Immersive Bridge Digital Twin for Damage Assessment

Computers in Industry 164 (2025) 104189
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Computers in Industry
journal homepage: www.sciencedirect.com/journal/computers-in-industry
Development of immersive bridge digital twin platform to facilitate bridge
damage assessment and asset model updates
Muhammad Fawad a,b, Marek Salamak a , Qian Chen c,* , Mateusz Uscilowski a , Kalman Koris b,
Marcin Jasinski a, Piotr Lazinski a, Dawid Piotrowski a
a
b
c
Department of Mechanics and Bridges, Faculty of Civil Engineering, Silesian University of Technology, Poland
Department of Structural Engineering, Budapest University of Technology and Economics, Hungary
Division of Civil Engineering, School of Engineering, University of British Columbia Okanagan Campus, Canada
A R T I C L E I N F O
A B S T R A C T
Keywords:
Augmented Reality (AR)
Structural Health Monitoring (SHM)
Bridge infrastructure
Digital Twin (DT)
Immersive planning and control
Conventional infrastructure asset management practices have heavily relied on static data collection and suffered
from decision lags. Though advanced Structural Health Monitoring (SHM) systems were extensively explored
based on multi-functional sensor deployment, asset model updating has not been achieved to facilitate timely
and effective decision-making of infrastructure managers due to a lack of system integration. To address this
challenge, this study develops the Immersive Bridge Digital Twin Platform (IBDTP) to allow infrastructure
managers to automate the SHM processes of bridges and engage them in immersive decision-making processes
based on Scan-to-BIM and Augmented Reality (AR) technologies. A novel 3D game engine is proposed as part of
IBDTP and was tested using a single-span concrete arch bridge located in Poland. Results show that the mea­
surement data collected and presented in IBDTP improves the infrastructure managers’ accessibility to major
damage data of the bridge to plan for future interventions. The functions of the IBDTP can be potentially scaled
for different types of bridges and critical infrastructure, substantially improving the traditional SHM in terms of
data management and 3D structural visualization.
1. Introduction
Bridges are an integral component of infrastructural networks,
bearing the weight of various operational and environmental factors
that can gradually influence their structural performance and integrity
over the years (Zhou and Sun, 2019; Zumstein et al., 2022; Fawad et al.,
2019). Maintaining their long-term durability and safety necessitates the
implementation of robust Structural Health Monitoring (SHM) systems
(Kaloop et al., 2022; Al-Nasar and Al-Zwainy, 2022). The SHM facilitates
effective maintenance planning by assessing the structural health of
bridges in order to foresee deterioration and catastrophic scenarios
(Ndinga Okina et al., 2023). The SHM of bridges received the attention
of bridge maintenance authorities in the past few decades (Alokita et al.,
2018; García-Macías and Ubertini, 2022). The prevalent SHM practices
have focused on the use of information communication technologies
such as fiber optic sensors and the application of advanced algorithms
such as bayesian inference and probabilistic approaches (Figueiredo E,
2022; Sonbul and Rashid, 2023; Deng et al., 2023; Palma et al., 2023).
Despite these data-driven methods, infrastructure managers find it
challenging to obtain the up-to-date information about the bridges and
have difficulties understanding the complex 3D geometry of structures
(Omar and Nehdi, 2018; Vagnoli M and Remenyte-Prescott, 2018).
Integrating technology with the SHM system will be a viable technical
solution for bridge asset monitoring (Fawad et al., 2024; Mohammad­
khorasani et al., 2023). Thus, there is a growing need for innovative
solutions to help them establish a comprehensive understanding of
bridge condition states and associated information around operation
and maintenance (Palma et al., 2023; He et al., 2022a). Particularly new
solutions are needed to help them identify issues early and take proac­
tive measures to maintain the safety and longevity of the assets.
Emerging technologies such as reality capture technologies and
building information modelling (BIM) technologies hold the promise of
revolutionizing the infrastructure management processes, offering new
avenues for data depth, accessibility, and three-dimensional
* Corresponding author.
E-mail addresses: [email protected] (M. Fawad), [email protected] (M. Salamak), [email protected] (Q. Chen), [email protected]
(M. Uscilowski), [email protected] (K. Koris), [email protected] (M. Jasinski), [email protected] (P. Lazinski), [email protected]
(D. Piotrowski).
https://doi.org/10.1016/j.compind.2024.104189
Received 16 April 2024; Received in revised form 5 September 2024; Accepted 12 September 2024
Available online 23 September 2024
0166-3615/© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC license (http://creativecommons.org/licenses/bync/4.0/).
