Efficient estimation and optimization of building costs using machine learning

Abstract

This study provides a fast and accurate Machine learning (ML) and optimization framework, which allows a quick estimate for building costs, hence improving operational efficiency and competitiveness of a construction company. A dataset composed of 10,000 parametric building configurations, collected from end-to-end real-world activities in our partner company, was used to train and validate the ML models to perform multiple tasks. Among the 13 ML regression algorithms used, the Artificial Neural Network (ANN), Gradient Boosting, and XGBoost models appear to be the most suitable to estimate the building costs and the required resources with an accuracy of 99% within less than a second of the training time. The ANN models are also developed to identify available options of the building features under a given budget. The optimization problem under constraints is solved, helping clients determine the optimal building costs according to their preferences. Besides, the optimized building costs obtained by this study are 7% smaller than those of the actual data, hence to improve the company’s competitiveness. This study showcases that ML models can be efficiently used in the construction sector to optimize the workflow for cost savings and provide some practical implications for data-driven management.

Keywords:

• Building cost
• maintenance cost
• machine learning
• regression analysis
• neural networks
• operational efficiency

Acknowledgement

The support of Thu Dau Mot University for this project is greatly appreciated.

Disclosure statement

No potential conflict of interest was reported by the author(s).