Construction labor productivity modeling with neural networks


Sonmez R., Rowings J.

JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT-ASCE, vol.124, no.6, pp.498-504, 1998 (Peer-Reviewed Journal) identifier identifier

  • Publication Type: Article / Article
  • Volume: 124 Issue: 6
  • Publication Date: 1998
  • Doi Number: 10.1061/(asce)0733-9364(1998)124:6(498)
  • Journal Name: JOURNAL OF CONSTRUCTION ENGINEERING AND MANAGEMENT-ASCE
  • Journal Indexes: Science Citation Index Expanded, Scopus
  • Page Numbers: pp.498-504

Abstract

Construction labor productivity is affected by several factors. Modeling of construction labor productivity could be challenging when effects of multiple factors are considered simultaneously. In this paper a methodology based on the regression and neural network modeling techniques is presented for quantitative evaluation of the impact of multiple factors on productivity. The methodology is applied to develop productivity models for concrete pouring, formwork, and concrete finishing tasks, using data compiled from eight building projects. The predictive behaviors of the models are compared with the previous productivity studies. Model results, advantages of the methodology, and study limitations are discussed.