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Several types of research currently use machine learning (ML) methods to estimate the mechanical characteristics of concrete. This study aimed to compare the capacities of four ML methods: eXtreme gradient boosting (XG Boost), gradient boosting (GB), Cat boosting (CB), and extra trees regressor (ETR), to predict the splitting tensile strength of 28-day-old self-compacting concrete (SCC) made from recycled aggregates (RA), using data obtained from the literature. A database of 381 samples from literature published in scientific journals was used to develop the models. The samples were randomly divided into three sets: training, validation, and test, with each having 267 (70%), 57 (15%), and 57 (15%) samples, respectively. The coefficient of determination (R2), root mean square error (RMSE), and mean absolute error (MAE) metrics were used to evaluate the models. For the training data set, the results showed that all four models could predict the splitting tensile strength of SCC made with RA because the R2 values for each model had significance higher than 0.75. XG Boost was the model with the best performance, showing the highest R2 value of R2 = 0.8423, as well as the lowest values of RMSE (=0.0581) and MAE (=0.0443), when compared with the GB, CB, and ETR models. Therefore, XG Boost was considered the best model for predicting the splitting tensile strength of 28-day-old SCC made with RA. Sensitivity analysis revealed that the variable contributing the most to the split tensile strength of this material after 28 days was cement.

This article presents an overview of the bibliographic picture of the design parameter’s influence on the mix proportion of self-compacting concrete with recycled aggregate. Design parameters like water-cement ratio, water to paste ratio, and percentage of superplasticizers are considered in this review. Standardization and recent research on the usage of recycled aggregates in self-compacting concrete (SCC) exploit its significance in the construction sector. The usage of recycled aggregate not only resolves the negative impacts on the environment but also prevents the usage of natural resources. Furthermore, it is necessary to understand the recycled aggregate property’s role in a mixed design and SCC properties. Design parameters are not only influenced by a mix design but also play a key role in SCC’s fresh properties. Hence, in this overview, properties of SCC ingredients, calculation of design parameters in mix design, the effect of design parameters on fresh concrete properties, and the evolution of fresh concrete properties are studied.

The worldwide production of sugar generates large volumes of bagasse wastes, which are burnt in uncontrolled manner for heating boiler, which are deposited in landfills, which create negative effects in the environment. The ash obtained by burning bagasse is generally used as Supplementary Cementing Material (SCM) in concrete production without proper knowledge of pozzolanic material characterization. This paper summarizes the results obtained from the various techniques to determine pozzolanic mineral profiles in sugarcane bagasse ash (SCBA). Techniques employed in the present study include X-Ray Diffraction (XRD), Energy-Dispersive X-ray Analysis (EDAX) spectrometer, Fourier Transform Infra-Red Spectroscopy (FTIR), Scanning Electron Microscopy (SEM) and Thermal Analysis [Thermo-Gravimetric Analysis (TGA) and Derivative Thermo-Gravimetric (DTG)] in order to understand the type, form, nature, morphology, concentration, etc. of pozzolanic minerals.

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