ArticleScience and technology of advanced materials2020
Corrosion rate prediction and influencing factors evaluation of low-alloy steels in marine atmosphere using machine learning approach.
Article in Science and technology of advanced materials, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
- Early Rust-Layer Evolution of Q355 Carbon Steel in the Pingtan Marine Atmosphere.Materials (Basel, Switzerland) · 2026Article
- Machine Learning for Structural Steels: Materials Design, Property Prediction, Durability, and Future Directions.Materials (Basel, Switzerland) · 2026Review
- Data-intelligence driven methods for durability, damage diagnosis and performance prediction of concrete structures.Communications engineering · 2025Review
- A machine learning approach for corrosion rate modeling in Patna water distribution network of Bihar.Scientific reports · 2025Article
- Integrating Machine Learning into Additive Manufacturing of Metallic Biomaterials: A Comprehensive Review.Journal of functional biomaterials · 2025Review
- Development of a Predictive Model for Carbon Dioxide Corrosion Rate and Severity Based on Machine Learning Algorithms.Materials (Basel, Switzerland) · 2024Article
- Machine learning-based corrosion rate prediction of steel embedded in soil.Scientific reports · 2024Article
- Integrated hybrid modeling and SHAP (SHapley Additive exPlanations) to predict and explain the adsorption properties of thermoplastic polyurethane (TPU) porous materials.RSC advances · 2024Article
- Early Predicting Tribocorrosion Rate of Dental Implant Titanium Materials Using Random Forest Machine Learning Models.Tribology international · 2023Article
- Optimal Design of the Austenitic Stainless-Steel Composition Based on Machine Learning and Genetic Algorithm.Materials (Basel, Switzerland) · 2023Article
- Machine learning modeling of predictive external corrosion rates of spent nuclear fuel carbon steel canister in soil.Scientific reports · 2022Article
- Evaluation of Commercial Corrosion Sensors for Real-Time Monitoring of Pipe Wall Thickness under Various Operational Conditions.Sensors (Basel, Switzerland) · 2022Article
- Use of Artificial Intelligence for Predicting Parameters of Sustainable Concrete and Raw Ingredient Effects and Interactions.Materials (Basel, Switzerland) · 2022Article
- Plasticity of Bead-on-Plate Welds Made with the Use of Stored Flux-Cored Wires for Offshore Applications.Materials (Basel, Switzerland) · 2020Article
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4 authors.
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Abstract
The empirical modeling methods are widely used in corrosion behavior analysis. But due to the limited regression ability of conventional algorithms, modeling objects are often limited to individual factors and specific environments. This study proposed a modeling method based on machine learning to simulate the marine atmospheric corrosion behavior of low-alloy steels. The correlations between material, environmental factors and corrosion rate were evaluated, and their influences on the corrosion behavior of steels were analyzed intuitively. By using the selected dominating factors as input variables, an optimized random forest model was established with a high prediction accuracy of corrosion rate (
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