Mustafa Altin

Modelling and Experimental Study of Mechanical Behaviour of Walls Produced by Different Knitting

Abstract This study aims to investigate the shear behavior and resistance of brick walls produced by various knitting types. For this purpose, 15 samples were produced using Holland knitting, block knitting, cross knitting, lock knitting, and plain knitting. The samples were forced in experiments to collapse and in the non-linear region, the changes in the samples related with shear forces and shear rigidity of the wall sections were determined. In the performed experimental study, the standard sliding resistance test technique for the masonry wall samples has been determined as recommended in ASTM Standard 1391-81. The numerical modeling was carried out by the finite elements method with the program package Ansys, taking into account the local mechanical parameters of bricks and mortar. The behavior of the experimental samples and the cracks occurred during the experiments were analyzed and shear stress graphs and load-displacement graphs were derived. The modeling with the Ansys program was compared with the experimental results and the most appropriate wall knitting type could be proposed.

Artificial neural network design for behaviours of reinforced concrete column under axial load and comparison of experimental study

Construction (building) sector is one of the sectors which adapt the rapid development of the technology. Especially in frame (skeleton) systems, very serious studies on columns that are vertical conveyors (carriers) are being done. Generally, these studies are implemented by experimentally and computer software is used in these studies as they offer good results. Artificial neural network (ANN) has been used in several engineering application areas including civil engineering. The use of ANN to predict the behaviour of reinforced concrete (R/C) members, using the vast amount of experimental data as a test-bed for learning and verification of results, proved to be available method for carrying out parametric studies. In the present study, columns under the axial load were manufactured from concrete that has been produced without carrying out the standards. Load conveying capacities and tension-unit deformation relationships of columns manufactured with different geometrical and equipment (outfit) properties and same cross-section area have been investigated. At the same time, research has been done via modelling by using ANN that is one of techniques of artificial intelligence and gained importance in recent years. SPSS statistical packet program is used to evaluate the results of this research. After comparisons of results obtained in the experiments, it has been determined that square column samples have the most axial load conveying capacity. It has also been determined that number of displacement is less in samples of columns wrapped (winded) with fret comparing to samples of columns wrapped with stirrup. The same results are obtained after modelling by ANN. As the result of statistical analyses that have been done in %5 reliability interval between data obtained from experiments and ANN, it has been seen that ANN can be used as reliable method.

Charring rate of timbers in terms of size, layer material and fire class evaluated by the Taguchi method

Abstract This publication is focused on the fire performance of different wood materials to determine the charring rate. For this purpose, L27(313) Taguchi orthogonal design was used for the derivation of the parameter combinations. A total of 108 timber specimens made of 3 different natural timber types having 3 different sizes had been coated with fire retardant varnish and boron impregnation. Specimens were tested for three different fire classes and temperatures. The values of charring rate were obtained from fire tests according to the orthogonal design. Obtained results were analyzed using the signal-to-noise ratio, analysis of variance methods and 3D surface plots. It was concluded that the type of timber and specimen section are the most significant factors affecting the charring rate. The second significant factor affecting the charring rate was found to be the duration and temperature of fire. Lastly, coating material is the third important factor influencing the charring rate.

Determination of the resistance characteristics of self-compacting concrete samples by artificial neural network

In this study, in order to determine the resistance characteristics of self-compacting concrete (SCC) samples, experiments were done in the Konya Cement Factory, Ready-mix Concrete Establishment. Four different mixture proportions were chosen in the experimental study. 24 samples of the 4 mixtures were selected in order to set the cube compression strength. For each mixture, these 24 samples were broken down within 28 days and the characteristics of cube compression strength were obtained. After 28 days, compression strength average was found to be 50.0300 MPa. A model of Artificial Neural Network (ANN) was designed for this study and the results were obtained in this model of ANN. Both experimental and ANN data was analyzed with SPSS statistical packet software. The result of statistical analysis (p=0.9972) has been done in 95% of confidence interval. It has been seen that the ANN can be used as reliable modelling method for similar studies.