Productivity based method for forecasting cost & time of earthmoving operations using sampling GPS data

Research output: Contribution to journalArticlepeer-review

46 Scopus citations

Abstract

Accurate estimate of onsite productivity of earthmoving operations is essential for successful delivery of this class of projects. Estimating onsite productivity is a difficult task that requires tracking heavy and costly equipment and requires collecting, managing, and analyzing a considerable amount of data from construction sites on daily bases. Despite the fact that there are many systems available to carry out such process, those systems are expensive and require collection of large volume of data. This paper introduces a newly automated web based system for estimating actual productivity and for forecasting cost and time of earthmoving operations in near real time. The developed system integrates Global Positioning System (GPS) and Geographical Information System (GIS) in addition to four developed algorithms. The proposed system makes use of limited samples of GPS data for tracking and control purpose instead of collecting large volume of data from construction sites. The proposed system considers uncertainties associated with activity durations and cost. The results obtained from the application of the proposed system to two actual projects, indicates that the proposed system can be used as an effective tool for tracking and control of earthmoving operations.

Original languageEnglish
Pages (from-to)39-56
Number of pages18
JournalElectronic Journal of Information Technology in Construction
Volume21
StatePublished - Mar 2016

Bibliographical note

Publisher Copyright:
© 2016 The author.

Keywords

  • Cost
  • Forecasting
  • GPS
  • Productivity
  • Time
  • Uncertainties

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'Productivity based method for forecasting cost & time of earthmoving operations using sampling GPS data'. Together they form a unique fingerprint.

Cite this