Abstract Buildings are vital for critical community functions, and it is of great importance to efficiently invest the limited societal resources in the design of the buildings. To achieve this goal, careful assessment of risk from future hazards is required. In practice, the risk to building structures is regulated by structural design codes through target reliability levels, which are reflected in many code factors, including partial safety factors, load combination factors, and modification factors. Optimizing the target reliability levels often requires running a large number of nonlinear dynamic analyses of complex finite element models, which imposes a significant burden on the computational resources. Computation time can be reduced considerably by employing surrogate models that can efficiently approximate the relation between input design and hazard intensity variables with building response output. This paper explores the different surrogate models, namely, support vector machines, kriging, and neural networks, for structural response prediction of a building class so that they can be used in the target reliability index optimization of a building class (a group of buildings with the same load-carrying characteristics). The uniqueness of this study is in developing a single surrogate model for a group of buildings instead of a single building design. The investigation on the performance of the surrogate models in structural response prediction is conducted for mid-rise office building class by comparing the computation time, accuracy, and robustness.

中文翻译：

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建筑类别结构响应预测的替代建模

摘要 建筑对于关键的社区功能至关重要，将有限的社会资源有效地投入到建筑设计中具有重要意义。为了实现这一目标，需要仔细评估未来危害的风险。在实践中，建筑结构的风险是由结构设计规范通过目标可靠性水平来调节的，这体现在许多规范因素中，包括部分安全因素、荷载组合因素和修改因素。优化目标可靠性水平通常需要对复杂的有限元模型进行大量非线性动力学分析，这对计算资源造成了巨大的负担。通过采用替代模型，可以有效地近似输入设计和危险强度变量与建筑响应输出之间的关系，从而大大减少计算时间。本文探讨了不同的替代模型，即支持向量机、克里金法和神经网络，用于建筑类别的结构响应预测，以便将它们用于建筑类别（一组建筑物）的目标可靠性指标优化。相同的承载特性）。本研究的独特之处在于为一组建筑而非单一建筑设计开发了一个单一的替代模型。通过比较计算时间、精度、