In structural engineering practice, it is widely accepted that beam-to-column joints in reinforced concrete frames can be idealized as rigid regions. However, recent studies demonstrated that severe damage can be observed in these regions and neglecting inelastic deformations can lead to misinterpretations in performance-based seismic design and assessment process. Despite the large experimental and analytical efforts in establishing a generalized method for predicting inelastic behavior of exterior and interior beam-to-column joints, literature survey revealed that there has only been little consensus about the factors affecting the shear stress–strain envelope. This study introduces the application of Generalized Regression Neural Networks to joint deformation problem and proposes a prediction model. Accuracy and reliability of the proposed model are demonstrated with statistical measures and comparison to various methods available in the literature.

在结构工程实践中，钢筋混凝土框架中的梁柱节点可以理想化为刚性区域，这一点已被广泛接受。但是，最近的研究表明，在这些区域中可以观察到严重破坏，而忽略非弹性变形会导致在基于性能的抗震设计和评估过程中产生误解。尽管在建立通用的预测外部和内部梁至柱节点非弹性行为的通用方法方面进行了大量的实验和分析工作，但文献调查显示，关于影响剪切应力-应变包络的因素只有很少的共识。本文介绍了广义回归神经网络在关节变形问题中的应用，并提出了一种预测模型。