Incorporating crumb rubber in slag-based geopolymer: Experimental work and predictive modelling

• Published in: Case Studies in Construction Materials 2024-12, Vol.21, p.e03893, Article e03893
• Main Authors: Raut, Ashwin Narendra, Alyaseen, Ahmad, Khan, Afzal Husain, Janga, Supriya, Poddar, Arunava, Sihag, Parveen, Sumra, Vinisha, Lee, Daeho, Kumar, Raj, Singh, Tej
• Format: Article
• Language: English
• Subjects: Chemical compositions; Meta-ensemble models; Microstructural analysis; Rubberized geopolymer concrete; Sensitivity analysis; Uncertainty analysis
• ISSN: 2214-5095; 2214-5095
• DOI: 10.1016/j.cscm.2024.e03893

Crumb rubber (CR), a prevalent hazardous waste material, poses significant environmental challenges that necessitate innovative management strategies. Recent studies have focused on incorporating CR and ground granulated blast furnace slag (GGBS) into geopolymer concrete (GC) to address this challenge. This research presents a novel approach that combines experimental analysis with machine learning (ML) techniques to investigate the chemical effects of these materials on GC. Initially, compressive strength (CS) tests are conducted on rubberized geopolymer (RG) concrete, and the resulting data are further analyzed through ML algorithms to enhance the understanding of material behavior. Despite numerous attempts by researchers to integrate CR into geopolymer-based materials, the challenge of mitigating strength degradation persists. Therefore, it becomes imperative to construct a predictive model that relies on new variables influencing the strength characteristics. This research develops a predictive model to assess concrete compressive strength (CS). Key variables such as CR grade (particle size), incorporation percentage, and oxide ratio were analyzed for their impact on geopolymer strength. Employing ensemble techniques, namely Bagging (BA) and Gradient boosting (GB) with five learners (M5P, random forest (RF), random tree (RT), reduced error pruning tree (REPT), and support vector machine (SVM), the study found that M5P-GB models offered the most accurate CS predictions with the highest accuracy and lowest errors. Moreover, the dataset was checked via the k-fold cross-validation technique and confirmed the developed models' robustness, reliability, and effectiveness. Furthermore, uncertainty analysis revealed that GB-M5P-unpruned models-maintained uncertainty percentages of 14.769 % at 7 days and 11.277 % at 28 days, which was under the 35 % threshold. Additionally, the sensitivity analysis identified the CR grade and replacement percentage by volume of crusher dust (CD) as the most critical factors affecting CS. These findings underscore the importance of considering predictive model development in decision-making processes related to RG concrete properties, providing valuable insights into optimizing the model's robustness and reliability for practical applications. However, the study's reliance on specific input parameters and controlled laboratory conditions may limit the generalizability of the predictive models, necessitating further validation un