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Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures
Highlights Fibre optic measurements were performed on near-surface-mounted memory-steel bars. Strain peaks were observed due to bending cracks during loading of concrete slabs. Post-processing revealed axial stress and bond shear stress in the memory-steel. Section analysis with Bayesian modelling was validated with strain measurements. Sobol' indices enabled sensitivity analysis of the used model.
Abstract This investigation aimed at studying a novel strengthening method using ribbed memory-steel bars by means of quasi-static load experiments and computational modelling of large-scale concrete members in flexure. The first part of the study focussed on the strain in activated and non-activated memory-steel bars measured with a distributed fibre optic measurement system during external loading of strengthened slabs. In the second part, a computational cross-section analysis model was developed and enhanced with two uncertainty quantification methods: sensitivity analysis and Bayesian model calibration. The distributed fibre optic strain measurements enabled detailed monitoring of the peak strains owing to concrete cracking. The strain data was post-processed to obtain axial stress and bond shear stress in proximity to the largest bending crack. The sensitivity analysis provided the relative importance of the calibrated input parameters and their interdependencies. Model calibration quantified the uncertain parameters such as prestress, concrete parameters, and memory-steel constitutive laws, resulting in good accordance between the model and the experiments. The models were validated by independent strain measurements. Parametric studies enabled prediction of the effects of the investigated strengthening method on similar concrete members with different prestress, materials and geometries.
Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures
Highlights Fibre optic measurements were performed on near-surface-mounted memory-steel bars. Strain peaks were observed due to bending cracks during loading of concrete slabs. Post-processing revealed axial stress and bond shear stress in the memory-steel. Section analysis with Bayesian modelling was validated with strain measurements. Sobol' indices enabled sensitivity analysis of the used model.
Abstract This investigation aimed at studying a novel strengthening method using ribbed memory-steel bars by means of quasi-static load experiments and computational modelling of large-scale concrete members in flexure. The first part of the study focussed on the strain in activated and non-activated memory-steel bars measured with a distributed fibre optic measurement system during external loading of strengthened slabs. In the second part, a computational cross-section analysis model was developed and enhanced with two uncertainty quantification methods: sensitivity analysis and Bayesian model calibration. The distributed fibre optic strain measurements enabled detailed monitoring of the peak strains owing to concrete cracking. The strain data was post-processed to obtain axial stress and bond shear stress in proximity to the largest bending crack. The sensitivity analysis provided the relative importance of the calibrated input parameters and their interdependencies. Model calibration quantified the uncertain parameters such as prestress, concrete parameters, and memory-steel constitutive laws, resulting in good accordance between the model and the experiments. The models were validated by independent strain measurements. Parametric studies enabled prediction of the effects of the investigated strengthening method on similar concrete members with different prestress, materials and geometries.
Fibre optic measurements and model uncertainty quantification for Fe-SMA strengthened concrete structures
Schranz, Bernhard (author) / Wagner, Paul-Remo (author) / Czaderski, Christoph (author) / Shahverdi, Moslem (author)
Engineering Structures ; 256
2022-02-07
Article (Journal)
Electronic Resource
English
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