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Computers in Industry 164 (2025) 104189
visualization (Chen et al., 2018, 2020; Fawad et al., 2023). Numerous
studies have emphasized the benefits of Scan-to-BIM in automating data
collection for structural maintenance through 3D laser scanning (Allegra
et al., 2020; Qiu et al., 2022) whose data is captured from the project site
in the form of point clouds (Tang et al., 2022). The point cloud data can
be further processed and integrated with the BIM tool to enable
real-time detection of structural irregularities and deformations, there­
fore contributing to the development of safety and reliability programs
for bridges (Bosché et al., 2015; Zhao et al., 2022; Meyer et al., 2022).
The Scan-to-BIM approach also plays a vital role when integrated with
SHM as it yields a Digital Twin (DT) model of the bridge (Zhang et al.,
2023). This DT model often integrates a computational model of a
bridge with a real bridge equipped with sensors to monitor and optimize
asset performance, utilizing bidirectional data exchange between
physical and digital models of a bridge to enable almost real-time
monitoring, prompt anomaly detection, and proactive maintenance of
the bridge asset (khorasani and Fernando, 2023). This integrated
framework has proved to be specifically useful to maintain structural
integrity under dynamic conditions (Gao et al., 2023).
To enhance the user experience of implementing DT models,
immersive technologies have been explored to support model visuali­
zation, such as Augmented Reality (AR). Practitioners developed tool­
kits such as Trimble XR10 that include intelligent AR-based data
dashboards to engage construction and infrastructure stakeholders to
obtain intuitive information about projects. Current practices, however,
may focus on siloed applications ranging from the Scan-to-BIM approach
for BIM generation, DT for infrastructural management, to applications
of AR for bridge inspection individually. A lack of research is reported to
showcase the use of the Scan-to-BIM method as the basis of an updated
DT model for bridge SHM which can be further used with an AR
application for immersive and interactive decision-making. This
research intends to fill this research gap where a novel gamification
approach is developed to develop DT of bridge SHM based on the Scanto-BIM approach. Further, the same DT model is converted to an AR
application deployed to an AR headset which can perform real-time
onsite bridge health monitoring. To leverage the benefits of existing
technologies, this study develops an Immersive Bridge Digital Twin
Platform (IBDTP) to improve infrastructure management and moni­
toring. This immersive DT explains the conception, development, and
implementation of an integrated bridge digital twin, and how damage
detection can be performed for intervention planning.
The rest of the paper is structured as follows: Section 2 presents the
literature review on the existing approaches to SHM and DT develop­
ment for bridges; Section 3 describes the research framework of IBDTP
including the designing of the SHM system, IoT data collection, and
visualization of bridge SHM data using AR-based 3D game engines and
simulations. Section 4 illustrates the case study of a concrete arch bridge
in Poland to test the usefulness of the proposed IBDPT which emphasizes
how this digital twin supports predictive decision-making, allowing
authorities to anticipate possible structural problems, maximize main­
tenance plans, and schedule interventions ahead of time. Section 6
discusses the managerial implications of the IBDPT, followed by the
conclusions and future work in Section 7.
distributed sensing systems, feature high sensitivity, long-range capa­
bilities, and immunity to electromagnetic interference to be suitable for
deployment in large infrastructure assets. Nevertheless, these methods
exhibit certain constraints, as they are highly dependent on manual
intervention, causing several issues such as time-consuming processes,
labor-intensive work, involvement of human errors, and challenged
quantification of measured data (Zhang et al., 2022). Moreover, they
lack comprehensiveness and detailed damage evaluation in the context
of advancements in bridge monitoring (Sonbul and Rashid, 2023).
Considering these limitations, these methods were replaced by digitally
advanced and robust sensors like wired strain gauges, fiber-optic sen­
sors, acceleration sensors, piezoelectric transducers, and Liquid leveling
sensors for accurate and rapid monitoring of bridge health (Vegas, 2021;
Chen et al., 2017; He et al., 2022b), but even the system using such tools
have limitations concerning data management and 3D visualization of
structural defects in real-time.
With the increasing boom of technological advancement, Artificial
Intelligence, Digital Twins, and Virtual/Augmented Reality (VR/AR)
have taken over the structural monitoring domain of the construction
industry (Nassereddine et al., 2022). For example, AI algorithms such as
Convolutional Neural Networks (CNNs), Recurrent Neural Networks
(RNNs), and Autoencoders are utilized for feature extraction, anomaly
detection, and classification tasks in SHM. Bayesian models have shown
the potential to update the probability of damage or failure based on
new data and provide a probabilistic framework for decision-making.
