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<p>The corrosion of steel pipe piles in marine environments, particularly due to chloride ions, presents a significant challenge in marine engineering. Accurately predicting the service life of steel pipe piles at a low cost represents a critical technical challenge. This paper proposes a method based on the physics-informed neural network (PINN) to address this challenge. The method integrates the chloride ion diffusion model with the corrosion depth model to predict the service life of steel pipe piles. First, a chloride ion diffusion model is developed, and the relationship between the reduction in wall thickness and the degradation of mechanical properties is analyzed. Next, a multi-physics coupled mathematical model is formulated using Fick’s second law to predict the service life of steel pipe piles. Finally, by applying particle swarm optimization (PSO) to tune the PINN hyperparameters, the optimized model inferred an effective diffusion coefficient of D = 1.056 × 10⁻¹ mm²/s, deviating by only 4.3% from the experimental measurements. This study provides a low-cost, efficient solution for assessing the service life and informing maintenance decisions for steel pipe piles in marine environments, demonstrating substantial engineering applicability.</p>

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Prediction of corrosion lifetime of marine environment steel pipe piles based on physics-informed neural network

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月23日

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  • Zhaosheng Feng,
  • Zihao Jiang,
  • Chengren Yan

摘要

The corrosion of steel pipe piles in marine environments, particularly due to chloride ions, presents a significant challenge in marine engineering. Accurately predicting the service life of steel pipe piles at a low cost represents a critical technical challenge. This paper proposes a method based on the physics-informed neural network (PINN) to address this challenge. The method integrates the chloride ion diffusion model with the corrosion depth model to predict the service life of steel pipe piles. First, a chloride ion diffusion model is developed, and the relationship between the reduction in wall thickness and the degradation of mechanical properties is analyzed. Next, a multi-physics coupled mathematical model is formulated using Fick’s second law to predict the service life of steel pipe piles. Finally, by applying particle swarm optimization (PSO) to tune the PINN hyperparameters, the optimized model inferred an effective diffusion coefficient of D = 1.056 × 10⁻¹ mm²/s, deviating by only 4.3% from the experimental measurements. This study provides a low-cost, efficient solution for assessing the service life and informing maintenance decisions for steel pipe piles in marine environments, demonstrating substantial engineering applicability.