Raman Bedi

Mechanical Properties of Polymer Concrete

Polymer concrete was introduced in the late 1950s and became well known in the 1970s for its use in repair, thin overlays and floors, and precast components. Because of its properties like high compressive strength, fast curing, high specific strength, and resistance to chemical attacks polymer concrete has found application in very specialized domains. Simultaneously these materials have been used in machine construction also where the vibration damping property of polymer concrete has been exploited. This review deals with the efforts of various researchers in selection of ingredients, processing parameters, curing conditions, and their effects on the mechanical properties of the resulting material.

Flexural Fatigue-Life Assessment and Strength Prediction of Glass Fibre Reinforced Polymer Concrete Composites

The paper presents the results of an investigation conducted to assess the fatigue-life and prediction of flexural fatigue strength of polymer concrete composites based on epoxy resin as binder material. Three point flexural fatigue tests were conducted on polymer concrete specimens using MTS servo controlled actuator, to obtain the fatigue lives of the composites at different stress levels. One hundred and thirty-seven specimens of size  mm were tested in flexural fatigue. Forty-three static flexural tests were also conducted to facilitate fatigue testing. It has been observed that the probabilistic distribution of fatigue-life of polymer concrete composite (PCC) and glass fibre reinforced polymer concrete composite (GFRPCC), at a particular stress level, approximately follows the two-parameter Weibull distribution, with statistical corelation coefficient values exceeding 0.90. The fatigue strength prediction model, representing S-N relationship, has been examined and the material coefficients have been obtained for GFRPCC containing 0.5% and 1.0% glass fibres. Design fatigue lives for GFRPCC containing different contents of glass fibres have been estimated for acceptable probabilities of failure and compared with those of PCC.

Adaptive neuro-fuzzy inference system in modelling damping performance of epoxy polymer concrete

In this study the damping property of the epoxy polymer concrete is analysed in relation to its composition, i.e., percentage of epoxy and percentage of filler. Foundry sand having a mean particle size in 300 to 450 µm range is used as aggregate in polymer concrete. Twelve different compositions of polymer concrete are evaluated in this study considering the amount of epoxy resin and filler as variables. The testing of prepared samples is performed using dynamic mechanical analyser (DMA) technique. Damping of the prepared specimens has been evaluated at 10, 30 and 50 Hz. It is observed that resin percentage is most important factor affecting damping of epoxy polymer concrete. Adaptive neuro-fuzzy inference system (ANFIS) has been successfully used for modelling of damping behaviour of the epoxy polymer concrete. For all cases studied, an optimum selection of the training set for reliable modelling and elimination of the experimental cost was found to be between 80% and 70% of the available experimental data.

Mechanical Characterization of Epoxy Polymer Concrete Containing Fly Ash

Polymer concrete is a composite material made of aggregates and polymeric resin as a binder. The applications of polymer concrete is limited not only to the domain of civil engineering but it has got inroads in to structural applications in Machine tool structures. Till date no standard mix design procedures are available for polymer concrete systems. In this research, Polymer concrete samples were made using epoxy resin as a binder, fly ash as a filler and marble waste as aggregate. The various mix proportions were developed using concept of mixture design of experiments. Compressive and flexural strength was evaluated for each composition of polymer concrete thus obtained. The effect of variables such as epoxy resin, fly ash and aggregates were analyzed. An optimum mix composition has been developed using the regression equations. The coefficient of correlation (R-Square) between the experimental value and predicted value was found to be high near about one proving the fitness of the model.