Digital Twin Technology for Bridges: Challenges and Benefits

Creating digital twins of bridges today represents one of the most significant evolutions in management, maintenance, and monitoring of critical infrastructures. Thanks to the creation of a constantly updated digital replica of the physical work, it is possible to substantially improve the decision-making processes related to structural safety, asset management, and lifecycle planning.

In the construction and infrastructure sector, digital twins are now considered key tools not only in the design phase but especially in the operating and management phases. In this article, we focus in particular on the potential of bridge digital twins, that is, the digital twins applied to bridges, analyzing benefits, usage methods, and differences compared to traditional monitoring systems.

Managing entities, designers, and operators, through specialized software solutions such as:

• a digital twin software, can virtually replicate bridges and infrastructures of any kind and analyze, simulate, and optimize the performance of their physical resources in real-time;
• a Bridge Management Software, can simplify the management and maintenance processes of bridges and ensure higher standards of quality, safety, and efficiency.

What is a bridge digital twin?

Digital twins are computerized models aimed at virtually replicating systems and objects from the real world. In other words, digital twins are true digital replicas of a physical object or system designed to facilitate and improve decision-making processes, such as bridge and infrastructure management.

They are used in the engineering sector to provide a detailed and interactive digital representation of construction projects (such as those related to buildings, facilities, infrastructures, or entire cities).

The ability of digital twins to establish constant communication with their physical counterpart has transformed these models into a powerful tool for design, management, and monitoring of critical infrastructures, including bridges, viaducts, overpasses, etc.

The digital twin of a bridge, or bridge digital twin, can therefore be considered as the virtual copy of a real bridge and constitutes a unique and centralized system through which it is possible to manage all aspects related to safety, maintenance, and monitoring of the corresponding physical infrastructure.

Digital twin of a bridge

How does a bridge digital twin support asset management and lifecycle planning?

From the perspective of infrastructure asset management, the bridge digital twin plays a strategic role because it accompanies the bridge throughout its lifecycle, from the operational phase to any upgrading or end-of-life interventions.

Unlike a static model, the digital twin constitutes a system:

• centralized;
• updatable;
• decision-making oriented.

Within the bridge digital twin, various types of data converge:

• geometric and structural information;
• technical and performance data;
• operational and maintenance information;
• data from real-time monitoring.

This enables overcoming fragmented management of the infrastructure and adopting an approach based on objective and continuously updated data.

From reactive maintenance to predictive maintenance

Thanks to the constant connection with the physical structure, the bridge digital twin allows to:

• timely intercept anomalies and degradation signals;
• analyze the evolution of structural behavior over time;
• plan interventions before critical situations arise.

In this way, one transitions from a reactive maintenance, based on emergencies, to a predictive maintenance, which is more efficient and sustainable.

Support for lifecycle planning

The digital twin particularly supports:

• the scheduling of maintenance interventions in the medium and long term;
• the comparative evaluation of different intervention strategies;
• the estimation of the economic, operational, and managerial impact of decisions;
• the creation of a digital history of the infrastructure, useful for audits, inspections, and future verifications.

Integration of BIM and GIS data in a bridge digital twin model

A truly effective bridge digital twin arises from the integration of BIM informational models with GIS geospatial data, thus placing the infrastructure not only as an isolated object but as part of a broader territorial system. This integration enriches the digital twin with fundamental information related to the context in which the bridge is located.

BIM provides a detailed three-dimensional representation of the structure, along with the geometric, structural, and performance data necessary for lifecycle management. The GIS, on the other hand, adds the territorial dimension, allowing data such as the geographic location, environmental characteristics, the surrounding infrastructure network, urban planning constraints, and accessibility conditions to be associated with the bridge.

Within a bridge digital twin, the combination of BIM and GIS enables, for example, to:

• precisely locate the infrastructure within the road or railway network;
• analyze the bridge’s interaction with the surrounding environment, such as waterways, areas at risk of hydrogeological instability, or zones subject to heavy traffic;
• support inspection and maintenance activities by planning interventions based on territorial and logistic conditions;
• improve asset management at the network scale, especially when working on a set of bridges distributed throughout the area.

