Ensemble learning framework for forecasting construction costs

Abstract

Construction cost forecasting is vital for tendering processes, enabling the evaluation of bidding offers to maximize revenues and avoid losses. In recent years, the automation of this forecasting process has gained attention due to the limitations of traditional approaches that rely on human experts, which can lead to subjective judgments. This paper introduces an ensemble learning decision-support framework that combines regression random forests and gradient-boosting regression trees through regression voting to automate cost estimation for residential and commercial projects. Evaluation of this approach using the dataset from San Francisco’s building inspection department in the United States demonstrated significant performance improvements over support vector regression. This paper highlights the importance of automating construction cost forecasting with artificial intelligence techniques for construction companies and is expected to encourage companies and building inspection departments worldwide to publish more datasets for the application of advanced deep learning models.