Supervised vs. unsupervised learning for construction crew productivity prediction

ORAL M., Oral E. L., AYDIN A.

AUTOMATION IN CONSTRUCTION, cilt.22, ss.271-276, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 22
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1016/j.autcon.2011.09.002
  • Dergi Adı: AUTOMATION IN CONSTRUCTION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.271-276
  • Anahtar Kelimeler: Supervised learning, Unsupervised learning, Self Organizing Maps, Construction crew, Productivity, NEURAL-NETWORK MODEL, LABOR
  • Çukurova Üniversitesi Adresli: Evet

Özet

Complex variability is a significant problem in predicting construction crew productivity. Neural Networks using supervised learning methods like Feed Forward Back Propagation (FFBP) and General Regression Neural Networks (GRNN) have proved to be more advantageous than statistical methods like multiple regression, considering factors like the modelling ease and the prediction accuracy. Neural Networks using unsupervised learning like Self Organizing Maps (SOM) have additionally been introduced as methods that overcome some of the weaknesses of supervised learning methods through their clustering ability. The objective of this article is thus to compare the performances of FFBP, GRNN and SOM in predicting construction crew productivity. Related data has been collected from 117 plastering crews through a systematized time study and comparison of prediction performances of the three methods showed that SOM have a superior performance in predicting plastering crew productivity. (C) 2011 Elsevier B.V. All rights reserved.