The BIM–GIS integration also represents a key element in ensuring data interoperability and information continuity across different phases of the infrastructure lifecycle. In this context, Bridge Information Modeling (BrIM) constitutes a natural evolution of BIM applied to bridges, laying the groundwork for establishing consistent, updatable, and truly useful digital twins for decision-making processes.

BIM and GIS integration

Bridge digital twin and structural monitoring: differences compared to traditional systems

Traditional systems of structural monitoring of bridges generally rely on the installation of sensors dedicated to the real-time collection of information regarding:

• environmental conditions;
• material aging;
• structural safety;
• traffic impact and other significant parameters.

This information is transmitted to a digital platform intended for monitoring, where it can be processed and analyzed to identify any anomalies or critical situations.

Although this approach allows for effective control of structural performance, it often remains limited to just reading the data and does not always allow for an integrated view of the infrastructure as a whole. Traditional monitoring systems often operate as isolated tools, disconnected from the informational models and asset management decision processes.

The bridge digital twin overcomes these limitations by integrating monitoring data within a dynamic informational model, where geometry, technical information, and real-time data coexist in a single digital environment. In this way, the values detected by the sensors are not simply displayed, but contextualized concerning the structural behavior of the bridge and its evolution over time.

Unlike traditional monitoring systems, the bridge digital twin also allows for:

• simulating load or degradation scenarios directly on the digital model;
• evaluating the impact of different intervention strategies before their implementation;
• supporting decisions based on a global view of the asset, rather than on individual indicators.

The shift from traditional structural monitoring to the bridge digital twin thus represents an evolution from a reactive and fragmented approach to a predictive and integrated model, in which data become an active part of the infrastructure management process. A broader comparison between informational models and digital twins is available in this in-depth analysis of the differences between BIM and Digital Twin.

Key technologies behind a bridge digital twin: IoT, AI, sensors, and simulation

At the core of a bridge digital twin is a set of technologies that work together to enable monitoring, analysis, and simulation of the infrastructure’s behavior over time. Among these, a central role is played by:

• IoT devices (Internet of Things): interconnected systems that communicate with each other through the internet and include sensors, actuators, and data processing components. In the context of bridges, IoT devices are used to monitor parameters such as vibrations, deformations, temperature, and traffic, allowing for continuous control of structural conditions and supporting more efficient and timely maintenance;
• smart sensors: installed at strategic points of the physical infrastructure, they detect structural and environmental variations providing essential information on the bridge’s health status. Vibration and deformation sensors allow for the identification of anomalies and changes in structural behavior, while temperature and humidity sensors help monitor conditions that may affect material durability and promote degradation phenomena;
• advanced simulation tools: allow for analyzing the behavior of the bridge in hypothetical scenarios, evaluating the effects of variable loads, extreme environmental conditions, or different intervention strategies. Simulations help anticipate the structure’s responses and support technical decisions before intervening on the actual structure;
• artificial intelligence (AI) algorithms: used to analyze large amounts of historical and real-time data, identify recurring patterns, and predict degradation phenomena. Through machine learning techniques, AI can contribute to early identification of anomalies, classification of structural criticalities, and defining maintenance intervention priorities.

If you want to delve deeper into the role of these technologies, I recommend a contribution dedicated to digital twins and the digital transformation of the AEC sector.

Digital Twin Elements

The contribution of digital twin bridges to sustainability and the reduction of carbon footprint

Digital twin bridges represent an effective tool for improving the sustainability of infrastructures, as they allow for optimizing technical decisions and reducing waste throughout the entire lifecycle of the project.

In particular, the use of a digital twin contributes to the reduction of carbon footprint thanks to:

• targeted and predictive maintenance, which limits unnecessary interventions, repeated inspections, and frequent construction sites, reducing resource consumption and associated emissions;
• preventive simulation of interventions, which allows for comparing different design and maintenance solutions, choosing those with the lowest environmental impact and minimal traffic interference;
• optimization of management activities, thanks to more efficient planning of interventions and reduced movements and field operations;
• extension of the infrastructure’s useful life, achieved through early identification of degradation phenomena and the preservation of structural performance over time.

