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 industrial materials such as Blast Furnace Slag (BFS) and Fly Ash (F. Ash) will provide an effective alternative strategy for producing eco-friendly and sustainable concrete production. However, testing is a time-consuming process, and the use of soft machine learning (ML) techniques to predict concrete strength can help speed up the procedure. In this study, artificial neural networks (ANNs) and decision trees (DTs) were used for predicting the compressive strength of the concrete. A total of 1030 datasets with eight factors (OPC, F. Ash, BFS, water, days, SP, FA, and CA) were used as input variables for the prediction of concrete compressive strength (response) with the help of training and testing individual models. The reliability and accuracy of the developed models are evaluated in terms of statistical analysis such as R², RMSE, MAD and SSE. Both models showed a strong correlation and high accuracy between predicted and actual Compressive Strength (CS) along with the eight factors. The DT model gave a significant relation to the CS with R² values of 0.943 and 0.836, respectively. Hence, the ANNs and DT models can be utilized to predict and train the compressive strength of high-performance concrete and to achieve long-term sustainability. This study will help in the development of prediction models for composite materials for buildings.

This works promotes the idea of simultaneous incorporation of ground granulated blast furnace slag (GGBS) and jute fiber (JF) to develop eco-friendly and ductile concrete. For this purpose, an experimental investigation was conducted, where two concrete families were produced using 0% and 25% GGBS as partial replacements for cement. JF was incorporated as 0%, 0.25%, and 0.5% by volume fractions. Effect of plasticizer was also studied on behavior of jute fiber reinforced concrete (JFRC) with GGBS. Compressive strength-CS, splitting tensile strength-STS, flexural strength-FS, water absorption-WA, chloride ion penetration depth-CIPD, and electrical resistivity-ER were studied. The results showed that with the increasing JF content the CS of concrete declined and STS and FS improved. However, the positive effect of JF on CS was observed in mixes containing GGBS with or without a plasticizer. Incorporation of 0.5% JF without plasticizer improved the STS and FS of concrete by 11% and 17%, respectively. However, after achieving the target workability using plasticizer, the net gains in STS and FS due to the 0.5% addition of JF were 24.3% and 29%, respectively. The negative effects of hydrophilic nature of JF on WA and CIPD resistance of concrete were minimized by using GGBS and controlling workability.

This research work is devoted to the experimental investigation of both rheological and mechanical properties of self-compacting concrete (SCC) produced with waste galvanized copper wire fiber and rice husk ash (RHA). In the study, three different volume fractions of 0.5 p to 0.75 percent, 1 percent of scrap copper wire fiber as reinforcing material, and 2 percent RHA as cement replacement were used. To evaluate the fresh characteristics of SCC, the slump flow, J-ring, and V-funnel experiments were conducted for this investigation. Compressive strength, splitting tensile strength, and flexural strength of the concrete were conducted to assess the hardened properties. The test was carried out to compare each characteristic of plain SCC with this modified SCC mixture, containing RHA as pozzolanic materials and copper fiber as reinforcing material. Incorporating copper fiber in the SCC leads to a drop in fresh properties compared to plain SCC but remains within an acceptable range. On the other hand, the inclusion of 2% RHA makes the SCC more viscous. Although adding 2% RHA and 1% copper wire in SCC provide the highest strength, this mix has an unacceptable passing ability. The SCC mix prepared with 2% RHA and 0.75% copper fiber is suggested to be optimum in terms of the overall performance. According to this study, adding metallic fiber reinforcement like copper wire and mineral admixture like RHA can improve the mechanical properties of SCC up to a certain level.