Double, Double, Build and Trouble (Shoot): Digital Twins in Architecture, Engineering, Construction

Digitalization is believed to be the fourth industrial revolution and “Construction 4.0” is the corresponding terminology of the fourth industrial revolution (Industry 4.0) in the Architecture, Engineering, and Construction (AEC) industry. Digital Twin is predicted as one of the top ten most promising technology trends over the next ten years and has many successful use cases across sectors.

Digital twin simply represent a dynamic, real-time digital replica (twin) of a physical asset. Ideally, a change in the physical object's state directly leads to a change in the digital object's state and vice versa. The DT concept is acknowledged to have originated in 2002 and is called first time by NASA in 2010.

A recent paper published by the University of Houston Civil Department explores the potential of Digital Twin (DT) technology in civil engineering, advocating for its wider adoption across all project phases. While acknowledging the slower adoption of DT in this field compared to industries like manufacturing or aviation, the authors argue that DT can offer significant value to the often unique challenges of civil engineering projects.

?The paper provides a comprehensive overview of DT, defining its key components as a physical asset, a digital representation of that asset, and real-time bidirectional data exchange enabling feedback and control. The authors propose a five-level classification system for DTs based on their functionalities, ranging from basic digital representations to fully autonomous systems capable of controlling and optimizing physical assets. The core of the paper focuses on a phase-based analysis of DT's potential in civil engineering:

Planning/design Phase

The construction industry is undergoing a digital transformation, with Building Information Modeling (BIM) leading the charge. While BIM has revolutionized design and collaboration, creating a Digital Twin (DT) for a project that doesn't physically exist remains a significant challenge.

This paper offers an innovative solution: the concept of an "indirect DT." By leveraging data from similar or historical structures, we can create a virtual representation that serves as a proxy for the future project. This approach is particularly valuable for high-risk endeavors, providing invaluable insights to inform design decisions.

The paper outlines a five-level framework for DT development, starting with basic BIM visualization and progressing to AI-driven optimization. While the ultimate goal is a fully realized DT, the concept of an indirect DT opens up new possibilities for design optimization and risk mitigation.

By analyzing data from existing structures, we can identify trends, potential issues, and optimal design parameters. This data-driven approach can lead to significant cost savings, improved project outcomes, and increased safety. As the industry continues to evolve, the integration of BIM and DT technologies will become increasingly essential. The indirect DT approach represents a promising step forward in harnessing the power of data to shape the future of construction.

Construction Phase

The construction phase presents unique challenges for creating a Digital Twin (DT) due to its dynamic and rapidly evolving nature. The paper highlights the critical role of Building Information Modeling (BIM) integrated with sensors and IoT technologies in addressing these complexities. This combination enables real-time monitoring, analysis, and visualization of construction progress, safety conditions, and potential design conflicts.

While there have been advancements in using individual DT components, the paper emphasizes the need for a fully automated system with control and feedback mechanisms. Key areas explored include:

• Automated Site Progress Monitoring: Leveraging sensors, laser scanning, and photogrammetry to track and visualize construction progress, though manual intervention is still required.
• Hazard Identification and Construction Safety: Utilizing computer vision and motion tracking to identify safety hazards and monitor worker behavior.
• Clash Detection and Simulation: Employing BIM to automate clash detection, but highlighting the need for a holistic DT to address the root causes of design conflicts.
• Optimized Construction Logistics and Scheduling: Recognizing the potential of DT for optimizing material supply and site operations, but noting a scarcity of practical implementations.

The paper concludes by emphasizing the current limitations of DT in construction and the need for further research to develop fully automated systems with robust control and feedback mechanisms.

Operation and Maintenance Phase

This phase represents where a Digital Twin (DT) finds its most natural application in the lifecycle of a civil engineering project. With a fully realized physical structure in place, the challenge shifts from monitoring a constantly evolving construction site to understanding and managing the long-term performance of the asset. This is where Structural Health Monitoring (SHM) takes center stage, transitioning from traditional, often infrequent, visual inspections to a data-driven, real-time approach powered by DT technology.

The paper explains that SHM, in the context of a DT, leverages a network of sensors embedded within the physical structure. These sensors continuously collect data on various parameters, such as strain, vibration, displacement, and environmental factors. This data is then transmitted to the DT, where sophisticated algorithms and physics-based models come into play. The DT doesn't just archive this data; it actively uses it to update its own virtual representation of the physical asset, mirroring its real-world condition and performance over time. The paper highlight two key areas where DT-driven SHM brings significant value:

• Predictive Diagnostic SHM: the authors primarily focus on the DT's capacity to detect, locate, and assess the severity of damage in real-time. This predictive capability stems from the DT continuously analyzing sensor data and comparing it against established performance baselines. The moment an anomaly arises, whether it's a deviation in stress patterns or unusual vibrations, the DT can instantly flag it, offering invaluable lead time for intervention before the issue escalates. They illustrate this with examples of DTs successfully predicting concrete cracks in bridges or identifying potential failures in tunnel excavations, ultimately preventing catastrophic events and saving lives.
• Predictive Prognostic SHM: While damage detection is crucial, authors emphasize that a truly powerful DT goes beyond merely reacting to existing problems. Predictive Prognostic SHM, as detailed in the paper, focuses on the DT's ability to predict the future evolution of the structure and its components. By understanding how damage might propagate over time or how material degradation will impact structural integrity, the DT enables a shift from reactive maintenance to proactive intervention. Paper suggests this could lead to optimized maintenance schedules, where repairs are conducted precisely when and where needed, maximizing the lifespan of the structure while minimizing downtime and costs.

