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Abstract Super absorbent polymer (SAP) has a capacity to enhance the characteristics of cementitious composites in both their fresh and hardened forms. However, it is essential to recognize that the strength of SAP concrete may decrease. By altering the concrete composition and selecting the appropriate type of SAP, it is possible to reduce this reduction. This work employs machine learning (ML) to tackle the issue of strength degradation. The analysis considers ten distinct variables linked to concrete composition and the type of SAP. The study uses machine learning approaches that involve both regression and classification tasks. The use of ensemble learning greatly improves the quality and accuracy of the results, showing its superiority in combining several models to produce more precise predictions. The findings demonstrate that the Support Vector Machines (SVM) and Extreme Gradient Boosting (XGBoost) regression algorithms accurately forecasted the percentage of reduction in strength in SAP concrete. These predictions were based on the concrete composition and SAP details, resulting in R2 values of 0.90 and 0.88, respectively. Furthermore, XGBoost exhibited the highest accuracy, reaching 0.94, when compared to the various categorization algorithms. According to the results, the mean squared error (MSE) of the ensemble model demonstrated superior outcomes. Furthermore, the SHapley Additive exPlanations (SHAP) reveal that some variables, including SAP%, SAP size, and compressive strength, have a significant influence on the strength reduction model. This study aims to bridge the gap between academic research and practical application by developing a web application that employs ensemble learning to precisely forecast the reduction in compressive strength caused by the usage of SAP.

Abstract By conducting an analysis of chloride migration in concrete, it is possible to enhance the durability of concrete structures and mitigate the risk of corrosion. In addition, the utilization of machine learning techniques that can effectively forecast the chloride migration coefficient of concrete shows potential as a financially viable and less complex substitute for labour-intensive experimental evaluations. The existing models for predicting chloride resistance encounter two primary challenges: the constraints imposed by a limited dataset and the absence of certain input variables. These factors collectively contribute to a decrease in the overall effectiveness of these models. Therefore, this study aims to propose an advanced approach for dataset cleaning, utilizing a comprehensive experimental dataset comprising 1073 pre-existing experimental outcomes. The proposed model for predicting the chloride diffusion coefficient incorporates various input variables, such as water content, cement content, slag content, fly ash content, silica fume content, fine aggregate content, coarse aggregate content, superplasticizer content, fresh density, compressive strength, age of compressive strength test, and age of migration test. The utilization of the artificial neural network (ANN) technique is also employed for the processing of missing data. The current supervised learning incorporates both regression and classification tasks. The efficacy of the proposed models for accurately predicting the chloride diffusion coefficient has been effectively validated. The findings indicate that the XGBoost and SVM algorithms exhibit superior performance compared to other regression prediction algorithms, as evidenced by their high R2 scores of 0.94 and 0.91, respectively. In relation to classification algorithms, the findings demonstrate that the Random Forest, LightGBM, and XGBoost models exhibit the highest levels of accuracy, specifically 0.93, 0.96, and 0.97, respectively. Furthermore, a website has been developed that is capable of predicting the chloride migration coefficient and chloride penetration resistance of concrete.

Abstract Due to the Covid-19 pandemic, using large amounts of personal protective equipment (PPE) throughout the world has extensively increased in recent years. The lack of a practical method to dispose of these recycled materials is one of the main concerns of researchers. Hence, comprehensive experimental tests were conducted in the present study to investigate the feasibility of using disposable gloves in mortars to achieve a sustainable mixture. Accordingly, latex and vinyl gloves as recycled fibers were considered in the experimental program to improve the sustainability of 3D printing concrete. As using these recycled materials causes some deficiencies for printing layers, different mineral and chemical admixtures were used in the present study, including graphene oxide nanomaterials, polyvinyl alcohol, Cloisite 15A nanoclay, and micro silica fume. Also, the hybrid use of latex, vinyl, and polypropylene (PP) fiber was considered to improve the printability of concrete mixtures containing waste fibers. Moreover, the effect of internal reinforcement was also considered by using plain steel wire mesh to increase the composite behavior of printed layers in this simplified experimental program. Results indicate that the synergic influence of recycled fibers and admixtures meaningfully enhanced the 3D printing properties of mortar so that about 20%, 80%, 50%, and more than 100% improvements were obtained for workability, direct tensile strength, flexural strength, and buildability index respectively. However, an average percentage − 28.3% reduction was recorded for the concrete compressive strength. Sustainability analysis also showed that using waste disposable gloves considerably reduced CO2 emissions.