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Showing 2 results for Naderpour

Ali Kheyroddin, Hosein Naderpour,
Volume 5, Issue 1 (March 2007)

A parametric study is performed to assess the influence of the tension reinforcement index, ( ω = ρ fy /f Bc), and the bending moment distribution (loading type) on the ultimate deformation characteristics of reinforced concrete (RC) beams. The analytical results for 15 simply supported beams with different amounts of tension reinforcement ratio under three different loading conditions are presented and compared with the predictions of the various formulations and the experimental data, where available. The plastic hinge rotation capacity increases as the loading is changed from the concentrated load at the middle to the third-point loading, and it is a maximum for the case of the uniformly distributed load. The effect of the loading type on the plastic rotation capacity of the heavily reinforced beams is not as significant as that for the lightly reinforced beams. Based on the analytical results obtained using the nonlinear finite element method, new simple equations as a function of the tension reinforcement index, ω, and the loading type are proposed. The analytical results indicate that the proposed equations can be used for analysis of ultimate capacity and the associated deformations of RC beams with sufficient accuracy.
Masoud Ahmadi , Hosein Naderpour , Ali Kheyroddin ,
Volume 15, Issue 2 (Transaction A: Civil Engineering 2017)

Concrete filled steel tube is constructed using various tube shapes to obtain most efficient properties of concrete core and steel tube. The compressive strength of concrete is considerably increased by the lateral confined steel tube in circular concrete filled steel tube (CCFT). The aim of this study was to present an integrated approach for predicting the steel-confined compressive strength of concrete in CCFT columns under axial loading based on large number of experimental data using artificial neural networks. Neural networks process information in a similar way the human brain does. Neural networks learn by example. The main parameters investigated in this study include the compressive strength of unconfined concrete (f'c), outer diameter (D) and length (L) of column, wall thickness (t) and tensile yield stress (fy) of steel tube. Subsequently, using the reliable network, empirical equations are developed for the confinement effect. The results of proposed model are compared with recently existing model on the basis of the experimental results. The findings demonstrate the precision and applicability of the empirical approach to determine capacity of CCFT columns.

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