Prediction of Construction Cost for Field Canals Improvement Projects in Egypt
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
May-19-2019
- Country:
- Africa > Middle East
- Egypt (0.64)
- Asia > Middle East
- Palestine > Gaza Strip (0.14)
- Saudi Arabia > Eastern Province (0.13)
- Europe
- Jersey (0.13)
- United Kingdom > England (0.13)
- North America
- Canada
- United States
- California > Kern County (0.60)
- New Jersey (0.13)
- Ohio (0.13)
- Michigan (0.14)
- New York (0.14)
- Nebraska > Scotts Bluff County (0.60)
- Florida (0.13)
- Wyoming > Natrona County (0.60)
- Texas > Travis County
- Austin (0.13)
- Africa > Middle East
- Genre:
- Overview (1.00)
- Questionnaire & Opinion Survey (1.00)
- Research Report
- Experimental Study (1.00)
- New Finding (1.00)
- Workflow (0.88)
- Industry:
- Banking & Finance > Real Estate (0.67)
- Construction & Engineering (1.00)
- Energy > Oil & Gas (1.00)
- Government > Regional Government
- Transportation
- Ground > Road (1.00)
- Infrastructure & Services (1.00)
- Water & Waste Management > Water Management
- Lifecycle > Treatment (0.67)
- Water Supplies & Services (0.92)
- Technology:
- Information Technology > Artificial Intelligence
- Machine Learning
- Evolutionary Systems (1.00)
- Memory-Based Learning (1.00)
- Neural Networks (1.00)
- Statistical Learning > Regression (1.00)
- Representation & Reasoning
- Case-Based Reasoning (1.00)
- Expert Systems (1.00)
- Rule-Based Reasoning (1.00)
- Uncertainty > Fuzzy Logic (1.00)
- Machine Learning
- Information Technology > Artificial Intelligence