Artificial Intelligence for Project Management
Comparison of Artificial Intelligence algorithms for Project Conceptual Cost Prediction (First Edition)
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Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save freshwater. FCIPs are applied as a case study for Artificial Intelligence (AI) applications to projecting cost modeling. 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 prec...
Field canals improvement projects (FCIPs) are one of the ambitious projects constructed to save freshwater. FCIPs are applied as a case study for Artificial Intelligence (AI) applications to projecting cost modeling. 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 AI and machine learning methods such as quadratic regression, artificial neural networks (ANNs), fuzzy model and case-based reasoning, genetic algorithm (GA) and hybrid fuzzy systems. Sensitivity analysis is conducted to determine the contribution of selected key parameters. Finally, a simple friendly project data-input screen is created to facilitate usage and manipulation of the developed model.
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