2019/05/19 by Haytham H. Elmousalami, Elmousalami, Haytham H. · 1 citation
Computer Science · Decision Sciences · Engineering · Mathematics · #Artificial Intelligence (cs.AI) #BIM and Construction Integration #Construction Project Management and Performance #FOS: Computer and information sciences #Infrastructure Maintenance and Monitoring #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1905.11804
openalex publication_date 2019/05/19 · arxiv created 2019/05/20 · arxiv updated 2019/05/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.