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A Forecasting Model for Labor Resources in Construction Projects Using Machine Learning

  • Author / Creator
    Mohammadhosseinzadeh Golabchi, Hamidreza
  • Considering the high rates of labor resources in construction projects clearly indicates the importance of appropriate labor resource management methods. Accurate labor resource allocation is a substantial step towards successful labor resource management. With the recent developments in the area of artificial intelligence and machine learning, these technologies can potentially be adopted to develop prediction models. This research aims to combine the benefits of artificial intelligence and historical data of previous projects to identify the significant factors affecting the labor resource requirements and to develop an efficient predictive model to analyze and learn from the past construction projects in order to have a precise estimate of required labor hours for upcoming projects.
    The research involves collecting and analyzing historical data, investigating current industry practices in labor resource estimation, and implementing machine learning algorithms to predict required labor hours for various resources in construction projects. Also, this study explores the key factors impacting the needed labor resources by combining the literature review and industry practices. Furthermore, this thesis offers a neural network model which can forecast the required labor hours for a construction work package by utilizing the historical data. The proposed model provides the estimation of the required labor hours for each day of the work package. The developed model aids project managers in labor resource allocation in construction projects and provides them a precise insight to perform a decent labor resource management.

  • Subjects / Keywords
  • Graduation date
    Fall 2021
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/r3-vsnv-sv06
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.