@article{scholars5602,
            year = {2015},
         journal = {Journal of Materials in Civil Engineering},
       publisher = {American Society of Civil Engineers (ASCE)},
             doi = {10.1061/(ASCE)MT.1943-5533.0001279},
          number = {12},
            note = {cited By 32},
          volume = {27},
           title = {Strength prediction models for PVA fiber-reinforced high-strength concrete},
          author = {Nuruddin, M. F. and Ullah Khan, S. and Shafiq, N. and Ayub, T.},
            issn = {08991561},
        abstract = {During the last decade, synthetic fibers have been used widely in the structure application; however, the strength models of synthetic fiber-reinforced concrete are not available, as most of the models have been proposed for steel fiber-reinforced concrete only. Extensive experimental investigation has been conducted on poly-vinyl alcohol (PVA) fiber-reinforced high-strength concrete to develop the strength model based on multiple linear regressions analysis through least square error. Regression models have been obtained for the different responses of concrete as a function of process variables, i.e., compressive strength of concrete, fiber matrix interface, and fraction of metakaolin (MK) as cement-replacing material. A total of 50 mixes of concrete have been examined using metakaolin of 0, 5, 10, 15, and 20 by weight of cement and PVA fibers of aspect ratio 45, 60, 90, and 120 with volume fraction of 0, 1, 2, and 3. Five mixes without PVA fiber have been used as control mixes. For each mix, the compressive strength, splitting tensile strength, modulus of rupture, and modulus of elasticity have been determined at the age of 7, 28, 56, and 90 days. Moreover, models have been compared with the artificial neural network and existing predictive models of steel fiber-reinforced concrete. The existing models of steel fiber-reinforced concrete have not been found to be applicable to synthetic fiber-reinforced concrete. However, the proposed models are closely fit to the experimental results, and the results are comparative with the artificial neural network approach. {\^A}{\copyright} 2015 American Society of Civil Engineers.},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84947746818&doi=10.1061\%2f\%28ASCE\%29MT.1943-5533.0001279&partnerID=40&md5=999f2c7971d5cca6269935e59eea5b86},
        keywords = {Aspect ratio; Cements; Compressive strength; Concrete construction; High performance concrete; Neural networks; Predictive analytics; Regression analysis; Reinforced plastics; Steel fibers; Synthetic fibers; Tensile strength, High strength concretes; Metakaolins; Modulus of rupture; Splitting tensile strength; Strength models, Fiber reinforced concrete, artificial neural network; compressive strength; experimental study; kaolin; model; prediction; reinforced concrete; rupture; tensile strength}
}