The performance of glue laminated bamboo (glubam) members is governed by the nonlinear response at their joints, where high deformation levels and stress concentrations are developed. Numerous phenomenological models are presently employed to describe the hysteresis behavior of these joints, while these models always have an excessive number of parameters, and the physical interpretation of these parameters is often challenging. Moreover, some hysteresis models cannot capture all hysteresis features such as asymmetry, pinching, and damage. Consequently, this paper introduces a novel phenomenological-based hysteretic model named Asymmetric Pinching Damaged (APD) model, and implemented it in Abaqus by combining connector and spring elements in series or parallel. This model encompasses asymmetry, pinching, and strength degradation for bamboo joint components, with parameters that possess clear physical meanings and are readily comprehensible. This study also presented a parameter identification framework coupling the Parallel Genetic Algorithm (PGA) and Bayesian Neural Network (BNN). By merging the FE modeling and optimizing algorithms with the interactive application of ABAQUS and Python software platforms, the integrated identification framework is capable of performing multi-threaded parallel computation of finite element models considering the BNN-based uncertainty quantification, thus greatly improving the efficiency of parameter identification.

中文翻译：

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一种 glubam 杆件不对称捏损伤磁滞模型：参数辨识与模型比较


胶合竹 （glubam） 构件的性能受其接头处的非线性响应控制，其中会产生高变形水平和应力集中。目前采用许多现象学模型来描述这些关节的磁滞行为，而这些模型总是有过多的参数，并且这些参数的物理解释通常具有挑战性。此外，一些磁滞模型无法捕获所有磁滞特征，例如不对称、收缩和损伤。因此，本文引入了一种新的基于现象学的滞后模型，称为 Asymmetric Pinching Damaged （APD） 模型，并通过串联或并联组合连接器和弹簧元件在 Abaqus 中实现它。该模型包括竹节组件的不对称、捏合和强度退化，其参数具有明确的物理含义且易于理解。本研究还提出了一个耦合并行遗传算法 （PGA） 和贝叶斯神经网络 （BNN） 的参数识别框架。通过将有限元建模和优化算法与 ABAQUS 和 Python 软件平台的交互应用相结合，集成识别框架能够考虑基于 BNN 的不确定性量化对有限元模型进行多线程并行计算，从而大大提高了参数识别的效率。