Bridges are one of the most important assets of transportation networks. A closure of a bridge can increase the vulnerability of the geographic area served by such networks, as it reduces the number of available routes. Condition monitoring and deterioration detection methods can be used to monitor the health state of a bridge and enable detection of early signs of deterioration. In this paper, a novel Bayesian Belief Network (BBN) methodology for bridge deterioration detection is proposed. A method to build a BBN structure and to define the Conditional Probability Tables (CPTs) is presented first. Then evidence of the bridge behaviour (such as bridge displacement or acceleration due to traffic) is used as an input to the BBN model, the probability of the health state of whole bridge and its elements is updated and the levels of deterioration are detected. The methodology is illustrated using a Finite Element Model (FEM) of a steel truss bridge, and for an in-field post-tensioned concrete bridge.

桥梁是交通网络最重要的资产之一。桥接器的关闭会增加此类网络服务的地理区域的脆弱性，因为它会减少可用路由的数量。状态监视和劣化检测方法可用于监视桥梁的健康状态，并能够检测劣化的早期迹象。在本文中，提出了一种新颖的贝叶斯信念网络（BBN）方法用于桥梁劣化检测。首先介绍一种建立BBN结构并定义条件概率表（CPT）的方法。然后将桥梁行为的证据（例如由于交通引起的桥梁位移或加速）用作BBN模型的输入，更新整个桥梁及其元素的健康状态的概率，并检测劣化程度。使用钢桁架桥的有限元模型（FEM）以及现场后张预应力混凝土桥对方法进行了说明。