Suspension bridges are critical components of transport infrastructure around the world. Therefore, their operating conditions should be effectively monitored to ensure their safety and reliability. However, the main cables of suspension bridges inevitably deteriorate over time due to corrosion, as a result of their operational and environmental conditions. Thus, accurate annual corrosion rate predictions are crucial for maintaining reliable structures and optimal maintenance operations. However, the corrosion rate is a chaotic and complex phenomenon with highly nonlinear behavior. This paper proposes a novel predictive model for the estimation of the annual corrosion rate in the main cables of suspension bridges. This is a hybrid model based on the multilayer perceptron (MLP) technique optimized using marine predators algorithm (MPA). In addition, well-known metaheuristic approaches such as the genetic algorithm (GA) and particle swarm algorithm (PSO) are employed to optimize the MLP. In order to implement the proposed model, a comprehensive database composed of 309 sample tests on the annual corrosion rate from all around the world, including various factors related to the surrounding environmental properties, is utilized. In addition, several input combinations are proposed for investigating the trigger factors in modeling the annual corrosion rate. The performance of the proposed models is evaluated using various statistical and graphical criteria. The results of this study demonstrate that the proposed hybrid MLP-MPA model provides stable and accurate predictions, while it transcends the previously developed approaches for solving this problem. The effectiveness of the MLP-MPA model shows that it can be used for further studies on the reliability analysis of the main cables of suspension bridges.

悬索桥是世界各地交通基础设施的重要组成部分。因此，应有效监控其运行状况，以确保其安全性和可靠性。然而，由于运营和环境条件的影响，悬索桥的主缆不可避免地会随着时间的推移而老化。因此，准确的年腐蚀率预测对于保持可靠的结构和最佳的维护操作至关重要。然而，腐蚀速率是一种具有高度非线性行为的混沌复杂现象。本文提出了一种新的预测模型，用于估计悬索桥主缆的年腐蚀率。这是一种基于使用海洋捕食者算法 (MPA) 优化的多层感知器 (MLP) 技术的混合模型。此外，众所周知的元启发式方法，如遗传算法（GA）和粒子群算法（PSO）被用来优化 MLP。为了实施所提出的模型，使用了一个综合数据库，该数据库由来自世界各地的 309 个年腐蚀率样本测试组成，包括与周围环境特性相关的各种因素。此外，还提出了几种输入组合，用于研究模拟年腐蚀率的触发因素。使用各种统计和图形标准评估所提出模型的性能。这项研究的结果表明，所提出的混合 MLP-MPA 模型提供了稳定和准确的预测，同时它超越了以前开发的解决此问题的方法。