This paper is an extension of the work published in year 2010 in which compressive strength of plain concrete confined with Ferrocement was estimated using mathematical models and compared with 55 experimental results. In this paper, predictive model of compressive strength for plain concrete confined with Ferrocement has been developed by using MATLAB Artificial Neural Network (ANN) simulation. Out of 55, 19 experimental results are selected for training of multilayer feed forward neural network. Comparative analysis of the results showed that compressive strength estimated by ANN predictive model are very close to the experimental results than existing theoretical models.
Elevated temperatures, specifically in the event of fire, are likely to cause extreme deterioration in fibre-reinforced polymer (FRP) strengthened reinforced concrete (RC) structures. Various types of high-performance cementitious composites (HPCC) have been explored for the protection of RC structural members against elevated temperature, but there is inadequate information in this regard for ultra-high performance fibre-reinforced cementitious composites (UHPFRCC) containing high-alumina cement (HAC) and ground granulated blast furnace slag (GGBS) in conjunction with hybrid fibres – a prospective fire-resistant UHPFRCC for structural members. In this study, the change in mechanical strength of UHPFRCC was examined before and after heat treatment, followed by thermal and microstructural analysis. Besides the control sample, three other samples containing up to 1·5% of basalt fibres, and 1 kg/m3 of polypropylene fibres, were prepared and tested. Another mix was also prepared with only 1 kg/m3 of polypropylene fibres. Each sample was heated to 400, 700 and 1000°C. Results showed that the use of hybrid fibres significantly improved the room temperature mechanical strengths of UHPFRCC, which were found to be 80 MPa and 14·3 MPa, respectively. However, the optimum residual compressive and flexural strength was attained by UHPFRCC with only PP fibres and hybrid fibres, respectively.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.