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.

Conventional concrete causes significant environmental problems, including resource depletion, high CO₂ emissions, and high energy consumption. Steel slag aggregate (SSA), a by-product of the steelmaking industry, offers a sustainable alternative due to its environmental benefits and improved mechanical properties. This study examined the predictive power of four modeling techniques—Gene Expression Programming (GEP), an Artificial Neural Network (ANN), Random Forest Regression (RFR), and Gradient Boosting (GB)—to predict the compressive strength (CS) of SSA concrete. Using 367 datasets from the literature, six input variables (cement, water, granulated furnace slag, superplasticizer, coarse aggregate, fine aggregate, and age) were utilized to predict compressive strength. The models’ performance was evaluated using statistical measures such as the mean absolute error (MAE), root mean squared error (RMSE), mean values, and coefficient of determination (R²). Results indicated that the GB model consistently outperformed RFR, GEP, and the ANN, achieving the highest R² values of 0.99 and 0.96 for the training and testing dataset, respectively, followed by RFR with R² values of 0.97 (training) and 0.93 (testing), GEP with R² values of 0.85 (training) and 0.87 (testing), and ANN with R² values of 0.61 (training) and 0.82 (testing). Additionally, the GB model had the lowest MAE values of 0.79 MPa (training) and 2.61 MPa (testing) and RMSE values of 1.90 MPa (training) and 3.95 MPa (testing). This research aims to advance predictive modeling in sustainable construction through analysis and well-defined conclusions.

The bond between steel and concrete in reinforced concrete structures is a multifaceted and intricate phenomenon that plays a vital role in the design and overall performance of such structures. It refers to the adhesion and mechanical interlock between the steel reinforcement bars and the surrounding concrete matrix. Under elevated temperatures, the bond is more complex under higher temperatures, yet having an accurate estimate is an important factor in design. Therefore, this paper focuses on using data-driven models to explore the performance of the concrete-steel bond under high temperatures using a Gene Expression Programming (GEP) soft computing model. The GEP models are developed to simulate the bond performance in order to understand the effect of high temperatures on the concrete-steel bond. The results were compared to the multi-objective evolutionary polynomial regression analysis (MOGA-EPR) models for different input variables. The new model would help the designers with strength predictions of the bond in fire. The dataset used for the model was obtained from experiments conducted in a laboratory setting that gathered a 316-point database to investigate concrete bond strength at a range of temperatures and with different fibre contents. This study also investigates the impact of the different variables on the equation using sensitivity analysis. The results show that the GEP models are able to predict bond performance with different input variables accurately. This study provides a useful tool for engineers to better understand the concrete-steel bond behaviour under high temperatures and predict concrete-steel bond performance under high temperatures.

This paper presents a study to predict the shear strength of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques. The methodology involves the development of a Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) and Gene Expression Programming (GEP) models. The input variables considered are the longitudinal reinforcement ratio, recycled coarse aggregate ratio, beam cross-section dimensions, and concrete compressive strength. Data collected from the literature were used to train and validate the models. The results showed that the MOGA-EPR and GEP models can accurately predict the shear strength of beams without stirrups. The models also performed better than equations from the codes and literature. This study provides an alternative approach to accurately predict the shear strength of reinforced recycled aggregate concrete beams without stirrups.