Exploring the transition from BIM to Digital Twins in construction cost management: a systematic review and machine learning approach

Persistent cost overruns continue to constrain construction project success. Building Information Modelling (BIM) has introduced a digital approach to improve efficiency in cost management. Yet, its effectiveness is limited by interoperability issues, weak cost database integration, and lack of real-time lifecycle cost feedback. In response, Digital Twins (DTs) are gaining prominence for dynamic, data-driven predictive monitoring, adaptive budgeting, and lifecycle optimisation. However, the role of DTs in construction cost management, particularly during the BIM-DT transition, remains insufficiently understood. This study presents a systematic literature review of 208 journal articles from Scopus and Web of Science on BIM and DT applications in cost management. A hybrid analytical approach combining machine learning-based topic modelling, unsupervised clustering techniques, and qualitative synthesis was employed to identify thematic patterns and relationships in the literature. The review reveals five benefit clusters (accuracy and error reduction; efficiency and cost savings; integration and decision support; enhanced processes and quality; collaboration and transparency), two categories of challenges (technical interoperability limitations and organisational barriers), and five transition mechanisms linking BIM to DT cost management. The findings suggest that BIM-DT integration represents a staged socio-technical progression towards predictive, lifecycle-oriented cost optimisation, although institutional readiness and governance alignment remain implementation constraints.