Enhancement of Local Buckling Behaviour of Steel Structures Retrofitted Through Bonding GFRP Plates
Bibliographic record
Abstract
An effective technique involving the use of glass fiber reinforced polymer (GFRP) plates to enhance the local buckling behaviour of steel plates, beams, and moment resisting frames is presented in this Thesis. The enhancement in buckling capacity is achieved by bonding GFRP plates to the steel sections. These steel/GFRP joints have the advantages of ease of application, low cost, high strength-to-weight ratio, and resistance to corrosion. An interface element that simulates the behaviour of the adhesive bonding the steel and GFRP elements is developed and is implemented into an in-house developed finite element model to represent steel/GFRP joints. The model is based on a powerful nonlinear shell element that is capable of simulating both thin and thick-walled structures. The strength and stiffness of both the GFRP and the adhesive used in the model are based on values obtained from previously conducted tests.\nThe enhancement in buckling capacity of retrofitted steel/GFRP plates is studied by bonding GFRP plates to steel plates having different aspect and slenderness ratios. The study also considers the effect of initial geometric imperfections on both the elastic and inelastic buckling capacities of retrofitted plates. Better improvement in load capacity is predicted for slender steel plates. The strength of the adhesive is shown to play an important role in defining the mode of failure and in determining the capacity of the retrofitted plates.\nThe improvement in buckling behaviour of retrofitted steel/GFRP beams is then studied considering various thicknesses of GFRP plates. The conducted analysis covers a range of slenderness ratios of steel beams and assesses the effect of plastic modulus of steel, initial geometric imperfection, and residual stresses of the steel section on the load-deflection behaviour of steel beams.\nThe lateral behaviour of moment resisting steel frames retrofitted with GFRP plates is studied to assess their capacity improvement in seismic regions. Nonlinear static pushover analyses are carried out for frames retrofitted at their beams’ flanges with different thickness of GFRP plates. The global capacity curves for the retrofitted frames are compared with their corresponding original frames to assess the improvement in seismic performance of the frames.\nFinally, an experimental investigation is carried out to assess the strength and stiffness properties of adhesively bonded steel/GFRP joints under cyclic loading. A number of shear lap tests are conducted and the obtained results are used to determine the characteristics of spring systems that simulate the shear and peel behaviour of the adhesive. Comparison is made between the stiffness and strength capacity under cyclic loading to the corresponding values under monotonic loading.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".