@article{scholars20229,
          volume = {28},
            note = {cited By 1},
             doi = {10.1016/j.jmrt.2024.01.059},
            year = {2024},
           title = {Tailoring an engineered cementitious composite with enhanced mechanical performance at ambient and elevated temperatures using graphene oxide and crumb rubber},
         journal = {Journal of Materials Research and Technology},
           pages = {4508--4530},
          author = {Abdulkadir, I. and Mohammed, B. S. and Al-Yacouby, A. M. and Woen, E. L. and Tafsirojjaman, T.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85182503738&doi=10.1016\%2fj.jmrt.2024.01.059&partnerID=40&md5=79bea49039b809ce57db0dc44118a05f},
        keywords = {Graphene; Multiobjective optimization; Rubber; Surface properties, Ambients; Closed environment; Crumb rubber; Elevated temperature; Engineered cementitious composites; Graphene oxides; Mechanical performance; Performance; Response surface methodology; Response-surface methodology, Aggregates},
        abstract = {With the increased use of engineered cementitious composite (ECC) in closed environments and as a structural material, it is necessary to fully understand its performance under elevated temperatures. This research investigates the response of ECC to ambient and elevated temperatures and addresses the issue of explosive spalling by incorporating graphene oxide (GO) and crumb rubber (CR). Twenty GO-modified rubberized ECC mixes were designed using Response surface methodology (RSM), considering GO content, GO concentration for CR pretreatment, CR replacement of fine aggregate, and elevated temperature as the input variables. Results show that mixes with GO and GO-treated CR outperform those without GO or untreated CR at ambient and elevated temperatures. Response predictive models exhibited high coefficient of determination (R2) values ranging from 84  to 96 . Multi-objective optimization yielded optimum input factors, resulting in improved mechanical properties that were experimentally validated to confirm the accuracy of the developed models. {\^A}{\copyright} 2024 The Authors}
}