The architectural and construction industries are currently undergoing a fundamental digital transformation, reshaping how buildings are conceived, constructed, and managed. A central force behind this transformation is digital twin technology – a sophisticated concept that extends the capabilities of traditional 3D modeling and Building Information Modeling (BIM) (Bolton et al., 2018). While tools such as AutoCAD and Revit brought digital workflows into design and coordination, digital twins introduce an unprecedented layer of intelligence and interactivity by creating live, data-rich virtual replicas of physical assets (Sacks et al., 2020).
Initially popularized in aerospace and industrial manufacturing, the digital twin paradigm has since evolved to address the complexities of the built environment. Unlike static BIM models that capture a snapshot in time, digital twins are dynamic – constantly updated using real-time data from IoT sensors, systems, and external inputs (Glaessgen & Stargel, 2012). This live synchronization enables continuous monitoring and analysis throughout a building’s lifecycle – from early concept and design to operations and even end-of-life decommissioning.
The adoption of digital twins is especially timely given global concerns about sustainability, resilience, and building performance. By integrating performance simulations, occupancy data, and predictive analytics, architects and engineers can use digital twins to optimize building design, pre-empt maintenance needs, and improve user comfort (Fuller et al., 2020). In essence, this technology moves the discipline from “designing objects” to “designing responsive systems”.
When used with BIM platforms like Autodesk Revit and AutoCAD, particularly in combination with point cloud data from 3D scanning, digital twins become even more powerful. These integrations allow for the capture and real-time management of detailed as-built conditions, supporting applications in renovation, facilities management, and even emergency response (Volk et al., 2014).
As urban development becomes more complex, and the demand for efficient, adaptive buildings grows, digital twin technology is emerging as an indispensable tool for the next generation of architects and urban designers (Shahat et al., 2021).
This article’s novelty lies in synthesizing current academic and industry knowledge into a unified analytical framework (Fig. 1) that links digital twin applications across the architectural design, construction, and facility management lifecycle. The authors’ contribution focuses on defining this framework and identifying a workflow model based on practical case studies from Dawood Engineering and ArchiTube, demonstrating how digital twins can enhance smart building performance.
At its foundation, digital twin technology is about creating a bridge between physical and digital environments. A digital twin is not just a 3D model; it is a real-time digital representation of a physical entity – synchronized using live data from embedded systems, sensors, and external sources (Tao et al., 2019). This enables the virtual model to reflect the physical asset’s current state, behavior, and performance, and even predict future conditions.
Digital Twin Technology Framework for Smart Buildings (own research)
In the context of buildings, a digital twin combines both geometric information (from BIM) and semantic data (functional, environmental, usage, etc.). This comprehensive view allows stakeholders to analyze not only what a building looks like but also how it is functioning. Whether it’s temperature, lighting, structural integrity, or user activity, these attributes are updated and visualized in real time (Sacks et al., 2018).
Key enabling technologies for digital twins include:
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IoT Devices: These collect data on temperature, humidity, air quality, occupancy, and system performance (Fuller et al., 2020).
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Cloud Computing: Facilitates data storage and remote access to high-fidelity models (Lu et al., 2020).
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Artificial Intelligence and Machine Learning: Provide predictive insights, fault detection, and optimization strategies (Tao et al., 2019).
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BIM and 3D Modeling Tools: Such as Revit and AutoCAD, which serve as the visual and data backbone for the digital twin (Volk et al., 2014).
One of the critical distinctions between a BIM model and a digital twin is the feedback loop. BIM models are generally created during the design and construction phases and often become static over time. In contrast, a digital twin is continuously updated, evolving with the building. This makes it a “living model” – one that adapts based on sensor inputs and operational changes (Glaessgen & Stargel, 2012).
For example, a digital twin of a commercial building may integrate HVAC data, occupancy metrics, and weather inputs. Using this information, it can simulate different energy scenarios, detect inefficiencies, and suggest optimal schedules for heating and cooling. Over time, it can help reduce energy costs, improve occupant comfort, and prolong system lifespans (Shahat et al., 2021).
Beyond individual buildings, digital twins are now being scaled to represent entire campuses, infrastructure systems, or urban districts, forming a foundation for smart city planning and responsive governance (Bolton et al., 2018).
