The impact of a disaster is spread over a region and thus the corresponding risks can be managed effectively by carefully considering the community impact. Since structural design codes regulate the safety levels of structures, it is appropriate to consider a community-level objective in the determination of the safety levels stipulated in the structural design codes. However, evaluating community disaster impact requires consideration of loss interdependency among different buildings, which could be a complex task. Furthermore, implementing a community-level objective to design safety level determination requires the establishment of feasible methods to calculate it in a reasonable time. This paper develops a simple and viable framework for community-level objective-based design safety target optimization for multiple building classes subjected to seismic hazard. To reduce the computation cost, the framework employs an efficient procedure of using an artificial neural network to approximate the seismic response of buildings with common characteristics (occupancy use, and type and size of lateral resisting system). The methodology is illustrated for optimizing the target reliability indices of office and hospital building classes exposed to seismic hazard, with minimizing regional economic cost as objective. Simple loss and interdependency models are used for this illustration due to lack of available complex models, which can be replaced by more recent and complex models to improve accuracy of the analysis once they become available.

灾难的影响分散在一个地区，因此，通过仔细考虑社区的影响，可以有效地管理相应的风险。由于结构设计规范规范了结构的安全级别，因此在确定结构设计规范中规定的安全级别时，应考虑一个社区级别的目标。但是，评估社区灾难影响需要考虑不同建筑物之间的损失相互依赖性，这可能是一项复杂的任务。此外，实现社区级别的目标来设计安全级别确定要求建立可行的方法以在合理的时间内对其进行计算。本文针对遭受地震灾害的多个建筑类别，为社区一级基于目标的设计安全目标优化开发了一个简单可行的框架。为了降低计算成本，该框架采用了一种有效的程序，即使用人工神经网络来近似估算具有共同特征（占用使用以及侧向抵抗系统的类型和大小）的建筑物的地震响应。说明了该方法用于优化暴露于地震灾害的办公和医院建筑类别的目标可靠性指标，同时以最小化区域经济成本为目标。由于缺少可用的复杂模型，因此在此示例中使用了简单的损失和相互依赖性模型，