  Developing a reliable parametric cost model at the conceptual stage of the
project is crucial for projects 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 comparison analysis for AI models is required to guide
practitioners to the appropriate model. The study focuses on investigating
twenty artificial intelligence (AI) techniques which are conducted for cost
modeling such as fuzzy logic (FL) model, artificial neural networks (ANNs),
multiple regression analysis (MRA), case-based reasoning (CBR), hybrid models,
and ensemble methods such as scalable boosting trees (XGBoost). 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
showed that the most accurate and suitable method is XGBoost with 9.091% and
0.929 based on Mean Absolute Percentage Error (MAPE) and adjusted R2. Nonlinear
adaptability, handling missing values and outliers, model interpretation and
uncertainty have been discussed for the twenty developed AI models. Keywords:
Artificial intelligence, Machine learning, ensemble methods, XGBoost,
evolutionary fuzzy rules generation, Conceptual cost, and parametric cost
model.
