A REGRESSION-BASED MODEL FOR PARAMETRIC COST ESTIMATION OF INDUSTRIAL STEEL STRUCTURES

Construction industry is considered one of the most versatile industries characterized by uncertainties and risk. Estimating the steel structure cost of industrial buildings is a challenging task compared with traditional buildings due to the uniqueness of this class of projects. This paper aims to introduce an effective and accurate parametric model for construction cost estimation of industrial steel structures. The paper proposes a regression-based model for estimating the cost of a critical construction component: the industrial steel structure where the is not enough historical data is available. The factors that affect the construction cost of industrial steel structures are initially identified based on the literature and interviews with local experts. The correlation between input factors and model’s output is then investigated. In addition, sensitivity analysis is performed to examine the relative importance of the regression model’s inputs. The model is validated using actual data on industrial steel structure costs in Saudi Arabia. The model adequately predicted the construction costs of actual projects with an accuracy of more than 88%. This indicates that the model is capable of accurately predicting the cost of such structures. The proposed model can be of great assistance to investors and decision-makers looking to invest in the industrial sector.