These methods can process large volumes of data and identify patterns
associated with structural damage. The SHM and digitalization tools are
becoming more popular in bridge engineering thus their integration in
the context of bridge health monitoring is a cutting-edge approach
(Mohammadkhorasani et al., 2023). It combines real-time AR experi­
ences with SHM data, bridging the gap between digital and physical
assets, and offering an innovative platform for the monitoring and
maintenance of bridges (Dong C-Z, 2021; Moreu et al., 2017). By
superimposing digital information onto the physical bridge, AR en­
hances visualization, real-time data analysis, and decision-making ca­
pabilities (Yogeeswaran et al., 2023; Awadallah and Sadhu, 2023).
2.2. Advancement in digital twin infrastructure
The Digital Twin of a bridge comprises a connectivity module that
enables synchronization of the physical and virtual assets along the as­
set’s life cycle stages (Sacks et al., 2020), as well as a virtual duplication
of an actual bridge (Honghong et al., 2023). Existing DT models help in
bridge risk assessment, creating comprehensive 3D models for simu­
lating various load scenarios, assessing safety, and prioritizing mainte­
nance actions (Ye et al., 2019). Furthermore, the technology supports
real-time monitoring of structural movements, tracking deformations,
displacements, and settlements under diverse loads, ultimately ensuring
safe and efficient bridge operation (Hielscher et al., 2023). Specifically,
the Augmented Reality (AR) technology has been viewed as an imple­
mentation framework for DT models and was explored in use in the
context of bridge health monitoring (Mohammadkhorasani et al., 2023).
Focusing on the Augmented Reality (AR) for maintenance opera­
tions, Palmarini et al. emphasize its role in administrative tasks, tech­
nical functions, and decision-making processes (Palmarini et al., 2018).
Li et al., conducted an extensive review of the advantages of AR and
Virtual Reality (VR) within the construction sector to identify the trend
of utilizing computer vision algorithms for AR platforms, employing AR
headsets as effective interfaces for image-based techniques in the realm
of visualized field inspection (Li et al., 2018). Some scholars have
underscored the benefits of the integration of AR with Artificial Intel­
ligence (AI) for the classification and quantification of structural dam­
age with impressive accuracy and in-situ visibility. Wang et al.,
integrated Deep Learning (DL) models with AR headsets to facilitate
real-time detection of structural behaviour changes caused by factors
such as corrosion or fatigue (Shaohan Wang et al., 2019). Malek and
2. Literature review
2.1. Advancement in bridge structural health monitoring
Structural Health Monitoring (SHM) systems have experienced a
tremendous transformation from the conventional bridge monitoring
techniques which relied more on direct measurements of bridge
response using the traditional sensors on the bridge (Wan et al., 2023;
Premjeet et al.). These conventional methods involve direct assessment
of structural health using visual inspection techniques or simple hand­
held devices like portable sensors (Washer et al., 2019; Elhattab and
Uddin, 2018). These sensors, including Fiber Bragg Gratings (FBGs) and
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Moreu et al., studied the conceptual design of AR systems for structural
inspection, assessing their capabilities in hologram generation and their
impact on visual inspection, defect visualization, virtual measuring, and
documentation tasks (Malek and Moreu, 2022). Thus, the DT of a bridge
aided by the AR bridges the gap between digital and physical assets,
enhancing visualization, real-time data analysis, and decision-making,
especially in structural damage classification.
(FEA), which yielded the bridge health monitoring system designed as a
result of analysis parameters. It further helped to quantify the existing
damage associated with the bridge for which the monitoring devices
were installed on the bridge and bridge monitoring was carried out. The
other domain involved the bridge 3D scanning which adopted the Scanto-BIM approach to develop the BIM reality model of the bridge. After
this, both the domains were integrated to develop the DT model of the
bridge SHM system which is further integrated with the AR to perform
the AR-based DT of the bridge SHM system.
2.3. Knowledge gap
3.1. Component 1: Scan-to-BIM approach for BIM reality model
development
The state-of-the-art work underlines the challenges related to static
data collection and processing. The Scan-to-BIM approach emerges as a
transformative technique to automate the generation of the BIM reality
models that enable real-time detection of structural irregularities,
providing proactive data flows to bridge safety and reliability. On this
basis, a fully integrated bridge digital twin model will be capable of
automating processes of bridge monitoring, facilitating autonomous
decision-making in proactive maintenance, as aided by real-time data
collection from sensors and IoT technology. In addition, the AR imple­
mentation gives an insightful visualization of the developed DT model to
assist bridge inspectors. Considering this missing knowledge in an in­
tegrated and holistic approach to bridge model updating, this study
intends to develop the Immersive Bridge Digital Twin Platform (IBDTP)
to improve infrastructure management and monitoring and showcase its
potential for running future infrastructure projects.
