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Predicting the deformation capacities of reinforced concrete columns is necessary for seismic evaluation of existing and new buildings. These deformation capacities have been set in terms of the rotation angle in EC-8 (2005) and ASCE/SEI 41 (2013) and strain in Turkish Seismic Code (2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016). In this study, finite element models of sixty-nine experimentally tested column specimens were modelled and analyzed by using nonlinear finite element method. As a result of the analyzes, data, which is difficult to measure in experiments such as the strain of outer fiber of confined concrete, were calculated and compared with code deformation limits. As a result of this study, EC-8 column ultimate deformation limits found to remain unsafe according to calculated deformation capacities herein. Taking into account TSC and ASCE/SEI 41, calculated capacities are in the safe region. However, for the axial load ratios over 0.45 and columns produced of high strength concrete, code limit state expressions are unable to predict the limit values correctly. In addition, TSC may significantly limit the deformation capacity of RC columns under their actual capacities. There is a need to improve these code limits using a more extensive column database which includes different design parameters.

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Abstract:The compressive strength factor in civil engineering is a very important parameter used to determine the performance of structures. The stability of structures can be tested with this parameter which is used to measure the performance of concrete under different loads. This parameter, which should be determined for the safety of the structures, is usually based on experimental analyses performed in the laboratory environment. In this study, a new approach to compressive strength measurement in civil engineering is proposed. With this approach, which is based on image processing, measurement of compressive strength parameter of concrete samples taken from structures is performed. For this purpose, images of concrete specimens with different strengths are taken and these images are divided into two groups as training and test set. Then, image processing algorithms are applied to these images and the compressive strength of concrete specimens is calculated. It has been determined that the approach suggested in the test runs performed with an error rate of about 1-2%.