Efficient estimation and optimization of building costs using machine learning

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 13ML 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 work-flow for cost savings and provide some practical implications for data-driven management.

Automated summary

This study provides a fast and accurate machine learning and optimization framework for estimating building costs and improving operational efficiency and competitiveness of a construction company. The trained models accurately estimate building costs and required resources, and can determine optimal building costs under a given budget. The study demonstrates the efficient use of machine learning models in the construction sector for cost optimization and data-driven management.