Abstract

High-strength concretes (HSC) have been widely applied in civil construction. However, this material does not perform well in fire situations due to spalling, a phenomenon that can be significantly mitigated by adding synthetic fibers to HSC cementitious matrix. Estimating HSC physical and mechanical properties for different compositions and temperatures can therefore be useful, as the structure may be pre-designed without requiring prior material characterization in a fire situation. With this perspective, this paper aimed to propose regression models to estimate eight properties of High-Strength Concrete with Polypropylene Fibers (HSC-PPF), namely compressive strength, tensile strength, static modulus of elasticity, dynamic modulus of elasticity, ultrasonic pulse velocity, electrical resistivity, water absorption, and weight loss, at high temperatures (up to 800 °C). For this, it was evaluated the effective correlation between mechanical responses and independent variables, such as temperature, water absorption, and dynamic modulus, and the accuracy of regression models was assessed. The results obtained indicated that, among the models proposed, the two multiple regressions were better than the simple one for estimating HSC-PPF properties since they presented high adjustments (R2) and low mean absolute percentage (MAPE), and coefficient of variation (CV) errors. This methodology can be used in a wide variety of fiber-reinforced HSCs.

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