Modelling construction labour productivity using evolutionary polynomial regression

Sasan Golnaraghi, Osama Moselhi, Sabah Alkass, Zahra Zangenehmadar

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Construction projects are labour-intensive and labour costs are a substantial percentage of total budget. Impaired labour productivity causes an increase in construction project schedule. Labour productivity is one of the most frequently discussed topics in the construction industry, and modelling labour productivity by utilising different techniques has been getting more attention. It is a challenging task as it requires identifying the influencing factors as well as considering the associated interdependencies. This paper investigates the application of evolutionary polynomial regression (EPR) for modelling labour productivity in formwork installation. EPR is a data-driven hybrid modelling technique based on evolutionary computing and has been successfully applied to solving civil engineering problems. Results obtained from the EPR model were compared with the outcomes of three other methods: best subset, stepwise, and general regression neural network (GRNN). Results demonstrate the predictive superiority of the developed EPR model for nonlinear problems based on statistical performance indicators.

Original languageEnglish
Pages (from-to)207-226
Number of pages20
JournalInternational Journal of Productivity and Quality Management
Issue number2
Publication statusPublished - 2020
Externally publishedYes


  • Construction industry
  • EPR
  • Evolutionary polynomial regression
  • Labour productivity
  • Loss of productivity
  • Modelling
  • Regression

ASJC Scopus subject areas

  • General Business,Management and Accounting


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