Estimating water treatment plants costs using factor analysis and artificial neural networks

Abstract The cost of construction project is a fundamental input for decision making process set by owner during procurement stage. The paper identifies the cost drivers that are used in parametric cost estimation model for water treatment plants. Cost estimation at planning stage of projects is important for the success of the next stages in the projects. It is also very useful at the design stage of a project when information is incomplete and detailed designs are limited in such an early stage. Literature has been reviewed and interviews were conducted with experts and officials in water treatment plants to explore all variables that influence the construction cost of water treatment plants. A questionnaire survey was then conducted to assess the impact of the identified factors on construction costs of water treatment plants. Datasets that consist of 160 water treatment plant projects in Egypt were collected. Construction cost drivers have been nominated through two different procedures. The first technique is descriptive statistics ranking of variables by evaluating Mean Score and Relative Importance Index based on respondents' feedback in conducting questionnaire. The second technique utilizes exploratory factor analysis on the collected dataset. These cost drivers are used to construct two predictive models for estimating the construction cost of water treatment plants models using artificial neural networks. Analysis of results was performed to validate the models and demonstrate their effectiveness. The proposed methodology aids public authorities to perform comparative analysis and evaluate the different alternatives of water treatment plant projects.

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