Research on AI-based carbon emission analysis and reduction strategies in construction projects

This study presents an AI-enhanced Lifecycle Analysis (LCA) framework designed to address the challenges of carbon emission analysis in the construction industry and explores its application and significance within the context of smart city development. By combining real-time data collection, deep learning-based emission prediction, and optimization algorithms, the framework provides a dynamic and precise approach to reducing lifecycle emissions. The system incorporates IoT sensors and Building Information Modeling (BIM) to collect real-time data on material usage, energy consumption, and on-site activities. Emission prediction is achieved through advanced machine learning models, which demonstrate high accuracy and adaptability to changing project conditions. Optimization algorithms are applied to identify low-carbon materials and improve energy efficiency during both construction and operational phases. A case study on a commercial building highlights the framework’s effectiveness, achieving a 92% alignment between predicted and observed emissions, reducing resource waste by 30%, and improving energy management. In the context of smart cities, the integration of this AI-LCA framework facilitates city-wide data connectivity, supports real-time emission monitoring across multiple projects, and enables urban managers to formulate holistic carbon reduction strategies. Compared to static LCA methods, the proposed framework adapts dynamically to real-time changes, enabling stakeholders to make data-driven decisions that minimize environmental impact. This research highlights that the integration of AI technologies into the construction sector not only provides a scalable and efficient solution for achieving sustainability goals but also serves as a critical foundation for the carbon management infrastructure of smart cities.