecular structures. Applied Mathematics & Nonlinear Sciences, 2017, 2: 93-110.
  11. Ma S B, Yu Q F, Wei K, et al. Structural Optimization of Asphalt Pavement Overhaul Based on Response Surface Method. Highway Engineering, 2015, 40 (3): 175-180.
  12. Fang S E, Zhang Q H, Lin Y Q, et al. Interval Response Surface Model Revision Method for Uncertainty Parameter Recognition. Journal of Vibration Engineering, 2015, 28 (1): 73-81.
  13. Klymchuk T. Regularizing Algorithm for Mixed Matrix Pencils. Applied Mathematics & Nonlinear Sciences, 2017, 2(1): 123-130.
  14. Qian J, Sun L M, Jiang Y. Advances in Acoustic Emission Monitoring of Bridge Cable Damage. Applied Acoustics, 2016, 35 (4): 369-376.
  15. Chu Y T, Li Y G, Xu Y R, et al. Lightweight Study of Connecting Rods Based on Response Surface Model. Journal of Plastic Engineering, 2016, 23 (5): 1-7.
  16. Ding Q S, Xiang H F. Full-order and Single-parameter Searching Analysis of Coupled Flutter Instability for Long-span Bridges. Canadian Journal of Civil Engineering, 2016, 44 (3): 192-200.