Selecting or generating ground motions (GMs) that elicit seismic responses matching specific standards or expected benchmarks for nonlinear time‐history analysis (NLTHA) is crucial for ensuring the rationality of structural seismic design and analysis. Typical GM inputs for NLTHA, either natural or artificial, are normally spectrum‐compatible, which may produce significant variations in analysis results, even using multiple GMs. This paper introduces a response‐compatible ground motion generation (RCGMG) method for generating GMs that are tailored to be response‐compatible. NLTHA results using only a few of these artificial GMs can closely approximate the mean responses from a large set of natural spectrum‐compatible GMs or target responses. The RCGMG method adopts the response diagram in the time domain (RDTD) to characterize the nonstationary features of GMs and their impacts on structural dynamic responses. A physics‐guided conditional generative adversarial network is developed to produce artificial RDTDs with features and impacts of RDTDs of natural GMs. These generated RDTDs are then mapped into response‐compatible GMs through a feedforward neural network. To verify the effectiveness of RCGMG, NLTHA of different structure models under various site conditions and target spectra is conducted. Seismic responses of NLTHA using RCGMG‐generated GMs are compared with responses from spectrum‐compatible natural GMs. The results demonstrate that responses from RCGMG GMs are closer to the target responses, with fewer GM inputs and robust generalization performance.

中文翻译：

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使用物理引导神经网络的响应兼容的地面运动生成方法

选择或生成能够引发与非线性时程分析（NLTHA）特定标准或预期基准相匹配的地震响应的地面运动（GM）对于确保结构抗震设计和分析的合理性至关重要。 NLTHA 的典型 GM 输入，无论是天然的还是人工的，通常都是频谱兼容的，这可能会在分析结果中产生显着的变化，即使使用多个 GM 也是如此。本文介绍了一种响应兼容的地面运动生成（RCGMG）方法，用于生成响应兼容的 GM。仅使用其中一些人工 GM 的 NLTHA 结果就可以非常接近大量自然频谱兼容 GM 或目标响应的平均响应。 RCGMG方法采用时域响应图（RDTD）来表征GM的非平稳特征及其对结构动力响应的影响。开发了一种物理引导的条件生成对抗网络来生成具有天然 GM RDTD 的特征和影响的人工 RDTD。然后，这些生成的 RDTD 通过前馈神经网络映射到响应兼容的 GM 中。为了验证 RCGMG 的有效性，对不同场地条件和目标光谱下的不同结构模型进行了 NLTHA。使用 RCGMG 生成的 GM 的 NLTHA 地震响应与频谱兼容的天然 GM 的响应进行了比较。结果表明，RCGMG GM 的响应更接近目标响应，GM 输入更少，泛化性能更强。