Thus, the digital twin is not just a model – it’s a dynamic decision-making platform that enhances the way we design, construct, and manage our built environment. The conceptual structure of these interlinked components is illustrated in Figure 1, which presents the proposed Digital Twin Technology Framework for Smart Buildings.
Digital twin technology is profoundly transforming architectural design by providing a dynamic and interactive digital replica of a building that evolves alongside its physical counterpart (Fuller et al., 2020). Unlike traditional static CAD or BIM models, digital twins continuously incorporate real-time data from sensors, environmental inputs, and user interactions, offering architects deeper insights into how design decisions impact building performance throughout the lifecycle (Tao et al., 2019).
This continuous data integration allows architects to simulate multiple design scenarios and predict how different materials, spatial arrangements, and system configurations will behave under varying real-world conditions like lighting, temperature, and occupancy patterns (Lu et al., 2020). For example, designers can use digital twins to analyze daylight penetration or thermal comfort dynamically, informing sustainable design strategies and enhancing occupant wellbeing (Bolton et al., 2018).
Furthermore, digital twins enable enhanced collaboration by creating a shared, interactive environment where multidisciplinary teams – including structural engineers, MEP consultants, and clients can visualize and provide feedback on evolving designs in real time (Sacks et al., 2020a). This reduces costly redesigns and errors that typically occur when translating 2D or static 3D models into physical structures (Fig. 2).
Digital Twin Technology service for MEP facility (ArchiTube Sp. z o.o. /Dawood Engineering, https://dawood.net/service/digital-twins/)
In renovation and retrofit projects, digital twins generated from point cloud data captured via LiDAR scanning are integrated into platforms like Autodesk Revit, allowing architects to create highly accurate as-built BIM models (Volk et al., 2014).
This facilitates precise planning that respects existing conditions and optimizes the integration of new design elements with old structures.
In summary, digital twins enhance architectural design by enabling data-driven, performance-focused decision making, supporting multidisciplinary collaboration, and allowing accurate modeling of existing buildings for renovation (Fuller et al., 2020; Tao et al., 2019; Volk et al., 2014).
Digital twins and BIM (Building Information Modeling) are intrinsically linked technologies, with BIM providing the foundational 3D and semantic model, and digital twins layering real-time data streams on top to create a dynamic representation (Sacks et al., 2020b). BIM captures comprehensive geometric, spatial, and material properties during the design and construction phases, while digital twins extend this data by incorporating sensor-driven operational information throughout the building’s lifecycle (Glaessgen & Stargel, 2012).
This integration enables continuous performance monitoring, simulations, and predictive maintenance capabilities that traditional BIM workflows alone cannot achieve (Lu et al., 2020). For instance, BIM models created in software like Autodesk Revit or AutoCAD serve as the static framework to which real-time sensor data – temperature, occupancy, energy usage – is linked, producing a living digital twin (Fig. 3).
Integration with BIM model provided by Revit & Tandem (ArchiTube Sp. z o.o./Dawood Engineering, https://dawood.net/service/digital-twins/)
The synergy between BIM and digital twins also streamlines facility management by transforming BIM from a design and construction tool into a full lifecycle asset management system (Tao et al., 2018). Facility managers can track system health, monitor energy consumption patterns, and schedule predictive maintenance activities based on insights generated by the twin, thus reducing downtime and operational costs (Lu et al., 2020).
Moreover, integrating point cloud data (Fig. 4) from laser scanning into BIM environments enhances the accuracy of digital twins, especially for existing buildings where as-built conditions must be precisely documented (Volk et al., 2014).
Comparable integrations between BIM and digital twin systems are reported by Sacks et al. (2020b) and Khajavi et al. (2019), aligning with the industrial applications demonstrated by ArchiTube.
3D laser scanning creates point cloud results (ArchiTube Sp. z o. o. /Dawood Engineering, https://dawood.net/service/digital-twins/)
Overall, the fusion of BIM and digital twin technologies enables smarter buildings by combining static models with live data feeds, improving design validation, operational efficiency, and lifecycle management (Fig. 5).
