Yapay Sinir Aglari ile Eksenel Yüklü Kolonlarin Burkulma Analizi
Son yillarda, insaat mühendisligindeki bilgisayarli hesaplamalarda yapay zeka uygulamalari ilk sirayi almistir. Bu uygulamalar genellikle uzman sistemleri içermektedir. Bu makalede yapay sinir aglarina deginilmis ve bir uygulama yapilmistir. Eksenel yüklü kolonlar tasiyabilecekleri burkulma yükleri dikkate alinarak tasarimlanirlar. Bu çalismada çesitli mesnet kosullari iç in eksenel yüklü kolonlarin burkulma yükünü veren çok katmanli bir ag yapisi egitilmistir. Geriye yayilma egitim algoritmasi kullanilan çalismada dairesel, kare, dikdörtgen ve I kesitli kolonlar incelenmistir. Dört farkli mesnet durumu iç in egitilen ag, veriler karistirilarak dört farkli sinir kosulu için test edilmistir. Elde edilen sonuçlarin yeter duyarlilikta oldugu görülmüstür. Mantiksal programlama tekniginin bu alandaki uygulama potansiyeli vurgulanmistir.
The Buckling Analysis of Axially Loaded Columns with Artificial Neural Networks
Computation on Civil Engineering has concentrated primarily on artificial intelligence applications in the past few years. These applications generally involve expert systems. This article deals Neural Networks and applications were presented. Axially loaded columns are designed according to the their buckling load capacity. In this study, a multi-layer artificial neural network is trained to give critical load for axially loaded columns and various support conditions. Backpropagation training algorithms are used considering the circular, square, rectangular, and I cross-sections. The artificial neural network, with is trained for circular , rectangular ,square and I sections for four support conditions, is tested for the four support conditions. The results found using trained neural networks are sufficiently close to the theoretical solution. It is emphasized that logical programming has application potential in this area.
