Hybrid Gaussian Process Regression Models for Accurate Prediction of Carbonation-Induced Steel Corrosion in Cementitious Mortars
Steel corrosion prediction in concrete infrastructure remains a critical challenge for durability assessment and maintenance planning. This study presents a comprehensive framework integrating domain expertise with advanced machine learning for carbonation-induced corrosion prediction. Four Gaussian...
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MDPI AG
2025-07-01
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| Online Access: | https://www.mdpi.com/2075-5309/15/14/2464 |
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| author | Teerapun Saeheaw |
| author_facet | Teerapun Saeheaw |
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| collection | DOAJ |
| description | Steel corrosion prediction in concrete infrastructure remains a critical challenge for durability assessment and maintenance planning. This study presents a comprehensive framework integrating domain expertise with advanced machine learning for carbonation-induced corrosion prediction. Four Gaussian Process Regression (GPR) variants were systematically developed: Baseline GPR with manual optimization, Expert Knowledge GPR employing domain-driven dual-kernel architecture, GPR with Automatic Relevance Determination (GPR-ARD) for feature selection, and GPR-OptCorrosion featuring specialized multi-component composite kernels. The models were trained and validated using 180 carbonated mortar specimens with 15 systematically categorized variables spanning mixture, material, environmental, and electrochemical parameters. GPR-OptCorrosion achieved superior performance (R<sup>2</sup> = 0.9820, RMSE = 1.3311 μA/cm<sup>2</sup>), representing 44.7% relative improvement in explained variance over baseline methods, while Expert Knowledge GPR and GPR-ARD demonstrated comparable performance (R<sup>2</sup> = 0.9636 and 0.9810, respectively). Contrary to conventional approaches emphasizing electrochemical indicators, automatic relevance determination revealed supplementary cementitious materials (silica fume and fly ash) as dominant predictive factors. All advanced models exhibited excellent generalization (gaps < 0.02) and real-time efficiency (<0.006 s), with probabilistic uncertainty quantification enabling risk-informed infrastructure management. This research contributes to advancing machine learning applications in corrosion engineering and provides a foundation for predictive maintenance strategies in concrete infrastructure. |
| format | Article |
| id | doaj-art-b5d029344ada473f940addf04de069b2 |
| institution | DOAJ |
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| language | English |
| publishDate | 2025-07-01 |
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| spelling | doaj-art-b5d029344ada473f940addf04de069b22025-08-20T02:45:34ZengMDPI AGBuildings2075-53092025-07-011514246410.3390/buildings15142464Hybrid Gaussian Process Regression Models for Accurate Prediction of Carbonation-Induced Steel Corrosion in Cementitious MortarsTeerapun Saeheaw0Department of Teacher Training in Mechanical Engineering, King Mongkut’s University of Technology North Bangkok, Bangkok 10800, ThailandSteel corrosion prediction in concrete infrastructure remains a critical challenge for durability assessment and maintenance planning. This study presents a comprehensive framework integrating domain expertise with advanced machine learning for carbonation-induced corrosion prediction. Four Gaussian Process Regression (GPR) variants were systematically developed: Baseline GPR with manual optimization, Expert Knowledge GPR employing domain-driven dual-kernel architecture, GPR with Automatic Relevance Determination (GPR-ARD) for feature selection, and GPR-OptCorrosion featuring specialized multi-component composite kernels. The models were trained and validated using 180 carbonated mortar specimens with 15 systematically categorized variables spanning mixture, material, environmental, and electrochemical parameters. GPR-OptCorrosion achieved superior performance (R<sup>2</sup> = 0.9820, RMSE = 1.3311 μA/cm<sup>2</sup>), representing 44.7% relative improvement in explained variance over baseline methods, while Expert Knowledge GPR and GPR-ARD demonstrated comparable performance (R<sup>2</sup> = 0.9636 and 0.9810, respectively). Contrary to conventional approaches emphasizing electrochemical indicators, automatic relevance determination revealed supplementary cementitious materials (silica fume and fly ash) as dominant predictive factors. All advanced models exhibited excellent generalization (gaps < 0.02) and real-time efficiency (<0.006 s), with probabilistic uncertainty quantification enabling risk-informed infrastructure management. This research contributes to advancing machine learning applications in corrosion engineering and provides a foundation for predictive maintenance strategies in concrete infrastructure.https://www.mdpi.com/2075-5309/15/14/2464Gaussian Process Regressioncorrosion predictioncarbonationconcrete durabilitymachine learningkernel design |
| spellingShingle | Teerapun Saeheaw Hybrid Gaussian Process Regression Models for Accurate Prediction of Carbonation-Induced Steel Corrosion in Cementitious Mortars Buildings Gaussian Process Regression corrosion prediction carbonation concrete durability machine learning kernel design |
| title | Hybrid Gaussian Process Regression Models for Accurate Prediction of Carbonation-Induced Steel Corrosion in Cementitious Mortars |
| title_full | Hybrid Gaussian Process Regression Models for Accurate Prediction of Carbonation-Induced Steel Corrosion in Cementitious Mortars |
| title_fullStr | Hybrid Gaussian Process Regression Models for Accurate Prediction of Carbonation-Induced Steel Corrosion in Cementitious Mortars |
| title_full_unstemmed | Hybrid Gaussian Process Regression Models for Accurate Prediction of Carbonation-Induced Steel Corrosion in Cementitious Mortars |
| title_short | Hybrid Gaussian Process Regression Models for Accurate Prediction of Carbonation-Induced Steel Corrosion in Cementitious Mortars |
| title_sort | hybrid gaussian process regression models for accurate prediction of carbonation induced steel corrosion in cementitious mortars |
| topic | Gaussian Process Regression corrosion prediction carbonation concrete durability machine learning kernel design |
| url | https://www.mdpi.com/2075-5309/15/14/2464 |
| work_keys_str_mv | AT teerapunsaeheaw hybridgaussianprocessregressionmodelsforaccuratepredictionofcarbonationinducedsteelcorrosionincementitiousmortars |