Under the cyclic traffic loads, welded structural components of steel bridges may encounter fatigue, which can cause a shorter service life and lead to fracture. A precise fatigue life prediction of structural components requires an accurate collection of stress cycles of the respective component. The density of sensors installed for monitoring the component and the distance to the concentrated stress areas are the features, which impact the efficacy of the estimated fatigue life. In this study, a platform is developed for the data-driven fatigue assessment of welded structural components of steel bridges, using artificial neural networks (ANN). The proposed algorithm is implemented for a case study, vertical lift truss bridge, the Memorial Bridge, Portsmouth, NH. A 12-month data collection period is utilized for the algorithm, from the long-term SHM program of the bridge. The stress cycles are used to estimate fatigue responses of an instrumented structural component of the bridge and determine the correlation between the estimated fatigue responses at the instrumentation plan. Additionally, a validated finite element model of the bridge is utilized to investigate fatigue responses in the unhealthy condition of the objective component. Therefore, multiple physical damage cases are simulated to compute the damage-induced stresses and the resulting fatigue life. The healthy and damaged fatigue responses are the ANN inputs, to detect crack-induced variation in the estimated fatigue responses at the instrumented locations. It is demonstrated that the proposed damage detection method can effectively detect possible fatigue cracks using a detailed database of damaged and healthy fatigue damage indices for training ANNs.

在周期性的交通负荷下，钢桥的焊接结构部件可能会遇到疲劳，从而导致使用寿命缩短并导致断裂。结构部件的精确疲劳寿命预测需要各个部件的应力循环的准确收集。其特点是安装了用于监视组件的传感器的密度以及到集中应力区域的距离，这影响了估计疲劳寿命的功效。在这项研究中，使用人工神经网络（ANN）开发了一个平台，用于数据驱动的钢桥焊接结构部件的疲劳评估。拟议的算法用于案例研究，垂直升降桁架桥，新罕布什尔州朴茨茅斯纪念桥。该算法使用12个月的数据收集期，来自桥梁的长期SHM计划。应力循环用于估计桥梁的已检测结构部件的疲劳响应，并确定在检测计划中估算的疲劳响应之间的相关性。此外，采用了经过验证的桥梁有限元模型来研究在目标部件不健康状态下的疲劳响应。因此，模拟了多种物理损坏情况，以计算损坏引起的应力以及由此产生的疲劳寿命。健康的和损坏的疲劳响应是ANN输入，用于检测在仪器位置估计的疲劳响应中裂纹引起的变化。