Adaptive construction scheduling with AI in 4D/5D BIM: Integrating weather data and activity-based costing

Purpose – This study aims to examine how integrating artificial intelligence (AI), weather data, activity-based costing (ABC) and building information modelling (BIM) can improve construction project scheduling. It synthesises current evidence and specifies an AI-driven framework that couples weather-aware planning with activity-level costing and 4D/5D BIM visualisation. Design/methodology/approach – A structured review of peer-reviewed literature identifies integration gaps across the four domains. The study proposes an architecture and process model covering data ingestion, forecasting, cost attribution, schedule optimisation and BIM-based simulation and sets evaluation criteria for future empirical testing. Findings – The review shows that AI-enabled scheduling, weather-linked planning, ABC and 4D/5D BIM are often studied in isolation, with limited work on their combined use. The framework defines data interfaces between weather feeds, ABC cost pools and BIM task objects and outlines a learning pipeline for adaptive rescheduling and scenario analysis. Research limitations/implications – The work is conceptual and unvalidated on live projects. Future studies should implement the framework on case projects, share code and datasets where permissible and report robustness across project types and climates. Practical implications – Planners and contractors can use the framework to reduce weather-related disruption, improve activity-level cost transparency and communicate scenarios through 4D/5D models, supporting smart and sustainable delivery. Originality/value – The paper consolidates four fragmented streams into a single, auditable scheduling approach and sets a research agenda and reference architecture for digital construction.