Use cases of digital twin bridges: urban viaducts, rural bridges, and highway overpasses

Digital twin bridges find application in very different infrastructural contexts, adapting to the specific operational, environmental, and managerial needs of each type of bridge. Let’s take a look at the main use cases:

• urban viaducts: in urban viaducts, characterized by high traffic flows and strong interaction with the built environment, the digital twin bridge supports continuous monitoring of structural performance and the assessment of the impact of dynamic loads. The digital model also allows for planning maintenance interventions, reducing interference with urban mobility and improving safety levels in densely populated areas;
• rural bridges: for rural bridges, often distributed over vast and difficult-to-access territories, the digital twin represents an effective tool for remote monitoring of structural and environmental conditions. This approach allows for limiting on-site inspections that are not strictly necessary, optimizing resource allocation, and defining intervention priorities based on the actual state of degradation of the structure;
• highway overpasses and flyovers: in the case of overpasses and flyovers, subjected to high loads and particularly severe operating conditions, the digital twin bridge allows for analyzing structural behavior under heavy traffic and for early detection of fatigue or degradation phenomena. The simulations support the coordinated planning of interventions, contributing to ensuring service continuity and safety along the main transportation arteries.

The application of the digital twin bridge to different types of structures demonstrates how this technology can support a more informed and effective management of the entire infrastructure heritage. For a general overview of the characteristics and complexities of crossing structures, I recommend exploring the topic in the article entirely dedicated to bridge construction.

Most common data formats and standards for digital twin bridges (IFC, openBIM, etc.)

The realization and effective management of a digital twin bridge require the adoption of open data formats and standards, capable of ensuring interoperability, informational consistency, and continuity throughout the entire lifecycle of the infrastructure. In the infrastructural field, these aspects are particularly relevant, as they involve numerous actors, disciplines, and different software tools.

Among the most commonly used formats is the IFC (Industry Foundation Classes), an open standard that allows for a structured representation of the geometry and information associated with the elements of the bridge. The use of IFC overcomes the limitations of proprietary formats, facilitating data exchange between different platforms and making the informational model accessible even in the long term.

The openBIM paradigm extends this concept, promoting a collaborative approach based on shared standards, transparent processes, and open information flows. Within a digital twin bridge, openBIM allows for the integration of models, monitoring data, management information, and simulation results into a coherent digital ecosystem, reducing the risk of information loss or duplication.

In addition to informational models, standards for data management and informational requirements also play an important role, allowing for a clear definition of what information must be present in the digital twin and at which phase of the lifecycle. This aspect is crucial to ensure that the digital twin bridge remains updated, reliable, and truly usable to support decision-making.

The role of digital twin bridges in smart cities and connected infrastructures

Digital twins of bridges fit naturally within smart cities, where infrastructures, mobility systems, and urban services are increasingly interconnected and data-driven. In this context, the digital twin of the bridge is no longer an isolated system but becomes an integral part of a broader infrastructural network.

The integration of the bridge digital twin with urban platforms allows to:

• share structural and monitoring data with traffic management systems;
• support authorities in emergency management and safety planning;
• coordinate infrastructural interventions with other urban services (transport, utilities, civil protection);
• improve the resilience of linking networks through a unified view of assets.

Thanks to the connection with other digital twins and territorial information systems, bridge digital twins contribute to the creation of connected infrastructures, capable of adapting to operational and environmental conditions in real time. This approach fosters a more efficient management of the infrastructural assets and strengthens the role of data as a central element for city governance.

If you want to deepen the role of digital twins in the context of smart infrastructures, I recommend reading the article dedicated to digital twin and infrastructures.

