Artificial intelligence and machine learning are employed in creating functions for the prediction of self-compacting concrete (SCC) strength based on input variables proportion as cement replacement. SCC incorporating waste material has been used in learning approaches. Artificial neural network (ANN) support vector machine (SVM) and gene expression programming (GEP) consisting of 300 datasets have been utilized in the model to foresee the mechanical property of SCC. Data used in modeling consist of several input parameters such as cement, water–binder ratio, coarse aggregate, fine aggregate, and fly ash (FA) in combination with the superplasticizer. The best predictive models were selected based on the coefficient of determination (R²) results and model validation. Empirical relation with mathematical expression has been proposed using ANN, SVM, and GEP. The efficiency of the models is assessed by permutation features importance, statistical analysis, and comparison between regression models. The results reveal that the proposed machine learning models achieved adamant accuracy and has elucidated performance in the prediction aspect.

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 (C_c) in marine structures. For this purpose, the values of C_c 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.

In this study, basalt, which is common around Diyarbakır province (Turkey), is used as concrete aggregate, waste materials as mineral additives and Portland cement as binding material to prepare concrete mixes. This paper aims to determine the proper admixture levels and usability of Diyarbakır basalt in concrete mixtures based on mechanical, physical and chemical tests. Thus, in order to determine the strength and durability performance of concrete mixtures with Diyarbakır basalt as aggregate, 72 sample cubes of 150 mm were prepared in three groups: mineral-free admixture (MFA), 10% of cement amount substituted for silica fume (SFS) and 20% for fly ash (FAS) as waste material. The samples were exposed to water curing and 100g/L sulphate solution to determine the loss in weight of the concrete cubes and compressive strength was examined at the end of 7, 28 and 360 days of the specimens. Analysis of the microstructure and cracks that influence durability, were also performed to determine effects of sulphate attacks alkali-silica reactions on the specimens using scanning electron microscopy (SEM). A loss in weight of the concrete cubes and compressive strength was distinctly evident at the end of 56 and 90 days in both acids.