To meet the research goals of Digital Twinning of the developed SHM
system, a BIM reality model of the bridge is needed. In this research, the
Scan-to-BIM approach is selected to achieve and automate this process.
The Scan-to-BIM is a sophisticated procedure that uses state-of-the-art
laser scanning technology that closely captures the details of a phys­
ical asset’s structural composition (Danklmaier, 2022). This
points-based dataset is subsequently transformed into a high-fidelity
digital 3D model, which is then subjected to further refinement, and
processing to develop a comprehensive BIM reality model of a bridge
(Meyer et al., 2022). The Scan-to-BIM process involves three steps
explained in Fig. 2.
3. Methodology
3.2. Component 2: Development of Digital Twin model of SHM system
using 3D game engines
Deeply rooted in the capabilities and adaptability of Digital Twins for
bridge health monitoring and their integration with AR, this research
contributes to the ongoing evolution of efficient and proactive infra­
structure management practices. The proposed research framework is
shown in Fig. 1, which is tested using a real-life concrete arch bridge.
The major focus of this framework is the real asset, which in our case
is the real-life bridge. Then two different domains were adopted in this
research on this bridge. One of them is the Finite Element Analysis
This research has used a novel gamification technique (using the 3D
game engine, UNITY 3D) to develop the DT model of the SHM system.
For this purpose, the BIM reality model of the bridge is used. The
development of this DT involves several steps sequenced in Fig. 3.
The first step of this process is the creation of a BIM-based sensory
model that deploys the smart sensors to the BIM reality model of the
bridge. This can be done on the UNITY platform. For this purpose, the
BIM reality model of the bridge is exported to FBX format to directly
Fig. 1. Holistic research framework for the IBDTP.
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Fig. 2. Steps of our BIM reality model updating process.
Fig. 3. Technological workflows to develop IBDTP.
import it to the 3D game engine. It is worth mentioning that the IFC
formats or.rvt files, can also be used using third-party 3D animation apps
but importing models with all the native properties makes them heavy,
subject to data loss, and inefficient thus requiring some extra manual
work to import all the design data. Therefore, direct import is preferred
and recommended for this case. After importing the BIM reality model,
the virtual replicas of all the sensors are developed at the exact locations
that are physically installed on the real bridge. All these replicas virtu­
ally represent the actual sensors giving birth to the sensory model of the
bridge.
Each sensor in this gaming environment is developed with several
functions, but the major focus of this research is the integration of these
virtual sensors with real sensors. For this purpose, canvases (game en­
gine components used to develop gaming tools) are used to mesh (a
digital tool that assigns functions to the subcomponents of canvas) the
virtual model which develops the regenerative algorithms and links
them with the sensory model. These meshes control the automation of
the generated virtual elements and directly communicate with the web
platform connected to the sensory model. To automate the connection
between the sensory model and the canvas, a customizable C# script is
developed in Visual Studio (VS) and then embedded in the canvas. This
automatically starts the communication between the sensory model and
the IoT web platform of the SHM system. Inside the canvas, a special
function is generated to give clickable features to the IBDTP. This
function is then embedded into the virtual replicas of the sensors
enabling communication with the actual sensors. This way the whole
virtual system is developed which communicates with the real SHM
system automatically and performs its functions automatically. This
study focused on the creation of DT for real-time monitoring,
management, and visualization of the SHM data and the scope of
automatic detection of bridge damage is not included in this study.
4. Practical illustration of the research – A case study of the
Panewicka Concrete Arch Bridge in Poland
4.1. Description of the bridge structure
The experimental phase of this research is planned over an arch
bridge in Poland. The bridge is a single-span concrete arch facility with a
two-directional traffic flow. The theoretical length of the bridge is
37.60 m, with a 0.30 m thick concrete deck slab. Two prestressed con­
crete girders are 9.72 m apart making the whole width of 13.68 m. Two
concrete arches on each side of the bridge are connected to the deck slab
through steel hangers. The detailed layout and span details are shown in
Fig. 4.
The choice of this bridge is critical for this research due to its
distinctive design and inclusion of certain structural elements like deck,
arches, and hangers. These elements make it an ideal subject for
research, promising valuable insights on the design of the SHM system,
installation of sensors, monitoring of the facility, and development of
IBDTP.
4.2. FE Analysis of the bridge for the design of the SHM system
Finite Element Analysis (FEA) analysis of the bridge is carried out to
calculate internal forces and displacement. These parameters helped to
design the SHM system of the bridge which provides a physical system
for the development of the DT model. SHM system is designed according
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Fig. 4. Layout, span, and sectional details of the bridge.
to the maximum valued location of these numerical parameters.
