Comparison of Artificial Intelligence Techniques for Project Conceptual Cost Prediction: A Case Study and Comparative Analysis

Abstract

Developing a reliable parametric cost model at the conceptual stage of the project is crucial for project managers and decision makers. Existing methods, such as probabilistic and statistical algorithms have been developed for project cost prediction. However, these methods are unable to produce accurate results for conceptual cost prediction due to small and unstable data samples. Artificial intelligence (AI) and machine learning (ML) algorithms include numerous models and algorithms for supervised regression applications. Therefore, a comparative analysis for AI models is required to guide practitioners to the appropriate model. The article focuses on investigating 20 AI techniques which are conducted for conceptual cost modeling, such as fuzzy logic model, artificial neural networks, multiple regression analysis, case-based reasoning, hybrid models, such as genetic fuzzy model, and ensemble methods such as scalable boosting trees (XGBoost) and random forest. Field canals improvement projects (FCIPs) are used as an actual case study to analyze the performance of the applied ML models. Out of 20 AI techniques, the results show that the most accurate and suitable method is XGBoost with 9.091% and 0.929 based on mean absolute percentage error and adjusted R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively. Nonlinear adaptability, handling missing values and outliers, model interpretation, and uncertainty have been discussed for the 20 developed AI models. In addition, this study presents a publicly open dataset for FCIPs to be used for future models' validation and analysis.