Muhammed Yasin Durgun

Durability properties of concrete reinforced with steel-polypropylene hybrid fibers

Abstract In this study, some mechanical and durability properties of concrete specimens, with a water/cement (w/c) ratio of 0.47 and having a reference slump of 40 mm, produced by addition of steel fibers (SF) and/or polypropylene fibers (PF), were investigated. The additions of SF were 20 kg/m3 and 40 kg/m3 and PF additions were 0.3 kg/m3 and 0.6 kg/m3. Workability and unit weight tests were made on fresh concretes. Compressive stress, elastic modulus, and flexural tensile tests were performed on hardened concretes. Also, load-deflection curves and toughness indices were obtained during a three-point flexure tensile test. Freeze-thaw, abrasion and drying shrinkage tests were also carried out to determine the effects of SF and/or PF on some durability properties of concretes. It was observed that fiber inclusion to concrete decreases the workability and that the mechanical properties of concrete can be improved by the addition of fibers. Using hybrid fibers in the concrete both increases and decreases some mechanical properties of concrete. In order to see a greater effect of both SF and PF on the properties of concrete, different hybrid fiber volume fractions for different matrix strengths may be examined, since fiber type and fiber volume fractions are important parameters to be taken into account in the mix design of concrete for the performance

Modeling of Thermal Conductivity of Concrete with Vermiculite by Using Artificial Neural Networks Approaches

In this article, the thermal conductivity of concrete with vermiculite is determined and also predicted by using artificial neural networks approaches, namely the radial basis neural network and multi-layer perceptron. In these models, 20 datasets were used. For the training set, 12 datasets (60%) were randomly selected, and the residual datasets (8 datasets, 40%) were selected as the test set. The root mean square error, the mean absolute error, and determination coefficient statistics are used as evaluation criteria of the models, and the experimental results are compared with these models. It is found that the radial basis neural network model is superior to the other models.