Prediction of Construction Cost for Field Canals Improvement Projects in Egypt

TL;DR

This paper develops a machine learning-based conceptual cost model for field canal improvement projects in Egypt, identifying key cost drivers to enhance early-stage cost prediction accuracy.

Contribution

It introduces a novel combination of qualitative and quantitative approaches to select key cost drivers and applies various machine learning techniques for cost estimation.

Findings

Identified key cost drivers for FCIPs in Egypt

Developed a parametric cost model using regression, neural networks, fuzzy logic, and case-based reasoning

Enhanced early-stage cost prediction accuracy for FCIPs

Abstract

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.