Chart: Comparative Matrix – BIM vs. Digital Twin (own research)
Digital twin technology has emerged as a transformative force in the construction and operation phases of architectural projects. A digital twin is a dynamic, digital replica of a physical building or system that allows real-time monitoring, analysis, and optimization. In construction, it provides a shared digital space for architects, engineers, and contractors to collaborate effectively. This virtual model can simulate construction sequences, detect clashes, and improve coordination across disciplines, reducing errors and delays on site. For example, when combined with Building Information Modeling (BIM), a digital twin offers predictive insights that allow project managers to foresee potential issues before they manifest physically, thus enhancing construction quality and efficiency (Boje et al., 2020).
Beyond the construction phase, digital twins play a crucial role in building operations and facility management. Once a building is occupied, the Digital Twin continues to function as a real-time data hub, integrating inputs from Internet of Things (IoT) sensors embedded in the building. This enables facility managers to track energy usage, monitor structural integrity, and schedule maintenance proactively.
The ongoing synchronization between the physical and digital models allows building systems to be fine-tuned for optimal performance and sustainability. For instance, Siemens’ implementation of digital twins in smart buildings has shown how operational data can be used to reduce energy consumption and improve occupant comfort (Fuller et al., 2020).
Furthermore, digital twins support lifecycle asset management by offering an accurate historical record of a building’s performance and modifications. This capability is particularly beneficial for complex infrastructure projects such as hospitals or airports, where operational efficiency and safety are paramount. By maintaining a live model that reflects the current state of a facility, stakeholders can make better-informed decisions regarding renovations, expansions, or system upgrades (Fig. 6). The use of digital twins in managing Heathrow Airport, for example, demonstrates the power of this technology to streamline operations and respond rapidly to system disruptions (Sacks et al., 2020b).
Application of digital twins in construction project lifecycles (Zhou, 2024)
In conclusion, the integration of digital twin technology in the construction and operational stages of architecture is redefining traditional workflows. It not only enhances collaboration and reduces construction risk but also ensures long-term building efficiency through real-time data-driven management. As the technology matures, its role in fostering smart, adaptive, and sustainable building environments will continue to grow (Fig. 7).
Essential components to create a digital twin of building and difference with BIM (Khajavi et al., 2019)
The adoption of digital twin technology in architecture is revolutionizing facility management (FM) by enabling real-time monitoring, predictive maintenance, and data-driven decision-making. A digital twin – a virtual replica of a physical building – integrates IoT sensors, BIM (Building Information Modeling), and AI to simulate building performance and optimize operations. This technology allows facility managers to track equipment health, energy consumption, and space utilization, leading to cost savings and improved efficiency. By continuously analyzing data, digital twins help transition FM from reactive troubleshooting to proactive maintenance (Lu et al., 2020).
One of the most significant benefits of digital twins in facility management is predictive maintenance. Traditional maintenance relies on scheduled check-ups or breakdown repairs, which can be costly and disruptive. However, digital twins use real-time sensor data to predict equipment failures before they occur. For example, digital twins in commercial buildings reduced HVAC system downtime by 40 % by identifying inefficiencies early. This approach not only extends the lifespan of building systems but also minimizes unexpected repair costs, making facility management more sustainable and cost-effective (Bolton et al., 2018).
Beyond maintenance, digital twins play a crucial role in energy performance optimization. Buildings account for nearly 40 % of global energy consumption, and digital twins help reduce this footprint by simulating energy use patterns. Using AI-driven analytics, facility managers can test different energy-saving strategies – such as adjusting lighting, optimizing HVAC schedules, or integrating renewable energy sources – before implementing them in the real world. Buildings using digital twins achieved up to 30 % energy savings through dynamic adjustments based on occupancy and weather conditions. This makes digital twins a powerful tool for achieving net-zero energy buildings and complying with sustainability regulations (Deng et al., 2021).
Another key advantage of digital twins is their ability to enhance occupant comfort and space utilization. By analyzing data from motion sensors, air quality monitors, and thermal cameras, digital twins optimize indoor environments for productivity and well-being. For instance, The Edge in Amsterdam, one of the world’s smartest buildings, uses a digital twin to adjust lighting, temperature, and desk allocations in real time based on occupancy. This level of responsiveness ensures that buildings are not only energy-efficient but also human-centric, improving user satisfaction and operational efficiency (Khan et al., 2022).
