Raza Ali, Shah Syyed Adnan Raheel, Alhazmi Hatem, Abrar Muhammad, Razzaq Samia
Department of Civil Engineering, University of Engineering and Technology, Taxila 47080, Pakistan.
Department of Civil Engineering, Pakistan Institute of Engineering and Technology, Multan 66000, Pakistan.
Polymers (Basel). 2021 Apr 13;13(8):1265. doi: 10.3390/polym13081265.
Limited research work is available in the literature for the theoretical estimates of axial compressive strength of columns reinforced with fiber reinforced polymer (FRP) rebars. In the present work, an experimental database of 278 FRP-reinforced concrete (RC) compression members was established from the literature to recommend an empirical model that can accurately predict the axial strength (AS) of GFRP-RC specimens. An initial assessment of 13 different previously anticipated empirical models was executed to achieve a general form of the AS model. Finally, a new empirical equation for forecasting the AS of GFRP-RC short columns was proposed using the curve fitting and regression analysis technique. The performance of the proposed empirical model over the previous experimental database represented its higher accuracy as related to that of other models. For the further justification of the anticipated model, a numerical model of GFRP-RC columns was simulated using ABAQUS and a wide parametric study of 600 GFRP-RC samples was executed to generate a numerical database and investigate the influence of various parameters using numerical and empirical models. The comparison between theoretical and numerical predictions with R = 0.77 indicted that the anticipated empirical model is accurate enough to apprehend the AS of FRP-RC specimens.
关于纤维增强聚合物(FRP)钢筋增强柱的轴向抗压强度理论估算,文献中的研究工作有限。在本研究中,从文献中建立了一个包含278个FRP增强混凝土(RC)受压构件的试验数据库,以推荐一个能够准确预测玻璃纤维增强塑料(GFRP)-RC试件轴向强度(AS)的经验模型。对13种不同的先前预期经验模型进行了初步评估,以得到AS模型的一般形式。最后,采用曲线拟合和回归分析技术,提出了一个预测GFRP-RC短柱AS的新经验方程。与先前试验数据库相比,所提出的经验模型表现出比其他模型更高的准确性。为了进一步验证预期模型,使用ABAQUS模拟了GFRP-RC柱的数值模型,并对600个GFRP-RC样本进行了广泛的参数研究,以生成一个数值数据库,并使用数值模型和经验模型研究各种参数的影响。理论预测与数值预测之间的比较(R = 0.77)表明,预期的经验模型足以准确理解FRP-RC试件的AS。