نوع مقاله : مقاله پژوهشی
1 کارشناسی ارشد مهندسی و مدیریت ساخت، موسسه آموزش عالی طبری بابل، بابل،ایران
2 استادیار و رئیس موسسه آموزش عالی طبری بابل
عنوان مقاله [English]
In this article, artificial neural network, as a time – and budget – saving tool, was applied for predicting the compressive, tensile and flexural strength of Self-Compacting Concrete (SSC). Feed Forward ANN models were designed using the available test data of different concrete mix-designs of SCC were obtained from different sources. In order to show the effect of the selected input parameters on the amounts of the test error in the prediction of the desired properties, data used in ANN models were arranged in two formats of 8 and 140 input parameters. According to the results, the optimized Feed Forward networks are able to predict the properties of SCC with suitable accuracy. In addition, for all of the desired properties, the accuracy of the networks with 140 inputs is higher than networks with 8 inputs. In fact, the effective data that are selected as inputs in the ANNs can significantly increase the prediction precision by more simulation the prediction conditions to the experimental conditions.