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

Abstract

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.

Application of Advanced Machine Learning Approaches to Predict the Compressive Strength of Concrete Containing Supplementary Cementitious Materials

“…A total of eight factors were used as inputs for the models, including cement, FA, BFS, water, superplasticizer, coarse aggregate, fine aggregate, and age, while one variable, CS, was used as an output. The input variables and the number of data points have a significant impact on the model’s output [ 29 , 32 , 39 ]. The study used a total of 1030 data points (mixes) for the CS prediction of concrete incorporating FA and BFS.…”

“…Nguyen et al [ 31 ] forecasted the CS of eco-friendly FA-based geopolymer concrete utilizing ML algorithms. Ahmad et al [ 32 ] predicted the chloride penetration in concrete containing waste material using a decision tree, artificial neural network, and gene expression programming techniques. Gene expression programming was demonstrated to be a more effective prediction technique than other algorithms.…”

An Experimental Study on Mechanical Properties for the Static and Dynamic Compression of Concrete Eroded by Sulfate Solution

“…Through the tests, the static and dynamic mechanical properties measured in this study were basically consistent with the performance of concrete under the condition of erosion [ 31 ]. Through the static and dynamic tests, it is found that, in the process of sulfate solution erosion of concrete, the static and dynamic mechanical properties of concrete were improved to a small extent [ 32 ]. The performance of specimens decreased with the prolongation of erosion time.…”

Engineering and Manufacturing Technology of Green Epoxy Resin Coatings Modified with Recycled Fine Aggregates

“…However, this solution seems to be too expensive. The answer is to utilize the waste [56][57][58][59]. The preliminary studies show that the utilization of RFA in epoxy resin could reduce preparation costs of coating [57].…”