Accurate prediction of seismic responses and quantification of structural damage are critical in civil engineering. Traditional approaches such as finite element analysis could lack computational efficiency, especially for complex structural systems under extreme hazards. Recently, artificial intelligence has provided an alternative to efficiently model highly nonlinear behaviors. However, existing models face challenges in generalizing across diverse structural systems. This paper proposes a novel multi-channel gated recurrent unit (MC-GRU) network aimed at achieving generalized nonlinear structural response prediction for varying structures. The key concept lies in the integration of a multi-channel input mechanism to GRU with an extra input of structural information to the candidate hidden state, which enables the network to learn the dynamic characteristics of diverse structures and thus empower the generalizability and adaptiveness to unseen structures. The performance of the proposed MC-GRU is validated through a series of case studies, including a single-degree-of-freedom linear system, a hysteretic Bouc-Wen system, and a nonlinear reinforced concrete column from experimental testing. Results indicate that the proposed MC-GRU overcomes the major generalizability issues of existing methods, with capability of accurately inferring seismic responses of varying structures. Additionally, it demonstrates enhanced capabilities in representing nonlinear structural dynamics compared to traditional models such as GRU and LSTM.

翻译：准确预测地震响应与量化结构损伤在土木工程中至关重要。传统方法如有限元分析可能缺乏计算效率，尤其对于极端灾害下的复杂结构系统。近年来，人工智能为高效模拟高度非线性行为提供了替代方案。然而，现有模型在泛化至不同结构系统方面面临挑战。本文提出一种新颖的多通道门控循环单元（MC-GRU）网络，旨在实现对不同结构的广义非线性结构响应预测。其核心思想在于将多通道输入机制集成至GRU，并向候选隐藏状态额外输入结构信息，从而使网络能够学习不同结构的动态特性，进而提升对未见结构的泛化能力与适应性。通过一系列案例研究验证了所提MC-GRU的性能，包括单自由度线性系统、滞回Bouc-Wen系统以及实验测试中的非线性钢筋混凝土柱。结果表明，所提MC-GRU克服了现有方法的主要泛化问题，能够准确推断不同结构的地震响应。此外，相较于GRU和LSTM等传统模型，其在表征非线性结构动力学方面展现出增强的能力。