This study proposes a modularized deep learning-based loading protocol for optimal parameter estimation of Bouc-Wen (BW) class models. The protocol consists of two key components: optimal loading history construction and CNN-based rapid parameter estimation. Each component is decomposed into independent sub-modules tailored to distinct hysteretic behaviors-basic hysteresis, structural degradation, and pinching effect-making the protocol adaptable to diverse hysteresis models. Three independent CNN architectures are developed to capture the path-dependent nature of these hysteretic behaviors. By training these CNN architectures on diverse loading histories, minimal loading sequences, termed \textit{loading history modules}, are identified and then combined to construct an optimal loading history. The three CNN models, trained on the respective loading history modules, serve as rapid parameter estimators. Numerical evaluation of the protocol, including nonlinear time history analysis of a 3-story steel moment frame and fragility curve construction for a 3-story reinforced concrete frame, demonstrates that the proposed protocol significantly reduces total analysis time while maintaining or improving estimation accuracy. The proposed protocol can be extended to other hysteresis models, suggesting a systematic approach for identifying general hysteresis models.

翻译：本研究提出了一种基于深度学习的模块化加载协议，用于Bouc-Wen（BW）类模型的最优参数估计。该协议包含两个关键组成部分：最优加载历程构建和基于CNN的快速参数估计。每个组件被分解为独立的子模块，分别针对不同的滞回行为——基本滞回、结构退化和捏拢效应——从而使该协议能够适应多种滞回模型。研究开发了三种独立的CNN架构，以捕捉这些滞回行为的路径依赖性。通过在不同的加载历程上训练这些CNN架构，识别出被称为“加载历程模块”的最小加载序列，然后将其组合以构建最优加载历程。在各自加载历程模块上训练的三种CNN模型可作为快速参数估计器。对该协议的数值评估（包括对一个3层钢框架进行非线性时程分析以及对一个3层钢筋混凝土框架进行易损性曲线构建）表明，所提出的协议在保持或提高估计精度的同时，显著减少了总分析时间。该协议可扩展至其他滞回模型，为识别通用滞回模型提供了一种系统化方法。