纤维增强复材约束矩形混凝土轴压强度和极限压应变计算方法研究

RESEARCH ON CALCULATION METHOD FOR AXIAL COMPRESSIVE STRENGTH AND ULTIMATE COMPRESSIVE STRAIN OF RECTANGULAR CONCRETE CONFINED WITH FRP

轴压荷载下纤维增强复材(FRP)约束混凝土应力-应变本构关系可分为强化型和软化型两类约束模型,其在极限状态时所固有的轴压应力及应变构成了各自本构的参数基础,准确计算这些本构参数可为FRP约束混凝土结构的性能评估提供判别依据。通过对现有FRP约束矩形混凝土抗压强度和极限压应变经验模型的性能进行综合评价,表明已有经验模型普遍存在通用性差、预测准确性低以及离散性大等问题。针对传统预测模型的局限性,基于反向传播神经网络,分别建立了强化型和软化型FRP约束矩形混凝土抗压强度和极限压应变预测模型。研究结果表明:神经网络模型不仅能反映各类控制参数对轴压应力及应变的影响,且相比于已有经验模型,基于神经网络模型的计算值和试验值吻合更好,偏差和随机性都显著减小,保证了预测结果的准确性和稳定性。

The stress-strain constitutive relation of FRP-confined concrete can be divided into two categories of constraint models under axial load: work hardening and softening. The intrinsic axial compressive stress and strain in the ultimate state constitute the parameter basis of their constitutions. Calculating these constitutive parameters accurately can provide a discrimination criteria for evaluating the performance of FRP-confined concrete structures.The performance of the existing empirical models about compressive strength and ultimate compressive strain of rectangular concrete confined with FRP was evaluated comprehensively,and the results showed that the existing empirical models generally showed poor universality,low prediction accuracy and high dispersion. Aiming at the limitation of traditional prediction models,the prediction models of compressive strength and ultimate compressive strain for both hardening and softening types of rectangular concrete confined with FRP were constructed respectively based on back-propagation artificial neural network. The research results indicated that the neural network models could not only reflect the influence of various control parameters on the axial compressive stress and strain,but also the calculated values based on the neural network models were in better agreement with the experimental values when compared with the existing empirical models;moreover,the deviation and randomness were significantly decreased,so as to ensure the accuracy and stability of prediction results.