Digital twin maturity in the built environment: A systematic review of 112 implementation cases

Digital Twin (DT) technology has attracted growing interest in the built environment, yet reported implementations vary widely in maturity. This variation has not been benchmarked across application domains, making it difficult to identify which DT capabilities are well developed and which constrain progression toward higher levels of intelligence and autonomy. This study adapts an established five-dimensional DT decomposition into a domain-specific maturity framework and applies it to 112 published case studies across six application domains. The findings indicate that most implementations remain at an early stage of maturity. They collect real-time data from physical assets but do not yet send control actions back to them or operate autonomously. Digital modelling and the speed of physical-to-digital data exchange are the strongest capabilities, while the control of physical assets and the integration of multiple stakeholder organisations are the weakest. The coverage of data across lifecycle phases and the capacity to learn and adapt during operation are similarly underdeveloped across all domains. Four barriers account for this pattern, namely a gap between strong sensing and weak control, organisational fragmentation despite strong technical connectivity, narrow lifecycle data coverage, and limited adoption of adaptive learning. Research directions are suggested for each barrier. This review provides a quantitative baseline for diagnosing capability gaps and prioritising investment in DT development across the built environment sector.