Self-compacting concrete (SCC) is a type of concrete known for its environmental benefits and improved workability. In this study, data-driven approaches were used to anticipate the compressive strength (CS) of self-compacting concrete (SCC) containing recycled plastic aggregates (RPA). A database of 400 experimental data sets was used to assess the capabilities of multi-objective genetic algorithm evolutionary polynomial regression (MOGA-EPR) and gene expression programming (GEP). The analysis results indicated that the proposed equations provided more accurate CS predictions than traditional approaches such as the linear regression model (LRM). The proposed equations achieved lower mean absolute error (MAE) and root mean square error (RMSE) values, a mean close to the optimum value (1.0), and a higher coefficient of determination (R²) than the LRM. As such, the proposed approaches can be utilized to obtain more reliable design calculations and better predictions of CS in SCC incorporating RPA.

This study evaluates the applicability of using a robust, novel, data-driven method in proposing surrogate models to predict the maximum dry unit weight, optimum moisture content, and California bearing ratio of coarse-grained soils using only the results of the grain size distribution analysis. The data-driven analysis has been conducted using evolutionary polynomial regression analysis (MOGA-EPR), employing a comprehensive database. The database included the particle diameter corresponding to a percentage of the passing of 10%, 30%, 50%, and 60%, coefficient of uniformity, coefficient of curvature, dry unit weight, optimum moisture content, and California bearing ratio. The statistical assessment results illustrated that the MOGA-EPR provides robust models to predict the maximum dry unit weight, optimum moisture content, and California bearing ratio. The new models’ performance has also been compared with the empirical models proposed by different researchers. It was found from the comparisons that the new models provide enhanced accuracy in predictions as these models scored lower mean absolute error and root mean square error, mean values closer to one, and higher $a20-index$ and coefficient of correlation. Therefore, the new models can be used to ensure more optimised and robust design calculations.

One practical option for modifying an asphalt mixture’s performance is to use additives. This will help the mixture perform better against the damaging effects of traffic, loads, and climatic variations. In this regard, glass fiber (GF) has drawn much interest because of its positive effect. Therefore, this paper attempts to study the effect of glass fiber length and content on the performance and strength of asphalt mixtures. It also aims to determine the optimum glass fiber content and the best glass fiber length of modified asphalt mixtures. An experimental program is carried out, which includes the Marshall test, volumetric properties, freeze-thaw splitting test, immersion Marshall test, and wheel tracking test to characterize related properties of glass fiber incorporated in asphalt mixtures. Seven different percentages (0, 0.25, 0.5, 0.75, 1, 1.25, and 1.5) of glass fiber by total weight of aggregates in three various lengths are used to design 19 asphalt mixtures. Based on the results obtained, the performance of the asphalt mixture was enhanced remarkably after adding glass fiber. The use of various lengths of glass fiber led to a better-quality asphalt mixture in terms of volumetric properties, moisture damage resistance, and permanent deformation resistance. Specifically, asphalt mixtures made with (0.5%) glass fiber illustrated the highest quality, and adding (20 mm) length of glass fiber was better than (10 mm and 30 mm) glass fiber lengths. The results also show that adding (10 mm and 30 mm) lengths of glass fiber can improve the resistance of asphalt mixtures to water damage and permanent deformation compared with the control mixture (M0). The findings indicate the applicability of 20 mm glass fiber length in asphalt mixtures to achieve better resistance against moisture and reduce the chance of irreparable permanent deformation under growing traffic loads and hot climate changes. Although the inclusion of glass fiber in asphalt mixtures led to a modest increase (6%) in overall cost, the effective improvement in performance and extension of the service life of the asphalt pavement constitute a convincing argument for this approach, making it an attractive option. Finally, it was concluded that a higher amount of glass fiber (i.e., > 0.5%) and a length greater than (20 mm) could diminish the positive effect of glass fiber to improve the properties of glass fiber asphalt mixtures.