In a performance based earthquake engineering (PBEE) framework, nonlinear time-history response analysis (NLTHA) for numerous ground motions are required to assess the seismic risk of buildings or civil engineering structures. However, such numerical simulations are computationally expensive, limiting the real-world practical application of the framework. To address this issue, previous studies have used machine learning to predict the structural responses to ground motions with low computational costs. These studies typically conduct NLTHAs for a few hundreds ground motions and use the results to train and validate surrogate models. However, most of the previous studies focused on computationally-inexpensive response analysis models such as single degree of freedom. Surrogate models of high-fidelity response analysis are required to enrich the quantity and diversity of information used for damage assessment in PBEE. Notably, the computational cost of creating training and validation datasets increases if the fidelity of response analysis model becomes higher. Therefore, methods that enable surrogate modeling of high-fidelity response analysis without a large number of training samples are needed. This study proposes a framework that uses transfer learning to construct the surrogate model of a high-fidelity response analysis model. This framework uses a surrogate model of low-fidelity response analysis as the pretrained model and transfers its knowledge to construct surrogate models for high-fidelity response analysis with substantially reduced computational cost. As a case study, surrogate models that predict responses of a 20-story steel moment frame were constructed with only 20 samples as the training dataset. The responses to the ground motions predicted by constructed surrogate model were consistent with a site-specific time-based hazard.

翻译：在基于性能的地震工程框架中，评估建筑或土木工程结构的地震风险需要对大量地震动进行非线性时程响应分析(NLTHA)。然而，此类数值模拟计算成本高昂，限制了该框架的实际工程应用。为解决此问题，以往研究采用机器学习以较低计算成本预测地震动作用下的结构响应。这些研究通常对数百条地震动开展NLTHA，并利用结果训练和验证代理模型。值得注意的是，以往研究多集中于单自由度等计算成本较低的响应分析模型。为丰富基于性能地震工程中损伤评估所需的响应信息数量与多样性，需构建高保真响应分析的代理模型。然而，当响应分析模型保真度提高时，创建训练与验证数据集的计算成本随之增加。因此，亟需开发能通过有限训练样本实现高保真响应分析代理建模的方法。本研究提出一种利用迁移学习构建高保真响应分析模型代理模型的框架。该框架以低保真响应分析代理模型作为预训练模型，通过知识迁移构建高保真响应分析代理模型，显著降低计算成本。案例研究中，仅以20个样本作为训练数据集，构建了预测20层钢框架结构响应的代理模型。该代理模型预测的地震动响应结果与场地特定时程危险分析结果高度吻合。