To carry out the static linear analysis, a linear elastic model of the
bridge is developed as a shell and linear element. The geometric char­
acteristics of the bridge deck are adopted in accordance with the ge­
ometry shown in Fig. 5. The modular geometry includes the bridge deck
slab of C60/75 concrete, held by the edge girders on both sides of the
deck slab. Similarly, diaphragms are also a part of bridge geometry that
helps to resist the lateral forces and transfer loads to the support. The
values of the torsional moment are calculated throughout the girder, for
ease of managing the geometry of the structure. Further, the bridge
structure includes concrete C60/75 arches on both sides of the bridge
deck. These arches hold the bridge deck through tied steel hangers. The
cross-sections of the hangers are reduced to a circular cross-section
having a diameter resulting from the total cross-sectional area of the
circular solid steel section. In the design, the calculation model takes the
standard parameters of the concrete elasticity modulus into account,
according to EC-2 (The European Union, 2004).
The loading of the bridge is applied as per (PN-EN, “PN-EN, 1991–2,
Fig. 5. FE model and used the geometry of the bridge.
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2007), including self-weight, superimposed dead load, uniform tem­
perature load ranging from − 29 ◦ c to 31 ◦ c (range for the area), and
vehicular load of 32 t (calculated for the heaviest vehicles passing over
the bridge). The developed FE model of the bridge is shown in Fig. 5.
several steps starting from setting up the laser scanner. In the case of the
subject bridge, 26 locations were chosen to fully scan the bridge.
Further, the survey points in the form of geodetic targets were also set up
and measured with a tachymeter for cloud calibration. The major pre­
caution used in this method is to keep the sequence of 3D scans with
certain overlapping areas. After each scan, the point clouds are config­
ured with the previously scanned area, and 3D overlapping is performed
on the mobile/tablet device. The scans acquired from the scanner were
then combined with UAV captures using the photogrammetry technique
and were further processed using Reality Capture software to process
and align the scanned data. It helped to create a coherent 3D model of
the bridge (Geosystems, 2023b) as shown in Fig. 7. This model is
available in various formats, with the e57 format being the most
commonly used one.
4.3. Installation of SHM system and field data measurement
The results of the Finite Element (FE) analysis presented in Section
4.5 are used to identify the prone zone needing the monitoring of nu­
merical parameters. For this monitoring, heavy-duty, robust IoT sensors
are used in this research, which are provided by the industrial partner
using real-world bridge projects. These sensors include the following
devices:
• FlatMesh 3 G Gateway: to communicate with the wireless sensors
and Sencieve web monitor,
• 1x FlatMesh Crack Sensor Node: for the measurement of longitudinal
displacement,
• 1x FlatMesh Tilt Meter Node: for the measurement of rotation angle
showing the bridge rotation in 3-axis,
• 1x FlatMesh Optical Displacement Sensor Node: for the measurement
of vertical displacement and rotation at the center of the bridge deck.
4.4.1. Updating the Scan-to-BIM reality model
Following the Scan-to-BIM methodology, the steps of constructing
and updating the BIM reality model of the bridge are described below.
Step 1: Scanning: The first step of the Scan-to-BIM procedure in­
volves the precise surveying of the bridge using a specialized laser
scanner as described in Section 4.4 (Xiong et al., 2023). The laser scan
emits laser beams and accurately captures the reflected signals, enabling
the accurate determination of distances and spatial coordinates of sur­
face points. Subsequently, these collected data are transformed into a
comprehensive 3D point cloud, representing the structural layout of the
bridge under examination. These point clouds are then aligned to a
common reference point, which is a fundamental prerequisite before
proceeding to create a BIM reality model (Tang et al., 2022). Thus,
bridge scanning was performed at 26 points and point clouds were
developed from this scanned data.
Step 2: Registration: The gathered scan data is then stored in a
point cloud format, serving as a foundation for subsequent trans­
formations into 3D and BIM models. Common file formats for this pur­
pose include.e57 and.rcp files supported by Autodesk, widely
recognized for their efficiency in exchanging laser scan data. Then,
precise alignment of the point cloud data is carried out by linking and
harmonizing data collected from various scanning sessions or different
In addition to the listed sensors, a web-based monitoring system
database was integrated to monitor and manage the SHM data. This web
platform provides an extensive data storage capacity, allowing for the
graphical representation of acquired data and facilitating the seamless
extraction of data in any file format. All these sensors and the layout of
the web monitor are shown in Fig. 6.
4.4. Component 1: Scan-to-BIM reality model development
This research has used the Scan-to-BIM approach (discussed in detail
in Section 3.1) to develop the BIM reality model of the bridge. For this
purpose, a high-accuracy automated portable 3D laser scanner
(Geosystems, 2023a) is used to capture bridge scans, including
High-Dynamic Range (HDR) images. 3D scanning of the bridge involved
Fig. 6. Wireless sensors and IoT-based web platform of the SHM of bridge.
