Nonstructural Influence Factors of Dynamic Load Allowance for Concrete Beam Bridges
- 38 Downloads
Studies show that nonstructural parameters, such as pavement conditions or load patterns, have greater influences on the dynamic load allowance (DLA) of bridges than structural parameters. For pavement roughness effects, the values of DLA caused by roughness profiles are calculated by a self-compiled program. The results showed that the values of DLA are discrete even if they are caused by roughness profiles that belong to the same power spectral density (PSD) grade. The PSD grade method for pavement conditions has limitations when it is used in the analysis of DLA. Statistical analysis was also carried out on these DLA results. The statistical analysis indicated that the values of DLA followed a normal distribution when they were excited by roughness profiles that belong to the same grade. For the influence of vehicle string loads on DLA, an improved optimization approach based on a genetic algorithm for the largest DLA is presented. A new method is used to calculate the fitness value in the genetic algorithm (GA) method, which could substantially reduce calculation time. The new approach is able to obtain the most unfavorable arrangement of the vehicle string and estimate the largest DLA caused by it.
Keywordsdynamic load allowance vehicle-bridge interaction pavement roughness grade probability distribution vehicle string load genetic algorithm
Unable to display preview. Download preview PDF.
- Dang, D. (2012). Study on the highway bridge design load and combination effect, PhD Dissertation, Chang’An University, Xi’an, China.Google Scholar
- Gao, Q. F., Wang, Z. L., Koh, C.G., and Chen, C. (2015). “Dynamic load allowances corresponding to different responses in various sections of highway bridges to moving vehicular loads.” Advances in Structural Engineering, Vol. 18 No. 10, 2015, pp. 1685–1701, DOI: 10.1260/1369-4318.104.22.1685.CrossRefGoogle Scholar
- GB/T7031-2005 (2005). Mechanical vibration-road surface profiles - Reporting of measured data, AQSIQ, Beijing, China.Google Scholar
- JTG D60-2015 (2015). General code for design of highway bridges and culverts. MOT, Beijing, China.Google Scholar
- Li, W. Z. (2012). Research on working performance evaluation methods for highway concrete girder bridges based on dynamic testing, PhD Dissertation, Harbin Institute of Technology, Harbin, China.Google Scholar
- Liu, B. (2015). Research on generation mechanism of impact coefficient and reliability assessment of highway bridges in over size transport, PhD Dissertation, Shandong University, Jinan, China.Google Scholar
- Silva, M., Santos, A., Figueiredo, E., Santos, R., Sales, C., and Costa, C. (2016). “A novel unsupervised approach based on a genetic algorithm for structural damage detection in bridges.” Engineering Applications of Artificial Intelligence, Vol. 52, No. 6, pp. 168–180, DOI: 10.1016/j.engappai.2016.03.002.CrossRefGoogle Scholar
- Song, Y. F. (2000). Highway bridge dynamics, China Communication Press, Beijing, China.Google Scholar
- Tang, H. H. (2009). Research on optimization method and application of continuous bridges based on genetic algorithm, PhD Dissertation, Harbin Institute of Technology, Harbin, China.Google Scholar
- Yang, J. R. (2007). Local dynamic response of highway bridges to moving vehicles, PhD Dissertation, Tongji University, Shanghai, China.Google Scholar