Regression Modelling for Prediction of Construction Cost and Duration

Abstract:

Construction investments are sensitive to time and cost overruns. Delay and cost escalation are considered two threats to project success. The project objective is to develop a model to predict project cost and duration based on historical data of similar projects. Statistical regression models are developed using real data of building projects. The methodology is adopted in 3 steps: a) Data collection b) Statistical analysis using Statistical Package for Social Sciences (SPSS) software c) Interpretation of results. The real data of cost and duration of 51 building projects have been collected. In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modelling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. The analysis is done using SPSS developed by IBM Corporation. The Regression models have been developed using the data collected from Noel Builders, Kakkanad, Ernakulam to predict the project cost and duration. The developed models are validated using split sample approach. The model outputs can be used by project managers in the planning phase to validate the scheduled critical path time and project budget.

• Periodical: Applied Mechanics and Materials (Volume 857)
• Pages: 195-199
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.857.195
• Online since: November 2016
• Authors: Nivea Thomas*, Anu V. Thomas
• Keywords: Modelling, Relationship, Statistical Regression, Validation

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