Application of soft computing in predicting the compressive strength of self-compacted concrete containing recyclable aggregate

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

Multiscale soft computing-based model of shear strength of steel fibre-reinforced concrete beams

Concrete is weak in tension, so steel fibres are added to the concrete members to increase shear capability. The shear capacity of steel fibre-reinforced concrete (SFRC) beams is crucial when building reinforced concrete structures. Creating a precise equation to determine the shear resistance of SFRC beams is challenging since many factors can influence the shear capacity of these beams. In addition, the precision available equations to predict the shear capacity are examined. The current research aims to examine the available equations and propose novel and more accurate model to predict the shear capacity of SFRC beams. An innovative evolutionary polynomial regression analysis (EPR- MOGA) is utilized to propose the new equation. The proposed equation offered improved prediction and increased accuracy compared to available equations, where it scored a lower mean absolute error (MAE) and root mean square error (RMSE), a mean (μ) close to the optimum value of 1.0 and a higher coefficient of determination (R 2) when a comparison with literature was conducted. Therefore, the new equation can be employed to assure more resilient and optimized design calculations due to their improved performance.</p