Abstract

Orthotropic steel decks (OSD) of bridges are subjected to cyclic vehicle loads, leading to the growth of fatigue cracks. Fatigue strengths are dependent on fatigue details and the load effects vary from position to position; consequently, the spatial distribution of fatigue cracks on the bridge deck is not uniform. Previous studies on fatigue effects of OSD are mainly limited to a single fatigue detail. This article presents a Bayesian network (BN)-based method to investigate the uneven distribution of fatigue cracks on a cable-stayed bridge deck. The bridge deck is divided into girder-elements, and types of fatigue cracks are considered. Critical variables controlling the uneven distribution of fatigue cracks are represented by root nodes, while the fatigue states of girder-elements are represented by leaf nodes; the causal relationships are described by directed edges between nodes. The proposed approach is validated using field inspection results. A sensitivity analysis is conducted to reveal the importance of each node in the BN. Compared to previous researches involving fatigue cracks, the proposed approach is superior in calculating the occurrence probabilities of types of fatigue cracks given critical variables as well as quantitatively analysing the main causes for types of fatigue cracks.

摘要

桥梁的正交各向异性钢桥面板 (OSD) 受到车辆循环载荷的影响，导致疲劳裂纹的增长。疲劳强度取决于疲劳细节，载荷效应因位置而异；因此，桥面疲劳裂纹的空间分布并不均匀。先前关于 OSD 疲劳影响的研究主要限于单个疲劳细节。本文提出了一种基于贝叶斯网络 (BN) 的方法来研究斜拉桥面板上疲劳裂纹的不均匀分布。桥面分为梁单元，并考虑疲劳裂纹的类型。控制疲劳裂纹不均匀分布的关键变量用根节点表示，梁单元的疲劳状态用叶节点表示；因果关系由节点之间的有向边描述。使用现场检查结果验证了所提出的方法。进行敏感性分析以揭示BN中每个节点的重要性。与以往涉及疲劳裂纹的研究相比，所提出的方法在计算给定临界变量的疲劳裂纹类型的发生概率以及定量分析疲劳裂纹类型的主要原因方面具有优越性。