Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R²), statistical evaluation, and k-fold cross-validation. A sensitivity approach was also used to analyze the impact of factors on predicting results. The analysis showed that the GB and RF models performed well, and the XGB approach was in the acceptable range. Gradient Boosting showed the highest precision with an R² of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R² values of 0.94 and 0.86, respectively. Moreover, statistical and k-fold cross-validation studies confirmed that Gradient Boosting was the best performer, followed by Random Forest (RF), based on reduced error levels. The Extreme Gradient Boosting model performance was satisfactory. These ensemble machine learning algorithms can benefit the construction sector by providing fast and better analysis of material properties, especially for fiber-reinforced concrete.

The utilization of waste glass with micro- and nanoparticles in ultra-high-performance concrete (UHPC) has garnered significant interest due to its potential to enhance sustainability and material performance. This study focuses on the implications of integrating microwaste glass (MG) and nanowaste glass in the presence of waste foundry sand and its impact on the properties of UHPC. The particular emphasis of the current work is on compressive strength, tensile strength, sorptivity, and microstructure. It is found that MG enhances compressive strength, decreased tensile strength, reduced sorptivity, and a more compact microstructure. The results indicate that replacing cement with 20% microglass achieves the optimal compressive strength by increasing up to 11.6% at 7 days, 9.5% at 28 days, and 10.18% at 56 days. Nanowaste glass, owing to its increased reactivity and larger surface area, accelerates calcium silicate hydrate formation and improves compressive strength. At the same time, the effective utilization of nanowaste glass improves long-term resilience with an optimum compressive strength at 1.5% replacement ratios of 17.5, 18.9, and 16% at 7, 28, and 56 days, respectively. Splitting tensile strength increased by 16% at 20% MG and 21% at 1.5% nanowaste glass, respectively. Utilizing MG and nanowaste glass in UHPC with waste foundry sand is a promising method for boosting material performance and minimizing environmental impact.