Digital twins in construction: Getting started

While Building Information Modeling (BIM) provides static or partially updated 3D representations of structures, digital twins integrate live data that reflects the current state of the asset. Unlike traditional measurement or analytic tools, a digital twin continues to evolve throughout the asset’s lifecycle.

Digital twins provide context for making operational decisions, running predictions, and conducting simulations about potential changes or disruptions. In construction, this capability enables monitoring jobsite progress, optimizing resource allocation, managing risk proactively, and improving facility operations after project completion.

Digital twins vs. BIM: Understanding the distinction

Building Information Modeling (BIM) provides static or partially updated 3D representations of structures, while digital twins go further by integrating live data that reflects the current state of the asset. Key differences include:

• Real-time updates: Digital twins continuously update with fresh data from sensors, enterprise systems, and manual entries, while BIM models are typically updated periodically.

• Operational focus: Digital twins support operational decision-making, predictions, and simulations, whereas BIM primarily focuses on design and construction.

• Dynamic vs. Static: Digital twins evolve throughout the lifecycle of an asset, while BIM models are relatively static representations.

• Data integration: Digital twins integrate diverse data sources (IoT sensors, weather data, maintenance records), while BIM primarily focuses on design and structural information.

BIM can serve as a foundation for digital twins, but the latter extends capabilities significantly by incorporating real-time operational data.

Digital twins in structural engineering

In structural engineering, a digital twin creates a real-time virtual model of a structure that continuously updates based on sensor data, inspection records, and operational information. This enables:

• Monitoring structural health in real-time by tracking strain, vibration, settlement, and other critical parameters

• Simulating how a structure will respond to external forces like earthquakes, wind loads, or increased occupancy

• Predicting maintenance needs before structural issues become critical

• Optimizing performance by adjusting systems based on actual usage patterns

• Creating a comprehensive record of structural behavior over time

For example, a digital twin of a bridge might include sensor data measuring vibration, temperature, load, and settlement, along with inspection reports and maintenance records. This allows engineers to understand how the structure is performing compared to design expectations and identify potential issues before they lead to failures.

Why digital twins matter in construction

The construction industry routinely deals with fragmented data across silos. Architects, engineers, contractors, suppliers, and facility managers each use their own systems and standards. This fragmentation hampers decision-making and introduces expensive inefficiencies and delays.

Digital twins enhance visibility by allowing project managers and facility operators to see the real-time status of everything from equipment on a jobsite to the flow of materials through the supply chain. They enable proactive issue detection by integrating live data to flag deviations from planned progress, enabling early intervention to prevent schedule overruns and cost escalations.

Teams can simulate scenarios in the digital twin before physically moving machinery or installing complex systems to identify bottlenecks or unintended consequences. After construction, digital twins serve as operational dashboards, continuously monitoring conditions such as temperature and energy use while supporting predictive maintenance.

Real-world application example

A digital twin building could be a hospital project where the digital replica integrates real-time data from various sources. For instance, the digital twin might include sensor data tracking HVAC performance, occupancy levels, energy usage, and equipment status. This allows facility managers to monitor building conditions, simulate changes before implementation, and perform predictive maintenance. During construction, the digital twin would show real-time progress, material deliveries, and resource allocation, helping project managers identify potential issues before they cause delays.

Core technologies

Building a digital twin requires orchestrating multiple technologies. Sensor networks and IoT devices collect real-time data about the environment, equipment, and personnel. Software platforms consolidate information from diverse sources, harmonize it, and make it available through interactive dashboards or data feeds.

At the heart of a modern digital twin platform is a data processing layer capable of ingesting changes as they happen. Traditional databases and data warehouses often introduce latency or require costly, batch-oriented processes not suitable for live operations.

A construction manager might need to join live feeds from equipment trackers, weather APIs, material delivery schedules, and worker attendance logs to determine whether work can proceed on a given day. Traditionally, this would require extensive data engineering and custom middleware. Streaming platforms optimized for live data transformation make this process manageable and reliable.

Getting started: laying the groundwork

Launching a digital twin initiative in construction should be approached methodically through several key phases.

1. Define clear use cases

Not every problem in construction requires a digital twin. Start by identifying areas where real-time visibility or simulation will provide tangible benefits. Common entry points include monitoring the movement of assets such as cranes, excavators, and vehicles on construction sites.

Tracking the flow of critical materials like steel and concrete from warehouse to project location represents another valuable application. Coordinating multidisciplinary teams on schedule-sensitive projects and improving facility management post-construction also provide clear benefits.

Consider a general contractor working on a complex hospital project in an urban area. Coordination of deliveries is critical, as delays could stall multiple trades. By integrating GPS data from supplier trucks, RFID tags on delivered materials, and schedule milestones into a digital twin, the team can predict when supply chain interruptions are likely and redirect resources accordingly.

