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    Fire resistance of hybrid fiber reinforced SCC: Effect of use of polyvinyl-alcohol or polypropylene with single and binary steel fiber
    (Techno-Press, 2023) Turk, Kazim; Kina, Ceren; Balalan, Esma
    This study presents the experimental results performed to evaluate the effects of Polyvinyl-alcohol (PVA) and Polypropylene (PP) fibers on the fresh and residual mechanical properties of the hybrid fiber reinforced SCC before and after the exposure of 250 & DEG;C, 500 & DEG;C and 750 & DEG;C temperatures. The compressive and splitting tensile strength, modulus of rupture (MOR), ultrasonic pulse velocity (UPV) as well as toughness and weight loss were investigated at different temperatures. PVA and PP fibers were added into SCC mixtures having only macro steel fiber and also having binary hybridization of both macro and micro steel fiber. The results showed that the use of micro steel fiber replaced by macro steel fiber improved the fresh and hardened properties compared to the use of only macro steel fiber. Moreover, it was emphasized that PVA or PP enhanced the residual flexural performance of SCC, generally, while it negatively influenced the workability, weight loss, UPV and the residual strengths with regards to the use of single steel fiber and binary steel fiber hybridization. Compared to the effect of synthetic fibers, PP had slightly more positive effect in the view of workability while PVA enhanced the residual mechanical properties more.
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    Machine Learning Prediction of Residual Mechanical Strength of Hybrid-Fiber-Reinforced Self-consolidating Concrete Exposed to Elevated Temperature
    (Springer, 2023) Turk, Kazim; Kina, Ceren; Tanyildizi, Harun; Balalan, Esma; Nehdi, Moncef L. L.
    Establishing the engineering properties of cement-based composites at elevated temperature requires costly, laborious, and time-consuming experimental work. Data-driven models can provide a robust and efficient alternative. In this study, extreme learning machine (ELM), support vector machine (SVM), artificial neural network (ANN), and decision tree (DT) models were trained to predict the residual compressive, splitting tensile, and flexural strengths of hybrid fiber-reinforced self-compacting concrete (HFR-SCC) exposed to high temperatures. Mixtures including macro and micro steel fibers, polyvinyl alcohol (PVA), and polypropylene (PP) were subjected to different temperature levels, leading to an experimental database of 360 specimens. Eleven input parameters including cement, fly ash, water, sand, gravel, fiber type, water reducer, and temperature were deployed. The residual mechanical strengths were targeted as output parameters. ANOVA was used to explore the influence of input parameters. Temperature was found to be the most influential parameter. Dataset consisting of 114 instances was retrieved from pertinent literature and used along with the authors' experimentally generated dataset for residual strength prediction. The experimental results were compared with predictions of ELM, SVM, ANN, and DT. ELM achieved superior performance and can offer a robust tool for predicting the residual mechanical strengths of HFR-SCC upon exposure to high temperature.