Identification of a Distribution of Stiffness Reduction in Reinforced Concrete Slab Bridges Subjected to Moving Loads
- Authors
- Park, Taehyo; Noh, Myung-Hyun; Lee, Sang-Youl; Voyiadjis, George Z.
- Issue Date
- Sep-2009
- Publisher
- American Society of Civil Engineers
- Keywords
- Algorithms; Bridges, concrete; Damage; Dynamic loads; Finite element method; Noise; Stiffness
- Citation
- Journal of Bridge Engineering, v.14, no.5, pp 355 - 365
- Pages
- 11
- Indexed
- SCIE
SCOPUS
- Journal Title
- Journal of Bridge Engineering
- Volume
- 14
- Number
- 5
- Start Page
- 355
- End Page
- 365
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/176283
- DOI
- 10.1061/(ASCE)1084-0702(2009)14:5(355)
- ISSN
- 1084-0702
1943-5592
- Abstract
- The method for identifying arbitrary stiffness reduction in damaged reinforced concrete slab bridges under moving loads is proposed and dynamic signals measured at several points are used as response data to reflect the properties of the moving loads sensitivity. In particular, the change in stiffness in each element before and after damage, based on the system identification method, is described and discussed by using a modified bivariate Gaussian distribution function. The proposed method in this work is more feasible than the conventional element-based damage detection method from the computational efficiency because the procedure of finite-element analysis coupled with microgenetic algorithm using six unknown parameters irrespective of the number of elements are considered. The validity of the technique is numerically verified using a set of dynamic data obtained from a simulation of the actual bridge modeled with a three-dimensional solid element. The numerical calculations show that the proposed technique is a feasible and practical method that can prove the exact location of a damaged region as well as inspect the complex distribution of deteriorated stiffness, although there is a modeling error between actual bridge results and numerical model results as well as a measurement error like uncertain noise in the response data.
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