MEHMET UĞRAŞ CUMA, HAYRULLAH ÖZEL, TAHSİN KÖROĞLU

Prediction of Ultimate Tensile Strength of Prestressed Concrete Strand Using Artificial Neural Network Model

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 constant. To understand the material properties of the product such as the yield and the ultimate tensile strength, some mechanical tests are carried out. The product, the time and the labor loss occured in these mechanical tests reveal the need to develop a prediction method based on non-destructive measurement. In this study, the mechanical properties of PC strand product is predicted by using artificial neural networks (ANN). 'Feed-Forward Backpropagation (FFBP)' has been preferred since it is the most accurate network type for the current process. To determine the ultimate tensile strength, the data such as the load applied to the material (loadcell output), 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 a real production line and are utilized as the input parameters of the ANN in the simulation environment. The study illustrates that the ANN model give a very good prediction of the ultimate tensile strength of PC strand.

Yapay Sinir Ağ Modeli Kullanılarak Ön Germeli Beton Demeti Maksimum Çekme Mukavemetinin Tahmini

Demir ve çelik endüstrisi, bir ülkenin endüstriyel ve ekonomik kalkınması için vazgeçilmez sektörlerden biridir. Demir ve çelik endüstrisindeki en yaygın sorun, ürünün maksimum çekme mukavemetini belirlemektir. Ön germeli beton demeti (ÖGBD) ürününde kullanılan hammaddeler kuvvet altında deforme olmakta ve karakteristikleri sabit olmadığından şekilleri ve boyutları değişmektedir. Ürünün, akma ve maksimum çekme mukavemeti gibi malzeme özelliklerini anlamak için bazı mekanik testler gerçekleştirilir. Bu mekanik testlerde ortaya çıkan ürün, zaman ve iş gücü kaybı, tahribatsız ölçümlere dayanan bir tahmin metodu geliştirme ihtiyacını ortaya koymaktadır. Bu çalışmada, ön germeli beton demeti ürününün mekanik özellikleri yapay sinir ağları (YSA) kullanılarak tahmin edilmiştir. Mevcut işlem için en doğru ağ tipi olduğundan 'İleri Beslemeli Geri Yayılım (İBGY)' tercih edilmiştir. Maksimum çekme mukavemetini belirlemek için, malzeme üzerine uygulanılan yük (yük hücresi çıkışı), indüksiyon fırınının DC gerilimi ve DC akımı, ÖGBD hattının hızı, indüksiyon fırınının sıcaklığı, soğutma tankının sıcaklığı ve ÖGBD ürününün çapı gibi veriler gerçek bir üretim hattından toplanmakta ve simülasyon ortamında YSA’nın girdi parametreleri olarak kullanılmaktadır. Çalışma, ANN modelinin, ön gerilmeli beton demetinin maksimum çekme mukavemetine dair çok iyi tahminde bulunulduğunu göstermektedir.

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