The impact of corrosion on the nonlinear stress-strain behaviour of reinforcing bars under monotonic and cyclic loading was explored experimentally. The corrosion procedure was simulated in a laboratory environment using an accelerated corrosion procedure. A total of 132 corroded test specimens were produced and tested under mono tonic and cyclic loading. 23 corroded bars from the tests specimens were taken for further statistical analysis of their corrosion patterns. An advanced 3D optical measurement technique was employed to scan the surface of corroded bars. A novel stochastic signal processing technique was used for corrosion pattern analysis and subsequent development of probabilistic distribution models for the geometrical properties of corroded bars (cross section area, second moment of area etc.). Finally the scanned bars were tested under monotonic buckling and cyclic loading. The impact of the corrosion patterns on the nonlinear stress-strain behaviour of corroded bars was then investigated using nonlinear finite element modelling of the tested bars which was compared with the experimental results. Using the experimental and numerical data a new phenomenological uniaxial material model is also developed for reinforcing bars. The new material model accounts for the influence of corrosion damage, inelastic buckling and low-cycle fatigue degradation. This model is then implemented into the OpenSees platform as a new uniaxial material class known as Corroded ReinforcingSteel. Finally the material model is validated against 10 buckling critical flexural RC columns from the UW-PEER experimental RC column database. In addition, a new modelling technique is developed for modelling the flexural behaviour of buckling critical RC bridge piers using a distributed plasticity fibre beamcolumn model. These new models for the assessment of corrosion damaged RC columns have significantly improved the accuracy of previous models. This will help bridge managers and owners to develop a rigorous maintenance strategy to evaluate and predict the performance of their bridge network so that repairs can be prioritised and targeted at the most critical structures.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:650096 |
| Date | January 2014 |
| Creators | Kashani, Mohammad Mehdi |
| Publisher | University of Bristol |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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