Time-series prediction of steel corrosion in concrete using recurrent neural networks

Abstract

<p>Steel corrosion is a time-series problem where the current corrosion state is influenced by past corrosion and environmental factors. Most models incorporate time as a variable but fail to comprehensively consider past states and spatial-temporal factors. This study collects continuously monitored corrosion data from the literature and develops time-variant corrosion rate prediction models using traditional machine learning (TML) and recurrent neural networks (RNN). The TML model captures temporal variations, while the RNN model incorporates past and current corrosion factors, focusing on different lookback lengths. Empirical and electrochemical models are also compared to evaluate efficacy. The results indicate superior predictive capabilities for bothTML and RNN models. The TML model yields an RMSE of 0.26 μA/cm2 and a MAPE of 11.27%, while the RNN model achieves 0.22 μA/cm2 and 8.15%. In contrast, empirical and electrochemical models yield suboptimal RMSE (1.48 μA/cm2, 0.53 μA/cm2) and MAPE (77.31%, 35.05%). Analysis of Simple-RNN and LSTM models, reveals optimal performance when considering corrosion factors from the preceding two-time intervals. These results underscore the importance of incorporating past corrosion factors and demonstrate the enhanced efficacy of data-driven approaches, particularly LSTM, over traditional models.</p>