Prediction of Construction Cost for Field Canals Improvement Projects in Egypt
This work addresses cost prediction for water-saving infrastructure projects in Egypt, but it is incremental as it applies existing machine learning methods to a specific domain.
The study tackled the problem of predicting construction costs for field canals improvement projects in Egypt by developing a parametric cost model using machine learning methods, resulting in the identification of key cost drivers and the creation of a model for accurate preliminary cost estimation.
Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save fresh water. To finance this project, Conceptual cost models are important to accurately predict preliminary costs at the early stages of the project. The first step is to develop a conceptual cost model to identify key cost drivers affecting the project. Therefore, input variables selection remains an important part of model development, as the poor variables selection can decrease model precision. The study discovered the most important drivers of FCIPs based on a qualitative approach and a quantitative approach. Subsequently, the study has developed a parametric cost model based on machine learning methods such as regression methods, artificial neural networks, fuzzy model and case-based reasoning.