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Fig. 7. The process of data acquisition and processing for generating a point cloud.
scanning devices (Wang et al., 2022). Registration can be accomplished
manually or with partial automation through specialized software.
Manual registration involves using visual cues within the data to
establish connections, while automated methods employ algorithms to
automatically compare and register data based on geometric or textual
features. In this research manual registration was performed as the
dataset was not big enough to go for the automated methods. This step
significantly laid the foundation of the resulting BIM reality model,
ensuring the accuracy and fidelity of the final BIM reality model
(Esfahani et al., 2021).
Step 3: Modelling: Following the exportation and precise registra­
tion of data, the process of creating digital 3D and BIM models
commenced. This phase is carried out using specialized computer-aided
design (CAD) software, such as Revit or Archicad. The major task in­
volves exporting the point clouds into modeling software i.e., Autodesk
Revit. For this purpose specialized software i.e., Autodesk Recap can be
used which can directly convert point cloud file (.e57) into the .rcp file
format, compatible with BIM modeling software. Then using the ground
Fig. 8. Scan-to-BIM process and BIM reality model generation.
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coordinates of the developed point clouds, different structural elements
are drawn (Xue et al., 2019). This way accuracy of the developed BIM
models can be ensured closer to reality (Bosché et al., 2015). The
developed BIM reality model using the Scan-to-BIM approach can be
known for its millimeter-accurate digital replication methodology and is
an important tool for efficiently creating DT of existing bridges. The
finalized BIM reality model developed using the Scan-to-BIM approach
is shown as the BIM reality model in Fig. 8.
Z directions accompanied by the temperature sensing device measuring
a frequency of 30 minutes. Further, the LD sensor measured the vertical
displacement of the deck accompanied by the measurement of rotation
angles at the center of the span and the temperature. All the measure­
ments with the LD sensor are recorded at a frequency of 5 minutes. The
complete details of these sensors are listed in Table 1.
Initially, 20 days of data from the installed system was selected for
processing. Following the data retrieval from the web platform, data
fusion was performed using DataFusion in Python library which allows
the data binding through DataFrame API against the data stored in the
CSV files, and runs it in a multi-threaded environment, which helps to
integrate the diverse sensor data, resulting in a more consistent, accu­
rate, and useful dataset (Krishnamurthi et al., 2020; Google cloud,
2023). Using this approach, the problem of different frequencies of the
data was resolved and a convenient dataset was developed for further
processing. This consolidated dataset was subsequently used to generate
a time series graph for graphical interpretation. For the listed parame­
ters in Table 1, temperature and displacement measurements showed
important trends detailed in Fig. 10.
All three sensors were used to monitor the temperature variations at
different locations of the bridge; thus, different temperature values can
be observed in Graph 1 of Fig. 10. Notably, the maximum values of the
temperature are recorded by the CM (crack meter) fluctuating between
11 ͦC to 34 ͦC indicating a temperature gradient exceeding 20 ͦC.
Following this, the tilt meter recorded the temperature from 19 ͦC to 24 ͦC
displaying relatively low temperature gradient whereas the laser
displacement sensor recorded the lowest gradient of the temperature.
This substantial temperature variation caused the sudden expansion and
contraction along the bridge which can somehow be compromised by
the concrete but not by the solid steel sections of the hangers, thus
buckling is observed in the hangers mostly exposed to the sun. This
measurement thus helped to identify the major bridge damage.
In addition to temperature measurement, displacement results
(Fig. 10b) also revealed some interesting results but did not affect the
bridge’s health. The major variation can be observed in the vertical
displacement measured by the laser displacement sensor which is
4.5. Component 2: Development of Digital Twin model of SHM system
using 3D game engines
4.5.1. Design of SHM system based on FE analysis and data processing
The FE analysis results helped to identify the maximum valued lo­
cations of longitudinal deflection, vertical deflection, and angular
deflection of the bridge. As per these results, the expansion joint at
support A is found to be the best-suited place for the installation of a
Crack Meter (CM) because the expansion joint offers the maximum
longitudinal moments. Further, the deflection diagram of the bridge is
carefully observed to check for the rotation angles. The difference in
rotation angles varies from constant negative to constant positive, with a
sharp change at a distance of 0.5 m from the left bearing of support A,
which can be observed as the potential location for the damage
(McGeown et al., 2021), so the Tilt Meter (TM) is proposed to be
installed at this location. For monitoring of the vertical displacement,
the deflected shape of the bridge deck was observed, proposing that the
Laser Displacement (LD) sensor should be installed at the center of the
bridge deck. Additionally, temperature and humidity monitoring were
also planned as the results of the FE analysis to show the critical de­
flections with a temperature gradient. Therefore, the monitoring devices
mentioned in Section 4.3, are proposed at the mentioned location. The
complete SHM plan along with these installed sensors is shown in Fig. 9.
