A comparative study of machine learning regression models for predicting construction duration

Abstract

ABSTRACT Over the past few decades, the construction industry has been suffering from project delays. It remains a recognized challenge to accurately predict the actual project’s progress, despite the vast amount of field data and scheduling methods available. In this paper, four different machine learning (ML) models including K-nearest neighbor (KNN), support vector regression (SVR), gradient boosting trees (GBT), and artificial neural network (ANN) were used for forecasting the construction duration of work areas. Field data were collected, including progress, workload, labor, weather, planned days, and location, as inputs and construction days as output for each work area to develop the models. A simple and widely used baseline model was used to confirm the accuracy and generalization capability of ML models. The comparison of the results revealed that ANN and GBT models produced significantly superior results. Additionally, the GBT model is substantially more computationally efficient. In conclusion, the GBT model could be successfully and effectively applied to improve the prediction of construction duration. The proposed ML models could be utilized as a decision support tool for construction project managers to adjust construction site resource allocation.