Digital Twins for Integrated Energy and Structural Performance Assessment of Buildings: A Systematic Review and Research Gap Analysis

Although urban structures are essential to achieving sustainability, resilience, and public safety objectives, energy performance and structural integrity are still predominantly evaluated using discrete analytical frameworks. This fragmented approach limits holistic lifecycle decision making and reduces the effectiveness of retrofit strategies. Digital twin (DT) technology, supported by Internet of Things (IoT) sensing, Building Information Modeling (BIM), and data-driven analytics, offers a promising paradigm for integrated building performance assessment. However, despite the rapid growth of DT applications, there remains a lack of consolidated evidence on the joint implementation, validation, and operationalization of energy and structural assessment within unified DT frameworks. Unlike prior reviews that primarily focus on single-domain applications, this study presents a systematic review and research gap analysis of DT-based approaches for integrated energy and structural performance evaluation of buildings.

A systematic literature review was conducted following PRISMA guidelines. A total of 1,248 records were identified from Scopus, Web of Science, and IEEE Xplore using structured keyword combinations including “digital twin,” “BIM,” “energy performance,” “structural health monitoring,” and “building assessment” for the period 2015–2025. After removing duplicates (n = 312) and applying title–abstract screening (n = 936), 214 articles were assessed for full-text eligibility, resulting in 82 studies included in the final synthesis. The selected studies were analyzed using a combined thematic and quantitative synthesis framework covering DT architectures, BIM integration, energy simulation techniques, structural analysis approaches, sensor-data assimilation, and validation strategies.

The results indicate that, while 68% of studies focus on energy performance optimization and 54% on structural health monitoring, only 17% propose partially integrated frameworks, and fewer than 8% demonstrate fully interoperable co-simulation environments. Data exchange between energy and structural domains remains limited, with only 12% of studies enabling bidirectional coupling. Validation practices are largely scenario-based (approximately 70%), with minimal real-time or long-term field validation. Furthermore, only 9% of studies address district- or network-scale DT implementations. Key methodological challenges include BIM standardization, interoperability across simulation platforms, uncertainty quantification, lifecycle scalability, and computational efficiency.

This review advances beyond existing surveys by providing one of the first quantitatively grounded and integration-focused syntheses explicitly addressing the coupling of energy and structural domains within DT frameworks, alongside a structured evaluation of interoperability mechanisms and validation maturity levels. The findings highlight that integrated digital twin development remains an emerging research frontier. Future progress depends on AI-driven surrogate modeling, robust IoT-enabled real-time data synchronization, cloud-based co-simulation infrastructures, and multi-objective optimization frameworks. This study establishes a comprehensive evidence base and research agenda to support the development of scalable, lifecycle-aware, and resilient digital twins for sustainable and structurally robust built environments.