These sensors include the CM having built-in temperature measure­
ment devices, so it measured longitudinal displacement of the bridge
deck along with the temperature at a frequency of 30 minutes. Similarly,
the TM measured the rotation angles of the bridge deflection in X, Y, and
Fig. 9. SHM system installed on the bridge.
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Table 1
Details of the installed sensors and their measurement parameters.
Measurement
Parameters
Displacement [mm]
ͦ
Temperature [C]
X-axis rotation [deg]
Y-axis rotation [deg]
Z-axis rotation [deg]
Crack Meter
(CM)
Tilt Meter
(TM)
Parameter
√
√
Measured Frequency [min]
30
30
​
​
​
​
​
​
Laser Displacement (LD)
Parameter
Measured Frequency [min]
​
​
30
30
30
30
√
√
√
√
Parameter
√
√
√
√
√
Measured Frequency [min]
5
5
5
5
5
Fig. 10. Graphical representation of fusion data recorded by the SHM system.
fluctuating between − 6 mm to 4 mm with an overall displacement of
10 mm, which is considerably lower than the Eurocode limits (Fawad
et al., 2019) thus validating the safer functionality of the bridge. Like­
wise, longitudinal displacement recorded by the crack meter remained
below 2 mm during the measurement period; thus, highlighting the safer
bridge health in this criterion too. In addition to the temperature and
displacement results, the rotation of the bridge deck did not reveal any
significant threat to bridge health, thus not discussed here. In this way,
the installed system provides a detailed overview of bridge health
ensuring the safe functionality of the bridge.
Based on the above-mentioned proposals and methodology defined
in Section 3.2 the Immersive Bridge Digital Twin Platform (IBDTP) is
developed. The dashboard of IBDTP with different features and the realtime data visualization of the SHM data are shown in Fig. 11.
Universal Window Platform (UWP). These computing platforms help to
customize the applications that can run on the Windows system and AR
platforms. DT model is used as the base 3D model of this application and
all functions of DT model are imported as assets to this application. The
real-time functionality of the application is developed using the virtual
buttons as a connection between the virtual and real world. To import
the actual reality, an AR development plugin is used (Vuforia) (Vuforia,
2023). This plugin enables its own camera to convert the DT model from
the gaming environment to AR. So, when the user switches to the game
mode, the AR application connects the virtual SHM system to the real
SHM system which can be visualized in the AR in the gaming
environment.
4.6.2. AR application deployment to AR headset
After the development of the AR application, an important phase is to
deploy this app successfully to the AR headset. For this purpose, the
target device in UWP is selected as the HoloLens (HL) because Microsoft
HoloLens 2nd generation is used in this research. Then the project is
built as an application that generates a Visual Studio (VS) solution (.sln)
file. This file is then used to open the project in VS and the system is
paired with the HL device over the same Wi-Fi network. This step re­
quires turning on the developer mode of the HL device and retrieving a
code. Adding the code in the VS project connects the system with the HL.
After the IP address of the HL is added to the VS project for debugging,
the application starts the deployment process. So, debugging of the
4.6. AR-based Digital Twinning of the SHM system
For the purpose of developing immersive and interactive decisionmaking processes, an immersive technology application is developed
that can be deployed to any AR headset for immersive operations.
4.6.1. AR application development
After successfully testing the DT model in the 3D game engine, the
development of the AR application is initiated by creating a UNITY
project supplemented with Mixed Reality Tool Kit (MRKT) and
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Computers in Industry 164 (2025) 104189
Fig. 11. Dashboard of DT model of bridge SHM system.
application is started in VS which automatically starts the deployment of
the application to the HL. After successful deployment, the DT model can
be visualized in the app menu of the HL, where it can be operated
independently.
Fig. 12. DT model of bridge SHM system in Augmented Reality.
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Computers in Industry 164 (2025) 104189
4.6.3. AR-based DT model testing
The application was tested on the real-world bridge case project.
Using the “Marker” option in the HL, the virtual model is placed over the
physical model of the bridge. Due to such overlapping information, the
initial discrepancies in the physical model are identified. In this project,
stakeholders were able to identify the buckling of hangers, which
showed diversion from the virtual model. When switching to the AR
application in the HL, the virtual sensors were checked against the
physical sensors. All the SHM devices were found to be attached to the
bridge model at the same location where actual sensors were installed.