VR simulations

How to create the digital twin bridge step by step

The creation of a digital twin bridge follows a structured path that, although varying based on the complexity of the work, can be traced back to some fundamental phases. Let’s look at the main ones together:

1. Survey and geometric modeling of the structureThe first step involves accurately surveying the bridge, using techniques and tools appropriate to the context, such as topographic surveys, laser scanners, or drones. A reliable geometry represents the indispensable basis for constructing a coherent digital model.
2. Creation of the BIM model of the bridgeBased on the survey data, the BIM model is developed, enriched with structural information, materials, and technical data necessary for managing the work. In this phase, the adoption of Bridge Information Modeling (BrIM) allows for structured organization of the information and prepares the model for the evolution towards the digital twin.
3. Integration of sensors and IoT devicesThe BIM model is then connected to the physical counterpart through sensors and IoT devices. The data collected in real-time (vibrations, deformations, environmental conditions, traffic) are associated with the elements of the model, transforming it into a dynamic and continuously updated system.
4. Management, analysis, and simulation of dataThe digital twin bridge is finally integrated into a management platform that allows for data analysis, scenario simulation, intervention planning, and report generation to support decision-making. In this phase, the digital twin becomes an operational tool for monitoring, maintenance, and management of the infrastructure.

If you also want to get the most out of your resource management, you can rely on a Bridge Management Software, the complete solution for bridge management that allows you to conduct smart inspections, create accurate maintenance plans, generate customized reports, easily locate your infrastructures, and ensure efficient integration with IoT technology.

With this system, you will be able to simulate the impact of various design solutions directly on the BIM model of your infrastructure. You will also have at your disposal a powerful platform from which you can centrally manage all activities related to the safety, monitoring, and maintenance of your bridges or any other infrastructure work.

FAQ – Bridge Digital Twin for Bridges

What is a digital twin applied to bridges?

A bridge digital twin is the virtual replica of a real bridge: a centralized and dynamic digital model that allows for integrated management of safety, maintenance, and monitoring of the physical infrastructure. Unlike a simple 3D model, it links geometry, technical information, and operational data (even in real time) within a single environment.

How does a bridge digital twin differ from a traditional BIM model?

BIM provides a detailed three-dimensional representation with geometric, structural, and performance information useful for life cycle management. The bridge digital twin emerges when that model is connected to its physical counterpart and becomes updatable over time, integrating monitoring data, historical data, and operational information to support decisions and planning.

How does digital twin technology work for bridge maintenance?

Digital twin technology supports maintenance because it integrates survey data, information modeling, and monitoring into a single system. Thanks to constant connection with the physical asset (via sensors and IoT devices), the bridge digital twin enables:

• detecting signs of degradation and anomalies;
• analyzing the evolution of structural behavior;
• planning interventions before critical situations occur.

What are the benefits of using digital twins for infrastructure management?

The main benefits, from an asset management and life cycle management perspective, include:

• a centralized environment where geometric, technical, operational, and monitoring data converge;
• support for maintenance planning in the medium to long term;
• the ability to evaluate alternative strategies before taking action;
• estimation of the economic, operational, and managerial impact of decisions;
• creation of a digital history useful for audits, inspections, and verifications.

Why is a bridge digital twin more effective than traditional structural monitoring?

Traditional monitoring collects data (environmental, material, structural safety, traffic) and visualizes it on often separate and poorly integrated platforms. The bridge digital twin, on the other hand, contextualizes that data within a dynamic information model: it does not just “show the value,” but helps interpret it concerning geometry, components, and behavior over time, also supporting simulations and intervention choices.

What technologies make a bridge digital twin possible?

A bridge digital twin relies on integrated technologies, including:

• IoT to connect the structure and transmit data (vibrations, deformations, temperature, traffic);
• smart sensors to detect structural and environmental variations;
• advanced simulation tools to assess load scenarios, extreme conditions, and intervention strategies;
• AI algorithms to analyze historical and real-time data, identify patterns, and predict degradation phenomena.

What standards and data formats are common for managing a bridge digital twin?

To ensure interoperability and informational continuity throughout the life cycle, the IFC, an open standard for representing geometry and information of the elements of the work and facilitating data exchange between platforms, and openBIM, an approach based on shared standards and open information flows, are central to integrate models, monitoring data, and simulation results into a coherent ecosystem.

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