A study of preproject planning and project success using ANNs and regression models

Yu Ren Wang, Edd Gibson

Research output: Contribution to journalArticle

40 Citations (Scopus)

Abstract

It has long been recognized by the industry practitioners that how well preproject planning is conducted has a great impact on project outcome. Through industry project data collection and model analysis, this research intends to investigate the relationship between preproject planning and project success. Preproject planning and project performance information from 62 industrial projects and 78 building projects, representing approximately $5 billion U.S.D. in total construction cost, is collected and used for this research analysis. Based on the information obtained, preproject planning is identified as having direct impact on the project success (cost and schedule performance). Two techniques were then used to develop models for predicting cost and schedule performance: statistical regression analysis, and artificial neural networks (ANNs). The research results provide a valuable source of information that supports better planning in the early stage of the project life cycle and have positive impact on the final project outcome.

Original languageEnglish (US)
Pages (from-to)341-346
Number of pages6
JournalAutomation in Construction
Volume19
Issue number3
DOIs
StatePublished - May 2010

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Neural networks
Planning
Costs
Regression analysis
Life cycle
Industry

Keywords

  • ANN model
  • Preproject planning
  • Project success
  • Regression model

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Civil and Structural Engineering
  • Building and Construction

Cite this

A study of preproject planning and project success using ANNs and regression models. / Wang, Yu Ren; Gibson, Edd.

In: Automation in Construction, Vol. 19, No. 3, 05.2010, p. 341-346.

Research output: Contribution to journalArticle

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