In conclusion, digital twin technology is transforming facility management and performance optimization in architecture by enabling predictive maintenance, energy efficiency, and occupant-centric design. As IoT and AI technologies advance, digital twins will become essential for managing smarter, more sustainable buildings. The future of facility management lies in leveraging real-time data and simulations to create resilient, high-performance buildings that adapt to both environmental and human needs (Fig. 8).
Similar facility management applications have been discussed in peer-reviewed studies (Boje et al., 2020; Deng et al., 2021), confirming the operational benefits observed in industrial implementations such as those shown by Intellias.
Digital twins in facility management (https://intellias.com/digital-twins-in-facility-management-the-clear-path-forward-for-intelligent-buildings/)
As the architectural, engineering, and construction (AEC) industries continue to embrace innovation, digital twin technology is positioned to become a cornerstone of future building design and management. One major trend on the horizon is the integration of artificial intelligence (AI) and machine learning with digital twins. These technologies can analyze vast data sets from sensors embedded in smart buildings, predicting maintenance needs, optimizing energy use, and even simulating occupant behavior in real-time. This intelligent layer transforms digital twins from static models into adaptive systems, enabling buildings to “learn” and evolve with their environments (Lu et al., 2020).
Another emerging trend is the use of blockchain technology to secure the vast amounts of data generated and replied upon by digital twins. With building models becoming more interconnected and reliant on real-time updates from numerous stakeholders, the integrity and security of data becomes paramount. Blockchain offers a transparent, tamper-proof system for storing and transferring digital information, which can help reduce fraud, ensure accountability, and facilitate trust in multi-party collaboration processes (Zhao et al., 2022).
However, despite these advancements, several challenges still impede the widespread adoption of digital twins in architecture. One of the key barriers is the lack of standardization across platforms and software. Architectural firms, contractors, and facility managers often use different digital tools, leading to compatibility issues that hinder seamless data exchange. Until the industry adopts universal protocols or standards, digital twins may struggle to reach their full potential (Khajavi et al., 2019).
Additionally, the financial and technical demands of implementing digital twin systems can be overwhelming, especially for smaller architectural firms or public sector projects. Developing a high-fidelity digital twin involves significant investment in hardware, software, data infrastructure, and skilled personnel. As a result, digital twin technology may widen the gap between firms with advanced resources and those struggling with budget limitations (Baricelli et al., 2019).
Lastly, ethical and privacy concerns are becoming increasingly relevant. As digital twins collect continuous data from physical spaces – often involving human activity – there is a pressing need to establish clear policies on data governance and consent. Questions surrounding who own the data, how it is used, and for what purpose must be answered before digital twins can be fully embraced in sensitive environments such as schools, hospitals, or residential buildings (Batty, 2018).
Emerging directions and future challenges in digital twin development are summarized in Figure 9, highlighting key technological, ethical, and standardization trends.
Digital twins trends (https://research.aimultiple.com/digital-twin-trends/)
Digital twin technology is rapidly redefining the way architects, engineers, and facility managers conceive, design, build, and operate buildings. By creating a real-time digital replica of a physical structure, digital twins bridge the gap between design intent and actual performance – enabling data-driven decision-making throughout the entire building lifecycle.
In architectural design, digital twins enhance creativity and precision by simulating how spaces perform under real-world conditions before a single brick is laid. Architects can evaluate sustainability, user experience, and environmental impact with unprecedented accuracy. The integration with BIM technology adds another layer of power, transforming traditional modeling into a dynamic, sensor-fed system that evolves over time. Together, BIM and digital twins foster better collaboration, error reduction, and long-term building intelligence.
During construction, digital twins serve as live dashboards that monitor progress, identify clashes, and simulate construction logistics. Once a building becomes operational, they enable predictive maintenance, optimize energy use, and even improve occupant comfort by responding in real time to environmental and usage data.
Furthermore, the ability to generate digital twins from point cloud data makes it easier to model existing buildings with exceptional accuracy – critical for heritage preservation, renovations, and urban retrofits.
The future of architecture and buildings lies in this digital-physical integration. As cities move toward smarter and more sustainable development, digital twins will be central to achieving carbon neutrality, reducing lifecycle costs, and improving resilience against climate and structural challenges.
In conclusion, the digital twin is not just a tool – it is a paradigm shift in the way we understand and manage the built environment. Its adoption represents a new era where buildings are no longer static structures but living, responsive systems that evolve with us.