İnşaat Ekibi Üretkenlik Tahmininde K-En Yakın Komşu ve Genelleştirilmiş Sinir Ağının Performansının Karşılaştırılması

Abstract

Construction crew productivity prediction is one of the most important issues that affect the realistic prediction of construction duration and cost. Use of different search algorithms like Feed Forward Neural Network, Ant Colony, Artificial Bee Colony, Particle Swarm Optimization, Radial Based Neural Networks and Self Organizing Maps for crew productivity prediction problem have been discussed in previous studies. However, the significant effect of the coherence between the nature of the data and the characteristics of the method used in prediction performance has generally been neglected. The aim of the current research thus has been to analyse the prediction performance of two contemporary learning algorithms; K-Nearest Neighbour (K-NN) and Generalized Neural Network (GRNN) when applied to three different crew (formwork, tiling and masonry) productivity related data sets with different distribution characteristics. Performance of both methods varied with the changing coefficient of variation values. K-NN outperformed GRNN for all data sets and both of the methods had their worst performance on the dataset with the highest variance.