AI-Driven Construction Management: Innovations, Challenges, and Pathways to Success

This study proposes a novel approach for optimizing construction project schedules using artificial intelligence (AI). The methodology involves collecting primary data from historical project records, real-time sensors, and stakeholder preferences. Through rigorous data analysis and preprocessing, insights are extracted to inform the development of an AI algorithm tailored to address the complexities of construction scheduling. The algorithm undergoes rigorous training and validation using historical data, ensuring robustness and accuracy. Upon implementation and integration into existing project management systems, the AI-based scheduling optimization tool generates optimized schedules considering multiple objectives, constraints, and uncertainties. The outputs include interactive visualizations and dashboards for stakeholder communication and scenario analysis. Performance evaluation against traditional scheduling approaches demonstrates the effectiveness and efficiency of the proposed method. Feedback from users guides iterative improvements, leading to a continuously refined AI model. Overall, this study showcases the potential of AI in enhancing construction management practices, leading to more efficient and successful project outcomes.

Keywords:

Construction management , Artificial intelligence , Project scheduling , Optimization , Stakeholder preferences , Data-driven decision-making.