This research employed machine learning (ML) and SHapley Additive ExPlanations (SHAP) methods to assess the strength and impact of raw ingredients of cement mortar (CM) incorporated with waste glass powder (WGP). The data required for this study were generated using an experimental approach. Two ML methods were employed, i.e., gradient boosting and random forest, for compressive strength (CS) and flexural strength (FS) estimation. The performance of ML approaches was evaluated by comparing the coefficient of determination (R2), statistical checks, k-fold assessment, and analyzing the variation between experimental and estimated strength. The results of the ML-based modeling approaches revealed that the gradient boosting model had a good degree of precision, but the random forest model predicted the strength of the WGP-based CM with a greater degree of precision for CS and FS prediction. The SHAP analysis revealed that fine aggregate was a critical raw material, with a stronger negative link to the strength of the material, whereas WGP and cement had a greater positive effect on the strength of CM. Utilizing such approaches will benefit the building sector by supporting the progress of rapid and inexpensive approaches for identifying material attributes and the impact of raw ingredients.

Using solid waste in building materials is an efficient approach to achieving sustainability goals. Also, the application of modern methods like artificial intelligence is gaining attention. In this regard, the flexural strength (FS) of cementitious composites (CCs) incorporating waste glass powder (WGP) was evaluated via both experimental and machine learning (ML) methods. WGP was utilized to partially substitute cement and fine aggregate separately at replacement levels of 0%, 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%. At first, the FS of WGP-based CCs was determined experimentally. The generated data, which included six inputs, was then used to run ML techniques to forecast the FS. For FS estimation, two ML approaches were used, including a support vector machine and a bagging regressor. The effectiveness of ML models was assessed by the coefficient of determination (R2), k-fold techniques, statistical tests, and examining the variation amongst experimental and forecasted FS. The use of WGP improved the FS of CCs, as determined by the experimental results. The highest FS was obtained when 10% and 15% WGP was utilized as a cement and fine aggregate replacement, respectively. The modeling approaches’ results revealed that the support vector machine method had a fair level of accuracy, but the bagging regressor method had a greater level of accuracy in estimating the FS. Using ML strategies will benefit the building industry by expediting cost-effective and rapid solutions for analyzing material characteristics.

Climate change has become trending news due to its serious impacts on Earth. Initiatives are being taken to lessen the impact of climate change and mitigate it. Among the different initiatives, researchers are aiming to find suitable alternatives for cement. This study is a humble effort to effectively utilize industrial- and agricultural-waste-based pozzolanic materials in concrete to make it economical and environmentally friendly. For this purpose, a ternary blend of binders (i.e., cement, fly ash, and rice husk ash) was employed in concrete. Different variables such as the quantity of different binders, fine and coarse aggregates, water, superplasticizer, and the age of the samples were considered to study their influence on the compressive strength of the ternary blended concrete using gene expression programming (GEP) and artificial neural networking (ANN). The performance of these two models was evaluated using R2, RMSE, and a comparison of regression slopes. It was observed that the GEP model with 100 chromosomes, a head size of 10, and five genes resulted in an optimum GEP model, as apparent from its high R2 value of 0.80 and 0.70 in the TR and TS phase, respectively. However, the ANN model performed better than the GEP model, as evident from its higher R2 value of 0.94 and 0.88 in the TR and TS phase, respectively. Similarly, lower values of RMSE and MAE were observed for the ANN model in comparison to the GEP model. The regression slope analysis revealed that the predicted values obtained from the ANN model were in good agreement with the experimental values, as shown by its higher R2 value (0.89) compared with that of the GEP model (R2 = 0.80). Subsequently, parametric analysis of the ANN model revealed that the addition of pozzolanic materials enhanced the compressive strength of the ternary blended concrete samples. Additionally, we observed that the compressive strength of the ternary blended concrete samples increased rapidly within the first 28 days of casting.

This study examined the bibliographic data on fiber-reinforced geopolymers (FRGPs) using scientometrics to determine their important features. Manual review articles are inadequate in their capability to connect various segments of literature in an ordered and systematic manner. Scientific mapping, co-citation, and co-occurrence are the difficult aspects of current research. The Scopus database was utilized to find and obtain the data needed to achieve the study’s aims. The VOSviewer application was employed to assess the literature records from 751 publications, including citation, bibliographic, keyword, and abstract details. Significant publishing outlets, keywords, prolific researchers in terms of citations and articles published, top-cited documents, and locations actively participating in FRGP investigations were identified during the data review. The possible uses of FRGP were also highlighted. The scientometric analysis revealed that the most frequently used keywords in FRGP research are inorganic polymers, geopolymers, reinforcement, geopolymer, and compressive strength. Additionally, 27 authors have published more than 10 articles on FRGP, and 29 articles have received more than 100 citations up to June 2022. Due to the graphical illustration and quantitative contribution of scholars and countries, this study can support scholars in building joint ventures and communicating innovative ideas and practices.

