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Pipeline systems are critical infrastructure for modern economies, which serve as the essential means for transporting oil, gas, water, and other fluids. These pipelines are mostly buried underground, making their integrity highly crucial. Because they are buried, these pipelines are subject to stress and are prone to material degradation due to corrosion. Corrosion not only reduces the wall thickness of the pipes but also poses severe safety risks and can lead to catastrophic failures and substantial financial losses. Hence, there is an urgent need to develop accurate predictive models for evaluating pipe wall thickness. This paper aims to address this need by exploring machine learning-based algorithms to monitor the corrosion rates so that preventive measures can be taken to ensure pipeline integrity. Thus, four state-of-the-art machine-learning algorithms, namely, Convolutional Neural Network (CNN), Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (Bi-GRU), and Long Short-Term Memory (LSTM) are employed to predict accurate wall thickness of pipelines. The empirical results show that the LSTM algorithm outperforms its counterparts, achieving a low root mean squared error (RMSE) of 0.0721 mm. Therefore, incorporating LSTM-based models into pipeline integrity programs can be a significant step forward to safeguard these critical infrastructures.
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