Corrosion of marine steel structures can be regarded as a time-dependent process that might result in critical strength loss and, eventually, failures. The availability of reliable forecasting models for corrosion would be useful, enabling intelligent maintenance program management, and increasing marine structure safety, while lowering in-service expenses. In this study, an intelligent framework based on a data-driven model is developed that employs a group method of data handling (GMDH) type neural network to forecast free atmospheric corrosion as time-series problem. Therefore, data from sensor data with a 30-min interval over a 110 day period that includes free atmospheric corrosion as well as environmental factors are used. In addition, the Shapley additive explanations (SHAP) technique is used to investigate the impact of the surrounding environmental factors on free atmospheric corrosion. For the performance evaluation of the proposed intelligent framework, selected comparative metrics are used. Findings demonstrate the high accuracy and efficiency of the time series data-driven framework for tackling free atmospheric corrosion progression in marine environments.

海洋钢结构的腐蚀可以被视为一个与时间相关的过程，可能会导致临界强度损失，并最终导致故障。可靠的腐蚀预测模型将非常有用，可以实现智能维护计划管理，提高海洋结构的安全性，同时降低使用费用。在本研究中，开发了一种基于数据驱动模型的智能框架，该框架采用数据处理组方法（GMDH）型神经网络来预测自由大气腐蚀作为时间序列问题。因此，使用 110 天内每隔 30 分钟间隔一次的传感器数据，其中包括自由大气腐蚀以及环境因素。此外，还采用沙普利添加剂解释（SHAP）技术来研究周围环境因素对自由大气腐蚀的影响。为了评估所提出的智能框架的性能，使用了选定的比较指标。研究结果表明，时间序列数据驱动框架在解决海洋环境中的自由大气腐蚀进展方面具有高精度和高效率。