During the service period of a prestressed concrete bridge, as the number of cyclic loads increases, cumulative fatigue damage and prestress loss will occur inside the structure, which will affect the safety, durability, and service life of the structure. Based on this, this paper studies the loss of bridge prestress under fatigue load. First, the relationship between the prestress loss of the prestressed tendons and the residual deflection of the test beam is analyzed. Based on the test results and the main influencing factors of fatigue and creep, a concrete fatigue and creep calculation model is proposed; then, based on the static cracking check calculation method and POS-BP neural network algorithm, a prestressed concrete beam fatigue cracking check model under repeated loads is proposed. Finally, the mechanical performance of the prestressed concrete beam after fatigue loading is analyzed, and the influence of the fatigue load on the bearing capacity of the prestressed concrete beam is explored. The results show that the bridge prestress loss characterization model based on the POS-BP neural network algorithm has the advantages of high calculation efficiency and strong applicability.

中文翻译：

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基于PSO-BP神经网络疲劳荷载作用下桥梁预应力损失的确定

预应力混凝土桥梁在使用期间，随着循环荷载次数的增加，结构内部会出现累积疲劳损伤和预应力损失，影响结构的安全性、耐久性和使用寿命。基于此，本文研究疲劳荷载作用下桥梁预应力的损失。首先分析预应力筋预应力损失与试验梁残余挠度的关系。根据试验结果和疲劳徐变的主要影响因素，提出了混凝土疲劳徐变计算模型；然后，基于静力开裂校核计算方法和POS-BP神经网络算法，提出了重复荷载作用下预应力混凝土梁疲劳开裂校核模型。最后，对疲劳荷载后预应力混凝土梁的受力性能进行分析，探讨疲劳荷载对预应力混凝土梁承载力的影响。结果表明，基于POS-BP神经网络算法的桥梁预应力损失表征模型具有计算效率高、适用性强等优点。