In the last few decades, the concrete industry has been massively expanded with the adoption of various kinds of binding materials. As a substitute to cement and in an effort to relieve ecofriendly difficulties linked with cement creation, the utilization of industrial waste as cementitious material can sharply reduce the amount of trash disposed of in lakes and landfills. With respect to the mechanical properties, durability and thermal behavior, ground-granulated blast-furnace slag (GGBS) delineates a rational way to develop sustainable cement and concrete. Apart from environmental benefits, the replacement of cement by GGBS illustrates an adequate way to mitigate the economic impact. Although many researchers concentrate on utilizing GGBS in concrete production, knowledge is scattered, and additional research is needed to better understand relationships among a wide spectrum of key questions and to more accurately determine these preliminary findings. This work aims to shed some light on the scientific literature focusing on the use and effectiveness of GGBS as an alternative to cement. First and foremost, basic information on GGBS manufacturing and its physical, chemical and hydraulic activity and heat of hydration are thoroughly discussed. In a following step, fresh concrete properties, such as flowability and mechanical strength, are examined. Furthermore, the durability of concrete, such as density, permeability, acid resistance, carbonation depth and dry shrinkage, are also reviewed and interpreted. It can be deduced that the chemical structure of GGBS is parallel to that of cement, as it shows the creditability of being partially integrated and overall suggests an alternative to Ordinary Portland Cement (OPC). On the basis of such adjustments, the mechanical strength of concrete with GGBS has shown an increase, to a certain degree; however, the flowability of concrete has been reduced. In addition, the durability of concrete containing GGBS cement is shown to be superior. The optimum percentage of GGBS is an essential aspect of better performance. Previous studies have suggested different optimum percentages of GGBS varying from 10 to 20%, depending on the source of GGBS, concrete mix design and particle size of GGBS. Finally, the review also presents some basic process improvement tips for future generations to use GGBS in concrete.

Prior studies in the literature show promising results regarding the improvements in strength and durability of concrete upon incorporation of glass fibers into concrete formulations. However, the knowledge regarding glass fiber usage in concrete is scattered. Moreover, this makes it challenging to understand the behavior of glass fiber-reinforced concrete. Therefore, a detailed review is required on glass fiber-reinforced concrete. This paper provides a compressive analysis of glass fiber-reinforced composites. All-important properties of concrete such as flowability, compressive, flexural, tensile strength and modulus of elasticity were presented in this review article. Furthermore, durability aspects such as chloride ion penetration, water absorption, ultrasonic pulse velocity (UPV) and acid resistance were also considered. Finally, the bond strength of the fiber and cement paste was examined via scanning electron microscopy. Results indicate that glass fibers improved concrete’s strength and durability but decreased the concrete’s flowability. Higher glass fiber doses slightly decreased the mechanical performance of concrete due to lack of workability. The typical optimum dose is recommended at 2.0%. However, a higher dose of plasticizer was recommended for a higher dose of glass fiber (beyond 2.0%). The review also identifies research gaps that should be addressed in future studies.

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.

Recently, research has centered on developing new approaches, such as supervised machine learning techniques, that can compute the mechanical characteristics of materials without investing much effort, time, or money in experimentation. To predict the 28-day compressive strength of steel fiber–reinforced concrete (SFRC), machine learning techniques, i.e., individual and ensemble models, were considered. For this study, two ensemble approaches (SVR AdaBoost and SVR bagging) and one individual technique (support vector regression (SVR)) were used. Coefficient of determination (R²), statistical assessment, and k-fold cross validation were carried out to scrutinize the efficiency of each approach used. In addition, a sensitivity technique was used to assess the influence of parameters on the prediction results. It was discovered that all of the approaches used performed better in terms of forecasting the outcomes. The SVR AdaBoost method was the most precise, with R² = 0.96, as opposed to SVR bagging and support vector regression, which had R² values of 0.87 and 0.81, respectively. Furthermore, based on the lowered error values (MAE = 4.4 MPa, RMSE = 8 MPa), statistical and k-fold cross validation tests verified the optimum performance of SVR AdaBoost. The forecast performance of the SVR bagging models, on the other hand, was equally satisfactory. In order to predict the mechanical characteristics of other construction materials, these ensemble machine learning approaches can be applied.

Disastrous natural hazards, such as landslides, floods, and forest fires cause a serious threat to natural resources, assets and human lives. Consequently, landslide risk assessment has become requisite for managing the resources in future. This study was designed to develop four ensemble metaheuristic machine learning algorithms, such as grey wolf optimized based artificial neural network (GW-ANN), grey wolf optimized based random forest (GW-RF), particle swarm optimization optimized based ANN (PSO-ANN), and PSO optimized based RF for modeling rainfall-induced landslide susceptibility (LS) in Aqabat Al-Sulbat, Asir region, Saudi Arabia, which observes landslide frequently. To obtain very high precision and robust prediction from machine learning algorithms, the grey wolf and PSO optimization algorithms were integrated to develop new ensemble machine learning techniques. Subsequently, LS maps produced by training dataset were validated using the receiver operating characteristics (ROC) curve based on the testing dataset. Based on the area under curve (AUC) value of ROC curve, the best method for LS modeling was selected. We developed ROC curve-based sensitivity analysis to investigate the influence of the parameters for LS modeling. The Gumble extreme value distribution was employed to estimate the rainfall at 2, 5, 10, 20, 50, and 100 year return periods. Then, the landslide hazard maps were prepared at different return periods by integrating the best LS model and estimated rainfall at different return periods. The theory of danger pixels was employed to prepare a final risk assessment of the resources, which have been exposed to the landslide. The results showed that 27–42 and 6–15 km² were predicted as the very high and high LS zones using four ensemble metaheuristic machine learning algorithms. Based on the area under curve (AUC) of ROC, GR-ANN (AUC-0.905) appeared as the best model for LS modeling. The areas under high and very high landslide hazard were gradually increased over the progression of time (26 km² at the 2 year return period and 40 km² at the 100 year return period for the high landslide hazard zone, and 6 km² at the 2 year return period and 20 km² at the 100 year return period for the very high landslide hazard zone). Similarly, the areas of danger pixel also increased gradually from the 2 to 100 year return periods (37 km² to 62 km²). Various natural resources, such as scrubland, built up, and sparse vegetation, were identified under risk zone due to landslide hazards. In addition, these resources would be exposed extensively to landslides over the advancement of return periods. Therefore, the outcome of the present study will help planners and scientists to propose high precision management plans for protecting natural resources, which have been exposed to landslides.