Abdellatief Mohamed, Abd-Elmaboud Mahmoud E, Mortagi Mohamed, Saqr Ahmed M
Department of Civil Engineering, Higher Future Institute of Engineering and Technology in Mansoura, Mansoura, Egypt.
Irrigation and Hydraulics Department, Faculty of Engineering, Mansoura University, Mansoura, 35516, Egypt.
Sci Rep. 2025 Jul 29;15(1):27611. doi: 10.1038/s41598-025-12035-1.
Chloride-induced corrosion is a major threat to the durability of reinforced concrete (RC) structures. This is especially critical in marine tidal zones, where surface chloride concentration (Cs) plays a key role in predicting chloride ingress using Fick's second law. However, traditional assessment methods are time-consuming and impractical, necessitating advanced predictive models. This study developed a deep learning-based framework utilizing a convolutional neural network (CNN) trained on 284 samples with 11 critical features related to material composition and environmental conditions. The CNN's performance was benchmarked against four machine learning (ML) models: stepwise linear regression (SLR), support vector machine (SVM), Gaussian process regression (GPR), and random forest (RF). Results demonstrated CNN's superiority, achieving a coefficient of determination (R) = 0.849 and a lower root mean square error (RMSE) = 0.18%, outperforming conventional models. Shapley additive explanation (SHAP) analysis revealed exposure time, water content, and fine aggregate as the most critical factors influencing Cs predictions. The findings highlighted the importance of material composition and environmental exposure in optimizing concrete mix designs to mitigate chloride ingress in tidal zones. This research can enhance durability assessment, proactive maintenance strategies, and service life estimation of RC structures in harsh marine environments. Furthermore, it can contribute to the sustainable development goals (SDGs) by promoting resilient infrastructure, sustainable construction practices, and improved climate adaptation strategies. By integrating deep learning in durability assessments, this study can provide a scalable, efficient solution for optimizing maintenance planning and reducing premature failures in coastal RC structures, ultimately extending their service life.
氯离子诱导的腐蚀是钢筋混凝土（RC）结构耐久性的主要威胁。这在海洋潮汐区尤为关键，在该区域，表面氯离子浓度（Cs）在使用菲克第二定律预测氯离子侵入方面起着关键作用。然而，传统的评估方法既耗时又不切实际，因此需要先进的预测模型。本研究开发了一个基于深度学习的框架，该框架利用卷积神经网络（CNN），该网络在284个样本上进行训练，这些样本具有与材料成分和环境条件相关的11个关键特征。将CNN的性能与四种机器学习（ML）模型进行了基准测试：逐步线性回归（SLR）、支持向量机（SVM）、高斯过程回归（GPR）和随机森林（RF）。结果表明CNN具有优越性，其决定系数（R）=0.849，均方根误差（RMSE）较低，为0.18%，优于传统模型。夏普利加性解释（SHAP）分析表明，暴露时间、含水量和细集料是影响Cs预测的最关键因素。研究结果突出了材料成分和环境暴露在优化混凝土配合比设计以减轻潮汐区氯离子侵入方面的重要性。这项研究可以加强对RC结构在恶劣海洋环境中的耐久性评估、主动维护策略和使用寿命估计。此外，它可以通过促进有弹性的基础设施、可持续的建筑实践和改进的气候适应策略，为可持续发展目标（SDGs）做出贡献。通过将深度学习集成到耐久性评估中，本研究可以为优化维护计划和减少沿海RC结构的过早失效提供一个可扩展、高效的解决方案，最终延长其使用寿命。
