استخدام الشبكات العصبية الاصطناعية في تخمين انتاجية تغليف واجهات المباني بالحجر

Abstract

The main objective of this research is to introduce a new and alternative approach of using a neural network for productivity estimation of the finishing works for building project.The application of Artificial Neural Networks, as a modern technique, in Iraqi construction industry is necessary to ensure successful management, and many of the construction companies feel the need of such system in project managementMulti-layer perceptron trainings using the back-propagation algorithm were used. In this work, the feasibility of ANNs technique for modeling these productivity parameters was investigated. A number of issues in relation to ANNs construction such as the effect of ANNs geometry and internal parameters on the performance of ANNs models were investigated. Information on the relative importance of the factors affecting the above productivity parameters predictions were presented and practical equations for the predictions of the above cost were developed.One model was built for the prediction the total productivity of building project. It was found that ANNs have the ability to predict the Total productivity for finishing works for building project with a good degree of accuracy of the coefficient of correlation (R) was 93.2%, and average accuracy percentage of 96.4%.The ANNs model developed to study the impact of the internal network parameters on model performance indicated that ANNs performance was relatively insensitive to the number of hidden layer nodes, momentum term, and learning rate.