Prediction of ultimate tensile strength of prestressed concrete strand using artificial neural networs

The iron and steel industry is one of the essential sector for the industrial and economic development of a country. The most common problem in iron and steel industry is to determine the ultimate tensile strength of the product. The raw materials that are used in the Prestressed Concrete (PC) Strand product are deformed under force and their shape and size are changed since the characteristics of them are not fixed. To understand the material properties of the product such as the yield and the ultimate tensile strength, some mechanical tests are carried out by the manufacturers. The literature survey clearly shows that the easiest and the most important mechanical test applied to the seven steel wire strand concrete (PC Strand) is the 'Tensile Test'. Even in the simplest experiment, the product, the time and the labor loss reveal the need of the non-destructive measurement. This thesis study is focused on the prediction of mechanical properties of PC strand product by using artificial neural networks (ANN). 'Feed-Forward Backpropagation (FFBP)' has been preferred since it is the most accurate network type for the current problem. The data such as the loadcell, the DC voltage and the DC current of the induction furnace, the speed of the PC strand line, the temperature of the induction furnace, the temperature of the quench tank and the diamater of the PC strand product are collected from production line. These data are utilized as the input parameters of the ANN in the simulation environment. Eventually, the ultimate tensile strength of the PC strand is determined at the output of the ANN.