Exposure characterization in regional risk assessment aims to assign physical properties to the assets of interest so they can be associated with damage and loss functions. While this process has benefited from the growing availability of public infrastructure inventories, these datasets often lack the detailed attributes required for high-resolution risk assessment. Missing attributes are commonly inferred using predictive models or engineering-based rulesets. However, these imputations are inherently imperfect and can introduce bias and additional uncertainty in regional risk estimates. This study proposes a methodology to quantify the bias and uncertainty in regional risk assessment that arises from probabilistic exposure characterization. By integrating analytical and simulation-based approaches, the methodology decomposes the total uncertainty into contributions from incomplete exposure information as well as other sources, including hazard and damage characterization. This decomposition clarifies how bias and uncertainty associated with missing exposure information are generated and propagated through the risk assessment pipeline. The methodology is applied to both bridge-specific and regional risk assessments. A high-resolution bridge exposure inventory is developed using a data augmentation framework that combines publicly available information with machine learning and engineering-based imputation methods.

翻译：区域风险评估中的暴露特征刻画旨在为感兴趣资产分配物理属性，以便将其与损伤和损失函数关联。尽管该过程受益于公共基础设施清单日益增长的可用性，但这些数据集往往缺乏高分辨率风险评估所需的详细属性。缺失属性通常通过预测模型或工程经验规则进行推断。然而，这些插补方法本质上存在不完善性，可能给区域风险估计引入偏差和额外不确定性。本研究提出一种量化区域风险评估中由概率性暴露特征刻画引起的偏差与不确定性的方法。通过整合解析推导和仿真模拟方法，该方法将总不确定性分解为不完整暴露信息贡献以及包括灾害与损伤刻画在内的其他来源贡献。这种分解阐明了与缺失暴露信息相关的偏差和不确定性如何在风险评估流程中生成和传播。该方法分别应用于桥梁专项评估和区域风险评估。通过结合公开可用信息与机器学习和工程插补方法的数据增强框架，开发了高分辨率桥梁暴露清单。