Abstract Premature failure of bridge expansion joints has been increasingly observed in recent years, and nowadays it becomes a major concern of bridge owners. A better understanding of their performance in service is highly desired. Deterministic linear regression models between bridge temperature and expansion joint displacement have widely been adopted to characterize the in-service performance of bridge expansion joints. When such a regression pattern is elicited using real-time monitoring data, the deterministic models fail to account for uncertainty inherent in the monitoring data and interpret the model error. In this study, a probabilistic approach for characterization of the regression pattern between bridge temperature and expansion joint displacement by use of Structural Health Monitoring (SHM) data and for SHM-based condition assessment and damage alarm of bridge expansion joints is developed in the Bayesian context. The proposed approach enables to account for the uncertainty contained in the monitoring data and quantify the model error and the prediction uncertainty. By combining the Bayesian regression model and reliability theory, an anomaly index is formulated to evaluate the health condition of the expansion joint when newly collected monitoring data are available and to provide damage alarm once the probability of damage exceeds a certain threshold. In the case study, real-world monitoring data acquired from a cable-stayed bridge are used to illustrate the proposed approach, including examining the appropriateness of the design values of expansion joint displacements under extreme temperatures in serviceability limit state.

中文翻译：

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基于长期结构健康监测数据的桥梁伸缩缝状态评估和损坏报警的贝叶斯方法

摘要 近年来，桥梁伸缩缝过早失效的现象越来越多，已成为桥梁业主关注的问题。非常需要更好地了解他们在服务中的表现。桥梁温度和伸缩缝位移之间的确定性线性回归模型已被广泛用于表征桥梁伸缩缝的使用性能。当使用实时监测数据引出这种回归模式时，确定性模型无法解释监测数据中固有的不确定性并解释模型错误。在这项研究中，在贝叶斯背景下，开发了一种概率方法，用于通过使用结构健康监测 (SHM) 数据来表征桥梁温度和伸缩缝位移之间的回归模式，以及基于 SHM 的桥梁伸缩缝状况评估和损坏警报。所提出的方法能够解释监测数据中包含的不确定性，并量化模型误差和预测不确定性。结合贝叶斯回归模型和可靠性理论，制定异常指标，在新采集到的监测数据可用时评估伸缩缝的健康状况，并在损坏概率超过一定阈值时提供损坏报警。在案例研究中，