Application of Novel Machine Learning Techniques for Predicting the Surface Chloride Concentration in Concrete Containing Waste Material

• Published in: Materials Vol. 14; no. 9; p. 2297
• Main Authors: Ahmad, Ayaz, Farooq, Furqan, Ostrowski, Krzysztof Adam, Śliwa-Wieczorek, Klaudia, Czarnecki, Slawomir
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.04.2021; MDPI
• Subjects: Accuracy; Algorithms; Artificial neural networks; Chloride; Chloride ions; Concrete; Construction; Correlation coefficients; Corrosion; Decision trees; Deep learning; Gene expression; Laboratories; Laboratory tests; Learning theory; Literature reviews; Machine learning; Mechanical properties; Reinforced concrete; Reinforcing steels; Root-mean-square errors; Support vector machines; Variables; concrete; individual algorithm; artificial neural networks; gene expression programming; surface chloride concentration; aggressive ions environment
• DOI: 10.3390/ma14092297
• ISSN: 1996-1944

Structures located on the coast are subjected to the long-term influence of chloride ions, which cause the corrosion of steel reinforcements in concrete elements. This corrosion severely affects the performance of the elements and may shorten the lifespan of an entire structure. Even though experimental activities in laboratories might be a solution, they may also be problematic due to time and costs. Thus, the application of individual machine learning (ML) techniques has been investigated to predict surface chloride concentrations (Cc) in marine structures. For this purpose, the values of Cc in tidal, splash, and submerged zones were collected from an extensive literature survey and incorporated into the article. Gene expression programming (GEP), the decision tree (DT), and an artificial neural network (ANN) were used to predict the surface chloride concentrations, and the most accurate algorithm was then selected. The GEP model was the most accurate when compared to ANN and DT, which was confirmed by the high accuracy level of the K-fold cross-validation and linear correlation coefficient (R2), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) parameters. As is shown in the article, the proposed method is an effective and accurate way to predict the surface chloride concentration without the inconveniences of laboratory tests.