🕑 Reading time: 1 minute
A digital twin is the fusion of a real-world system and a virtual representation designed to control, monitor, and optimize a project’s functionality. With the help of data and feedback, both simulated and physical models can develop capacities for autonomy and learn from their environments.
At first, digital twins may appear to be replicas; however, digital twins are not necessarily realistic representations but are somewhat relevant abstractions of the physical asset. Digital twins need not attempt to mirror everything about the original system. What is essential is the need to develop digital twins that are fit for purpose, while the level of fidelity will vary depending on the primary use cases.
A particularly important function of digital twins is their ability to understand, reason, and provide value by simplifying a complex project. For example, a digital twin of a thermal power plant must simulate the core behavior of the power plant, i.e., production, storage, consumption, etc. together with human inputs to the system and the capacity to deal with unexpected occurrences. Only the simultaneous processing of these various kinds of data will enable efficient control of the network.
The digital twin can have single or multiple stakeholders and may utilize 3D simulations, building information modeling, IoT devices, blockchain, augmented reality, cloud computing, and AI.
It must also be noted that the difference between a digital twin and BIM lies in the way they are connected. In digital twins, the virtual representation is connected to the physical asset, allowing the users to get a real-time operational response. In contrast, BIM is intended just for visualization and collaboration during the design and construction phase.
Digital twins in the built environment range from basic to complex, and are categorized into five types as shown below:
Applications of Digital twins
1. Automated Progress Monitoring
Progress monitoring corroborates that the completed work is executed in accordance with the stipulated plans and specifications. This requires a physical site observation to verify the percentage of work done and determine the work status of the project. By connecting the job site to its digital twin, it is possible to compare the real-time execution and its execution in BIM and take relevant steps to correct any deviations.
2. As-executed Vs. As-planned Models
Through digital twins, it is possible to track changes in an as-executed model on a real-time basis. Early detection of any discrepancies can help with the detailed analysis of historical modeling data, acting as a layer of information for any further decision-making processes.
The project in-charge can then reconstruct the steps that caused the error and make changes in the future work schedule to prevent similar mistakes. They can also spot underperformers and try to remedy the cause of the problem earlier in the project, or plan the required changes to the budget and timescale of the whole project.
3. Resource Management and Logistics
The digital twin technology can help create an information pipeline between the office and the field. With the aid of automatic data delivery, one can predict allocation issues and help balance labor costs with the budget. This helps the job site run more efficiently and affordably.
A twin can make facilities managers aware of how a building is performing in real-time, which enables them to tweak performance to optimize efficiency. This data can be used for planning and designing future buildings.
It is a fact that about 25% of productive time is wasted on unnecessary handling and movement of materials. Digital twin technology provides waste tracking and automatic resource allocation monitoring, allowing for a predictive and lean approach to resource management.
With digital twin, technology companies can avoid over-allocation and dynamically estimate resource requirements on construction sites, thus preventing the need to move resources over long distances and improving time management.
4. Safety Monitoring
The digital twin technology allows the monitoring of hazardous places and people’s movement at site to prevent undesirable behavior, usage of unsafe materials, and activity in hazardous zones.
A company can develop a notification system, letting a project in-charge know when a field worker comes in dangerous proximity to running equipment, and notify about nearby dangers to a worker’s wearable device.
5. Quality Assessment
Image-processing algorithms can help check the condition of concrete through photographic images or videos. It is also possible to check for cracks on columns or material displacement at a job site. This would prompt additional inspections and thus help to detect possible problems at an earlier stage.
6. Optimization Of Equipment Usage
Equipment usage is something every construction firm wants to maximize. Idle machines should be released from one site to another to ensure optimum utilization of equipment at the required job site. Through automatic tracking and advanced imaging, it is now possible to know how frequently a machine is being used, at which location of the site, and on what kind of activity.
7. Monitoring And Tracking Of Workers
Some countries impose stringent regulations on tracking people’s presence on a job site. This involves the need to have a digital record of all personnel and their location within the site, so that this data could be used by the admin in case of an emergency. It is preferred to unify digital twin-based monitoring with an automatic entry and exit registration system, to have a multi-modal data integrated with a single analytics system.
Advantages of Digital Twins
1. Streamlined Supply and Demand Chain
A digital twin can make the entire supply chain transparent by tracking inventory in real-time and then recommending or automating redistribution based on the demand.
2. Enhanced Operational Performance
Digital twins can continuously monitor operations and identify deviations, allowing administrators to react promptly and reduce idle time. They can also apply machine learning for predictive maintenance. For example, in a sewer system with a prescribed direction of flow, predictive maintenance can be utilized to identify blockages by applying classification and anomaly detection algorithms. Other examples include automotive manufacturing plants, power plants, and wind farms.
3. Real-time Data Management
Digital twins can help with the management of assets by keeping records of inventory, processes, historical data, and additional equipment, including manuals and inspection data. This allows owners to identify inefficiencies and ways to address them.
4. Simulation Purposes
Designers and engineers can use digital twin-based models for quick, inexpensive prototyping of new ideas, particularly from the perspective of the end-user.
These twins can factor in anything from noise to weather, human interactions, lighting, and friction. Digital twins of transport hubs, for example, improve passenger experience by identifying the peak times and better understanding human flow, ultimately resulting in reduced congestion. Simulated systems can include buildings, engines, trains, autonomous vehicles, etc.
5. Reduced Construction and Operational costs
Virtual scenarios on construction sequencing and logistics can be run and visualized, acclimatizing workers with required tasks, and reducing costly re-works. Through data-driven decision-making, and AI/ML, they can predict maintenance activities and events, which in turn help navigate unexpected interventions and ultimately streamline costs throughout the asset’s operational life.
6. Increased Productivity and Collaboration
Important information about the built asset can be stored and analyzed throughout its lifecycle. This information (such as design documentation, inventories, material specifications, and programs/schedules) can be easily accessed and used to assist decision-making and facilitate project execution.
7. Optimized Asset Performance and Sustainability
Occupational and operational data can be monitored and analyzed in real-time, providing valuable insights on how the asset is used and currently performing. This data can further help with understanding and avoiding the changes that may be contributing to the decline in the performance of the asset.
