An intelligent framework for forecasting and investigating corrosion in marine conditions using time sensor data

Abstract 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