2. Assess data sources and integrations

Digital twins succeed or fail based on data quality and accessibility. Construction data often resides in disparate systems including proprietary BIM software, fleet management platforms, ERP and procurement tools, and manual spreadsheets.

A pragmatic first step is to catalog available data sources and assess how data will be extracted. Some feeds like equipment telematics may provide streaming APIs, while others might only offer daily extracts or require manual entry. Modern platforms support both batch and streaming ingestion, but the closer you get to true real-time data, the greater the value.

A subcontractor managing dozens of job sites may already use IoT sensors to monitor environmental conditions such as humidity, dust, and noise. When integrated into the digital twin, this information supports both safety compliance and process optimization.

3. Select an appropriate platform

Technical complexity increases quickly when building digital twins at scale. The chosen solution should support real-time data ingestion and transformation, integration with existing construction and facility management tools, and secure and granular access controls for diverse users.

The platform should also provide flexible modeling to represent both physical components like structural elements and machines, and logical components such as work schedules and contractual milestones.

A mid-sized construction firm retrofitting bridges in a region prone to severe weather could use a platform to define a real-time view combining stream gauge readings, site camera feeds, weather alerts, and structural health monitoring sensors. When certain thresholds are crossed, such as water levels spiking after a storm, the system triggers alerts and updates safety protocols automatically, minimizing response lag.

4. Develop a representation model

Creating the digital twin involves modeling the target asset or process. This could be as granular as individual bolts in a bridge or as broad as the end-to-end supply chain for a large infrastructure project.

In construction, a layered approach often works best. The base layer mirrors BIM or CAD models, while additional data layers represent system status, job progress, and operational data.

Static data includes as-built drawings and material specifications. Dynamic data encompasses sensor readings, jobsite photos, change orders, and incident reports. By combining both sources, the digital twin becomes a living document of all physical and operational changes.

5. Plan for iterative deployment

Digital twins deliver value quickly when outcomes are measurable and incremental. Many construction organizations start with pilot projects focused on a high-value segment such as a critical equipment fleet, a flagship project, or a set of energy-intensive building systems, and expand as business value is demonstrated.

Continuous feedback loops are important. Insights generated by the digital twin should inform onsite practices and strategic decisions, while lessons from each deployment inform future rollouts. This iterative model effectively manages risk and encourages buy-in across the organization.

Implementation costs and considerations

The cost of building a digital twin varies significantly based on complexity, scale, and desired functionality. Factors affecting cost include:

• The number and type of sensors and IoT devices required

• Integration complexity with existing systems

• Data processing platform selection

• Modeling requirements and level of detail

• Implementation and training costs

Small-scale projects might start at tens of thousands of dollars, while comprehensive digital twins for large infrastructure projects could run into millions. Organizations typically start with focused use cases that demonstrate ROI before expanding to more complex implementations. Costs can be managed by taking an iterative approach, starting with high-value segments and expanding as value is demonstrated.

Practical implications and patterns

Implementing digital twins transforms not just project execution, but also organizational data culture and operational models. They simplify architectures that might otherwise require complex, brittle code to synchronize disparate databases and systems.

Digital twins unlock new capabilities for operational intelligence. Live inventory tracking helps construction supply chains, which are vulnerable to disruption. With a digital twin, inventory managers receive up-to-the-minute alerts if a shipment is delayed or a supplier is running behind, allowing for rapid rescheduling.

Real-time digital twins form the backbone for deploying AI agents such as virtual assistants or intelligent monitoring systems, which need accurate, always-fresh data to act reliably. In multi-phase projects spanning geographies, digital twins aggregate process and status data in one consistent view, reducing friction and miscommunication between teams.

Consider a construction firm overseeing maintenance and repair schedules for hundreds of elevators in city buildings. By building a digital twin of each elevator that tracks usage statistics, recent faults, replacement parts installed, and technician visits, the company can forecast failures before they occur, dispatch teams efficiently, and maintain government compliance with less manual effort.

Overcoming organizational barriers

Like all major technology initiatives, deploying digital twins in construction brings challenges. Data quality can be uneven, especially if source systems are outdated or lack integration capabilities. Organizations need clarity on who owns specific data streams, how privacy and security are enforced, and what steps must be taken when anomalies are detected.

There is also a skills gap to be managed. Digital twins bring together IT staff, data engineers, construction managers, and domain experts. Alignment around shared objectives and vocabulary is crucial. The adoption of standard protocols and platforms designed to reduce operational complexity mitigates many pitfalls.

The path forward

Adoption does not need to be an all-or-nothing endeavor. By starting with targeted use cases such as supply chain monitoring or equipment management, firms can quickly demonstrate value, build organizational momentum, and lay the foundation for a data-driven future.

Digital twins are not just about technology. They change how teams interact with information, make decisions, and create value throughout the lifecycle of the built environment. For construction leaders looking to remain competitive, getting started with digital twins is increasingly not a question of if, but when.

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