Backbone curves are used to characterize nonlinear responses of structural elements by simplifying the cyclic force-deformation relationships. Accurate modeling of cyclic behavior can be achieved with a reliable backbone curve model. In this paper, a novel machine learning-based model is proposed to predict the backbone curve of reinforced concrete shear (structural) walls based on key wall design properties. Reported experimental responses of a detailed test database consisting of 384 reinforced concrete shear walls under cyclic loading were utilized to predict seven critical points to define the backbone curves, namely: shear at cracking point; shear and displacement at yielding point; and peak shear force and corresponding displacement; and ultimate displacement and corresponding shear. The predictive models were developed based on the Gaussian Process Regression method (GPR), which adopts a non-parametric Bayesian approach. The ability of the proposed GPR-based model to make accurate and robust estimations for the backbone curves was validated based on unseen data using a hundred random sampling procedure. The prediction accuracies (i.e., ratio of predicted/actual values) are close to 1.0, whereas the coefficient of determination (R2) values range between 0.90-0.97 for all backbone points. The proposed GPR-based backbone models are shown to reflect cyclic behavior more accurately than the traditional methods, therefore, they would serve the earthquake engineering community for better evaluation of the seismic performance of existing buildings.
翻译:使用后骨曲线来通过简化循环力退化关系来描述结构要素的非线性反应。可以通过一个可靠的主干曲线模型来精确地模拟周期行为模式。在本文中,提议了一个新的机器学习模型,以预测以关键的墙壁设计特性为基础的强化混凝土剪刀(结构)墙的骨架曲线。报告的详细测试数据库的实验反应,包括循环负荷下的384个强化混凝土剪刀壁,用于预测确定骨干曲线的7个关键点,即:裂口断裂点的剪刀;断裂点的切裂和流离失所;以及峰值的剪动力和相应的流离失所;以及最终的剪动和相应的剪动。预测模型是根据高斯进程回归法(GPR)开发的预测性曲线曲线曲线曲线曲线曲线曲线曲线曲线曲线曲线曲线,该模型采用了非对巴耶斯马氏法的方法。以GPR模型为基础对骨架曲线进行准确和稳健估计的能力进行了验证,根据100个随机抽样程序,预测了骨干曲线的7个临界点,即预测(i.i. 社区预测/直径断断断断断断断断率/直径比率) 显示G- missimal值的模型在10号的模型中,而显示所有基值之间为10-正基值的逻辑值,而显示的正基数值为10比正基数值。