Accurately assessing failure risk due to asset deterioration and/or extreme events is essential for efficient transportation asset management. Traditional risk assessment is conducted for individual assets by either focusing on the economic risk to asset owners or relying on empirical proxies of systemwide consequences. Risk assessment directly based on system performance (e.g., network capacity) is largely limited due to (1) an exponentially increasing number of system states for accurate performance evaluation, (2) potential contribution of system states with low likelihood yet high consequences (i.e., "gray swan" events) to system state, and (3) lack of actionable information for asset management from risk assessment results. To address these challenges, this paper introduces a novel approach to performance-based risk assessment for large-scale transportation networks. The new approach is underpinned by the Transitional Markov Chain Monte Carlo (TMCMC) method, a sequential sampling technique originally developed for Bayesian updating. The risk assessment problem is reformulated such that (1) the system risk becomes the normalizing term (i.e., evidence) of a high-dimensional posterior distribution, and (2) the final posterior samples from TMCMC yield risk-based importance measures for different assets. Two types of analytical examples are developed to demonstrate the effectiveness and efficiency of the proposed approach as the number of assets increases and the influence of gray swan events grows. The new approach is further applied in a case study on the Oregon highway network, serving as a real-world example of large-scale transportation networks.

翻译：准确评估因资产劣化和/或极端事件导致的失效风险，对于高效的交通资产管理至关重要。传统的风险评估针对单个资产进行，要么关注资产所有者面临的经济风险，要么依赖于系统层面后果的经验性代理指标。直接基于系统性能（例如，网络通行能力）的风险评估在很大程度上受到以下因素的限制：(1) 为准确评估性能所需的系统状态数量呈指数级增长；(2) 低可能性但高后果的系统状态（即"灰天鹅"事件）对系统风险的潜在贡献；(3) 风险评估结果缺乏可用于资产管理的可操作信息。为应对这些挑战，本文提出了一种针对大规模交通网络的、基于性能的风险评估新方法。该新方法以过渡马尔可夫链蒙特卡罗方法为理论基础，这是一种最初为贝叶斯更新而开发的序贯采样技术。本研究对风险评估问题进行了重构，使得 (1) 系统风险成为一个高维后验分布的归一化项（即证据），并且 (2) TMCMC 产生的最终后验样本能为不同资产生成基于风险的重要性度量。本文构建了两类分析示例，以证明所提方法在资产数量增加和灰天鹅事件影响增大情况下的有效性与效率。该新方法进一步应用于俄勒冈州公路网络的案例研究，作为大规模交通网络的一个现实世界示例。