Research on the Application of Artificial Intelligence-based Cost Estimation and Cost Control Methods in Green Buildings

Abstract

In the research titled Comprehensive AI-Driven Cost Dynamics Model (AICD-CDM) for Sustainable Green Building Projects, we delve into the burgeoning field of artificial intelligence to revolutionize cost estimation and control in green building construction. This study introduces AICD-CDM, a novel framework that integrates several advanced machine learning techniques, including Linear Regression (LR), Artificial Neural Networks (ANN), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LGBoost), and Natural Gradient Boosting (NGBoost), to address the multifaceted challenges of cost prediction and management in sustainable building projects. By leveraging the distinct strengths of these methods, the AICD-CDM model offers a multi-dimensional approach to cost estimation, providing not only point predictions but also probabilistic forecasts to better manage uncertainties inherent in green building projects. The model’s capability to process complex, non-linear relationships between a multitude of cost-influencing factors makes it exceptionally adept at handling the intricate dynamics of sustainable construction. Furthermore, the integration of AI techniques ensures enhanced accuracy, adaptability, and computational efficiency, making the AICD-CDM an invaluable tool for decision-makers in the green building sector. This research not only contributes to the field of construction management by introducing a sophisticated cost control mechanism but also aligns with global sustainability goals by promoting efficient resource allocation and cost optimization in green buildings. The findings and methodologies of this study have the potential to set new benchmarks in the application of AI in sustainable construction management.