While the paper paints an optimistic picture of DT-powered SHM in the O&M phase, they also acknowledge that achieving a truly comprehensive and autonomous system (level-5 DT) requires ongoing research. Key areas requiring further exploration include the development of more robust and computationally efficient methods for:

• Data Quality and Management: Ensuring data accuracy, consistency, and security is crucial for DT effectiveness.
• Model Fidelity: Developing accurate and computationally efficient DT models requires ongoing refinement and validation.
• Human-Machine Interaction: Designing intuitive interfaces that facilitate effective interaction between humans and the DT is essential for widespread adoption.
• Interoperability: Achieving seamless integration between different software systems and data sources is a prerequisite for a comprehensive DT.

The authors conclude by emphasizing that DT technology has the potential to revolutionize the O&M phase in AEC industry. By moving from reactive to proactive and predictive strategies, DT-driven SHM can significantly extend the lifespan of structures, optimize resource allocation, and minimize the environmental footprint of built structures. Future research should focus on developing advanced analytics, machine learning, and artificial intelligence techniques to unlock the full potential of DTs.

Demolition phase

While the paper mentions the demolition phase, it acknowledges a significant gap in research concerning DT applications for this phase.

Conclusion Summary

The authors emphasize the importance of a holistic DT approach that incorporates real-time data, feedback loops, and control mechanisms. While acknowledging the challenges, the paper presents a roadmap for DT implementation in civil engineering, including the integration of emerging technologies and addressing data privacy concerns. Ultimately, the successful adoption of DTs can contribute to more sustainable, resilient, and efficient civil infrastructure.

Digital twins are poised to become indispensable tools for the AEC industry. By enabling data-driven decision-making, optimizing resource allocation, and improving collaboration, these virtual replicas will drive innovation and sustainability. As technology continues to advance, we can expect even more sophisticated and integrated digital twin applications to emerge, transforming the way we design, build, and manage our built environment.

Case Studies in Digital Twin Implementation

Arup 2019 review report on digital twin established a metric system for scaling and classification, and also its values through case studies analysis.: Digital twin: towards a meaningful framework | Arup

Infrastructure: Houston Public Works and Arcadis aim to set a new standard in water distribution modeling in a $5.7 million, three-year project to update and develop a digital twin for Houston’s water distribution model. The project aims to improve water distribution, manage localized impacts, and ensure efficient monitoring and service for individual customers. Arcadis will use an “Intelligent Water” approach, integrating AI and digital twins to enhance decision-making and operational efficiency. The initiative seeks to optimize water distribution, manage water quality, and improve customer service through AI-powered analytics. ?

Additionally, a University of Houston professor has secured a three-year $500,000 grant from the Texas Department of Transportation (TxDOT) to explore the use of robots, data and AI for improving bridge inspections and safety. The team will create digital twins of bridges to monitor structural issues and enhance maintenance.

Brisbane Subway Project: The Cross River Rail (CRR) project in Brisbane, Australia's fastest growing city, has pioneered the use of digital twins in the construction industry. By integrating Building Information Modeling (BIM) and Geographic Information Systems (GIS), the CRR created a comprehensive 3D model of the subway system, facilitating planning, construction, and future maintenance. The project also leveraged Unreal Engine to develop an immersive virtual reality experience, allowing stakeholders and the public to visualize the project and understand its impact.

Smart City Planning: Wellington, New Zealand, has adopted a digital twin approach to inform large-scale urban planning decisions. The city's digital twin provides a platform for simulating different scenarios, assessing the impact of proposed developments, and engaging with the public. The Wellington City twin created by The Boundary, uses smart city technologies, with real-time data to provide Transportation statistics for bus, rail, ferry, bike and car, Air traffic visualisations, Cycle sensor data including how many trips were made in a time period, direction of travel, and which streets cyclists travel on, and Car park availability.

China's 51World has developed a comprehensive digital twins of Tokyo, Singapore, Beijing, and Shanghai, utilizing data from various sources to support disaster management, urban planning, and economic development. The digital twin offers insights into traffic flow, air quality, and other urban challenges.

Willow is partnering with Hollywood Park to create a digital twin of the 300-acre sports and entertainment complex, including SoFi Stadium. This virtual replica will optimize fan experience, sustainability, and risk management. SoFi Stadium will utilize this digital twin for daily operations, enhancing fan experience through personalized navigation and optimized amenities. This technology will be crucial for managing large-scale events like the Super Bowl and the 2028 Olympics.

Historic Preservation: A digital twin of Milan's historic masonry cathedral is being used to assess structural integrity and develop preservation plans. By combining photogrammetry, in-situ surveys, and real-time sensor data, researchers are creating a detailed virtual representation of the cathedral.

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