Machine Learning Model for Construction Time Prediction: A Case of Selected Public Building Projects in Hosanna, Ethiopia.

The duration of a construction project is a vital factor to consider before the commencement of the new project. Nowadays, the common problem in the construction industry is time overrun. The main reason for this is the poor prediction of construction contract durations. Therefore, the objective of this study is to evaluate and validate Bromilow's time‐cost model and Love et al.'s time‐floor model to estimate early project durations for public building construction projects in the Hadiya Zone. The study also suggested an alternative duration machine learning prediction model by considering possibly influential project influencing factors. A questionnaire survey is designed to collect data, and subsequently, the study was performed using the Python programming language for development and validation purposes with different libraries used. The study developed Bromilow's time‐cost model using a simple linear regression algorithm and Love et al.'s time‐floor model using a multiple linear regression algorithm and proposed a parametric model using random forest, XGBoost, decision tree, K‐nearest neighbor, and polynomial regression algorithms. This study extends the body of knowledge related to construction time performance, and it contributes valuable insights that inform the implementation of machine learning model for construction time prediction. [ABSTRACT FROM AUTHOR]