A progressive advance in the construction sector is attained by employing ultra-high performance concrete (UHPC) technology. Rigorous efforts have been made in this research domain to have remarkable quality levels with 150 MPa or more strength and significant durability, which was impossible previously. Steel fiber incorporation in UHPC is vital in improving its mechanical characteristics. This review on the incorporation of steel fibers in UHPC evaluates, identifies, and synthesizes research outcomes for creating a summary of current evidence that can contribute to evidence-based practice. This study summarized a review of the literature on steel fibers’ effect on UHPC, intending to explore its essential aspects. The aim is to summarize the literature in this research domain and provide guidance for future research. Moreover, the basic requirements and materials, mixing and casting, mechanical properties, modern applications, advantages and disadvantages, and future perspectives associated with steel fibers reinforced UHPC in the construction sector are discussed. It is revealed from the conducted analysis that the most widely applied keyword is “steel fibers.” Due to the graphical illustration of the contributing studies, the current work may benefit academic scholars in sharing novel techniques and ideas and establishing collaborative efforts. Furthermore, the present work reveals that steel fibers have the potential to enhance the mechanical properties of UHPC; however, the large-scale production and applications of steel fiber-reinforced UHPC are controlled by parameters like fiber content and geometry.

This study investigates the resistance of concrete to Rapid Chloride ions Penetration (RCP) as an indirect measure of the concrete’s durability. The RCP resistance of concrete is modelled in multi-expression programming approach using different input variables, such as, age of concrete, amount of binder, fine aggregate, coarse aggregate, water to binder ratio, metakaolin content and the compressive strength (CS) of concrete. The parametric investigation was carried out by varying the hyperparameters, i.e., number of subpopulations Nsub, subpopulation size Ssize, crossover probability Cprob, mutation probability Mprob, tournament size Tsize, code length Cleng, and number of generations Ngener to get an optimum model. The performance of all the 29 number of trained models were assessed by comparing mean absolute error (MAE) values. The optimum model was obtained for Nsub = 50, Ssize = 100, Cprob = 0.9, Mprob = 0.01, Tsize = 9, Cleng = 100, and Ngener = 300 with MAE of 279.17 in case of training (TR) phase, whereas 301.66 for testing (TS) phase. The regression slope analysis revealed that the predicted values are in good agreement with the experimental values, as evident from their higher R and R2 values equaling 0.96 and 0.93 (for the TR phase), and 0.92 and 0.90 (for the TS phase), respectively. Similarly, parametric and sensitivity analyses revealed that the RCP resistance is governed by the age of concrete, amount of binder, concrete CS, and aggregate quantity in the concrete mix. Among all the input variables, the RCP resistance sharply increased within the first 28 days age of the concrete specimen and similarly plummeted with increasing the quantity of fine aggregate, thus validating the model results.

Considerable research has been carried out on shotcrete for rock supports in tunneling and mining. This study used a novel approach based on scientometric analysis of shotcrete’s bibliographic data to determine its essential components. Knowledge mapping, co-occurrence, and citation analysis are the most difficult parts of current research. The information needed to complete the study was retrieved from the Scopus search engine. There were 1267 articles considered, and the VOSviewer program was used to analyze. Important publishing sources, keyword analysis, writers that have the most contribution in terms of citations and publications, the most-cited articles, and the regions most actively engaged in shotcrete research were identified throughout the data review process. Also, shotcrete’s current applications and main limitations were explored. In addition to its applications in tunneling and mining, shotcrete can also be used for refractory linings, soil stabilization, structural restorations, and the construction of river barriers, swimming pools, shell structures, domes, and even buildings. However, the durability performance of shotcrete is poorer compared to conventional concrete. In this regard, additional in-depth research is needed to strengthen the durability features of shotcrete. The graphical description and quantitative contribution of scholars and countries in this study might help academics build collaborative projects and disseminate new ideas and methods.