Linear and nonlinear model updating of reinforced concrete T-beam bridges using artificial neural networks

Date

2013-04-01

Author

Hasançebi, Oğuzhan

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

156
views

0
downloads

The key parameters affecting dynamic and static responses of structural systems often change during their life cycles due to aging, deterioration, damage and rehabilitation. Model updating is a major research field that investigates numerical methods to improve simulation ability of finite element (FE) models by identifying the modified parameters in structural systems based on data collected from field experiments and/or laboratory tests. In this paper, artificial neural networks (ANNs) are used to develop an efficient method for finite element (FE) model updating of reinforced concrete (RC) T-beam bridges. The FE model of a sample bridge selected from Pennsylvania's bridge population is calibrated using neural networks trained according to datasets generated from linear and non-linear analyses separately. The simulated responses obtained from calibrated FE models are compared to the field-measured responses of the bridge to quantify accuracy of parameter estimation and success of the model updating process. The present study evinces the fact that ANNs can still be used efficiently and reliably for parameter estimation tasks under a high level of uncertainty and complexity that arises from aging and deterioration of RC bridges as well as nonlinear material properties of concrete. The study also indicates significance of non-linear response analysis for parameter identification for RC bridges, and underlines that only consideration of dynamic responses for model updating may lead to erroneous parameter predictions especially when the calibration is based on linear bridge responses.

Subject Keywords

Structural identification, FE model updating and calibration, RC T-beam bridges, Linear and nonlinear structural analyses, Artificial neural networks, Structural analyses

URI

https://hdl.handle.net/11511/40934

Journal

COMPUTERS & STRUCTURES

DOI

https://doi.org/10.1016/j.compstruc.2012.12.017

Collections

Department of Civil Engineering, Article

Suggestions

OpenMETU
Core

Harmonic vibration analysis of large structures with local nonlinearity Abat, Diren; Özgüven, Hasan Nevzat; Department of Mechanical Engineering (2009) With the rapid development in today’s technology, reliability and performance requirements on components of various mechanical systems, which tend to be much lighter and work under much more severe working conditions, dramatically increased. In general, analysis techniques based on simplified model of structural components with linearity assumption may provide time saving for solutions with reasonable accuracy. However, since most engineering structures are often very complex and intrinsically nonlinear, in...
Drift spectra for inelastic shear frames Etemadi, Ali; Gülkan, Hakkı Polat; Department of Civil Engineering (2015) In assessing the damage originating from strong ground motions in building frames, it is necessary to identify properly the post-yield hysteresis degrading behavior of structural components that are well correlated with structural response and in turn, with damage. Likewise, structural damage during the ground motion is due to excessive interstory drift ratio; hence more realistic estimation of interstory drift demands has a significant role in the seismic evaluation of frame buildings. Existing approaches ...
Fragility based assessment of lowrise and midrise reinforced concrete frame buildings in Turkey Ay, Bekir Özer; Erberik, Murat Altuğ; Department of Civil Engineering (2006) In this study, structural vulnerability of reinforced concrete frame structures by considering the countryspecific characteristics is investigated to manage the earthquake risk and to develop strategies for disaster mitigation. Lowrise and midrise reinforced concrete structures, which constitute approximately 75% of the total building stock in Turkey, are focused in this fragilitybased assessment. The seismic design of 3, 5, 7 and 9story reinforced concrete frame structures are carried out according to the ...
Lifetime Performance Analysis of Existing Reinforced Concrete Bridges. I: Theory Akgül, Ferhat (American Society of Civil Engineers (ASCE), 2005-06-01) In this first part of a two-part paper, a general methodology for lifetime performance analysis of existing reinforced concrete bridges is presented. The framework for the methodology is established by identifying four distinct categories: limit state equations, random variables, deterministic parameters, and constant coefficients. The limit state equations are derived by strictly adhering to the load and capacity formulas and requirements set forth in AASHTO specifications. Generality is pursued by establi...
Inelastic seismic response analysis and design of torsionally coupled systems Kaatsız, Kaan; Sucuoğlu, Haluk; Department of Civil Engineering (2019) Torsional coupling due to irregular placement of load resisting members and/or uneven mass distribution along a story plan is a very common phenomenon in structural systems. Unsymmetrical-plan buildings with stiffness and/or mass asymmetry behave considerably different compared to regular buildings when they are subjected to earthquake-induced forces. Modern earthquake resistant design related code provisions that employ capacity design principles aim to achieve a certain amount of ductility in the structur...

Citation Formats

O. Hasançebi, “Linear and nonlinear model updating of reinforced concrete T-beam bridges using artificial neural networks,” COMPUTERS & STRUCTURES, pp. 1–11, 2013, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/40934.