Visual inspections is a common approach for the network‐scale monitoring of bridges. One of the main challenges when interpreting visual inspections is the observations being subjective and thus the observation uncertainty varies among different inspectors. In addition, observations uncertainties can be dependent on the structural element condition. These two factors introduce difficulties in differentiating between measurement errors and legitimate changes in a structure's condition. This study proposes a state‐space model suited for the network‐scale analyses of transportation infrastructure. The formulation of the proposed framework enables quantifying the uncertainty associated with each inspector. In addition, the proposed model accounts for the uncertainty of visual inspections based on the structure condition as well as the uncertainty specific to each inspector. The predictive capacity and robustness of the proposed model are verified with synthetic inspection data, where the true deterioration state is known. Following the verification step, the proposed model is validated with real data taken from a visual inspections database.

中文翻译：

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使用网络规模的状态空间模型通过视觉检查对基础架构退化进行建模

目视检查是网络规模的桥梁监控的常用方法。解释目视检查时的主要挑战之一是观察是主观的，因此观察不确定性在不同检查人员之间有所不同。此外，观测不确定性可能取决于结构要素条件。这两个因素在区分测量误差和结构条件的合理变化方面带来了困难。这项研究提出了一种状态空间模型，适用于交通基础设施的网络规模分析。拟议框架的制定能够量化与每个检查员相关的不确定性。此外，建议的模型根据结构条件以及每个检查员的特定不确定性考虑了目视检查的不确定性。所提出模型的预测能力和鲁棒性已通过综合检查数据进行了验证，其中真实的劣化状态是已知的。在验证步骤之后，将使用从视觉检查数据库中获取的真实数据验证所提出的模型。