Time Estimation of Construction Projects based on Engineering Economics and Machine Learning

The aim of this study is to apply time estimation approaches developed based on metrics such as earned schedule (ES), earned duration (ED), and effective earned schedule (ES(e)) within the framework of earned value management (EVM) project control methodology and to model the data obtained from these approaches using machine learning (ML) methods. Accordingly, a comparative analysis of different ML methods in terms of time estimation accuracy has been performed. A dataset is created using 18 different estimated time at completion (EAC(t)) time estimation methods based on data obtained from a residential construction project in Türkiye. Using this dataset, estimation models were developed through ML techniques. The performance of six ML models employing 18-time estimation approaches was evaluated using six performance criteria. The findings indicate that the GPR model achieved superior accuracy compared to the other methods, demonstrating the strong potential of ML-based approaches for time estimation in construction projects.