In order to perform AR-based SHM, virtual sensors were implemented in
AR and overlapped with the physical sensor. The DT model allows the
communications between virtual and physical sensors. Clicking each
sensor connects the virtual sensor with the web platform of the SHM
system where the physical sensors continuously stored the bridge health
data in real-time. Once the virtual sensor was activated, it showed the
bridge health data in real-time and highlighted all the information
monitored by the physical sensor. In this way, bridge inspectors can
visualize the SHM data in a graphical format as shown in Fig. 12. As each
monitoring parameter is set with certain threshold values (as per the
Eurocode) and if any parameter exceeds the threshold value, it will be
immediately identified in the AR-based dashboard views and immediate
measures should be taken by the bridge inspectors.
This data can be interpreted as per user requirements, which can be
set in real-time. Data can also be stored in HL as a CSV file which can
further be transferred to any workstation. This way a complete visuali­
zation of SHM data can be performed onsite or remotely and data can be
shared with project partners over the Internet. The field demonstration
includes running the app in the HL and visualizing data with just a click
which popped up the sensor data in AR with the possibility of changing
certain parameters as per the inspection’s requirement. The outcomes of
the field demonstration and the implementation of the DT model using
the AR application in an AR headset can be seen in Fig. 12.
Thus the integration of both AR and DT technologies shows the po­
tential tools to facilitate the fusion of virtual and physical, which is a
growing trend in the construction industry. Since SHM of bridges needs
practical implementations of cutting-edge technologies like DT and AR,
so, the automated SHM system in the immersive AR environment kickstarts this boom. Further, it can be observed that AR-enhanced DT of
bridge SHM systems not only helps the real-time monitoring of bridge
health but can also step towards the digitization of the bridge industry
and across sector digital construction. This fusion of SHM, Scan-to-BIM,
DT, and AR has turned the conceptual designs into a mature BIM
framework that can push BIM implementation stages to a new level and
reciprocate its applications. This can bring more unified and integrated
applications of AR-based Digital Twinning into bridge engineering.
(Geosystems, 2023a), which is used to capture bridge scans, including
High-Dynamic Range (HDR) images. This way the BIM reality model is
developed (Danklmaier, 2022) using the manual registration approach
as the dataset was not big enough to go for the automated methods. After
the development of the BIM reality model, the virtual sensors of the
bridge SHM system are embedded in the BIM reality model to develop
the virtual sensory model which connects the virtual system with the
real system in real-time. This way the virtual system communicates with
the real SHM system and performs its functions automatically giving
birth to the DT of the bridge SHM system. The DT is then used to develop
the AR application which is further deployed to the AR headset to
perform bridge health monitoring in AR. For this purpose, an AR
development plugin is used (Vuforia) (Vuforia, 2023). This plugin en­
ables its own camera to convert the DT model from the gaming envi­
ronment to AR. The developed AR application integrates both AR and
DT technologies to showcase the potential to facilitate the fusion of
virtual and physical assets, which will perform real-time automated
SHM of the bridge in the immersive AR environment.
4.7. Discussions
The application of BIM in bridge health assessment and monitoring
has been up-graded to a new level of implementation due to its powerful
integration with IoT sensors, augmented reality, and virtual reality.
However, the integration of these technologies can be challenging for
bridge projects considering a lack of interoperable and systematic
platform approach. To address this challenge, this research integrates
Scan-to-BIM, IoT sensors, and AR tools to revamp the current SHM
system to enable real-time data management, automated data mining,
and onsite visualization of SHM data in an immersive environment. This
research develops the Immersive Bridge Digital Twin Platform (IBDTP)
that allows for improved data collection and model updating. The
wireless sensors installed on bridge assets were seamlessly integrated
with health monitoring and the real-time data was collected for 20 days
on the subject bridge structure (Panewicka Concrete Arch Bridge in
Poland) to enable infrastructure managers to identify the damages
efficiently. To provide an immersive and interactive environment and
enhance the experience of infrastructure managers, the model is trans­
formed into an AR application using the same platform and deployed to
the AR headsets.
5. Managerial implications and recommendations
The major challenge associated with this research is the limited
capability of AR headsets (HoloLens). To date, the available headsets
have a limited number of elements (< 10k) in the bridge management
models that can be visualized in AR. In some cases of bridge assets, the
number of elements modelled in the BIM software is much higher than
this limit. This issue can be addressed in the proposed IBDTP where the
bridge model is imported with the VR background, and therefore the
whole structure acts as a single unit and individual elements are not
counted in numbers to overcome this problem. Besides, there remains
the issue of using HoloLens in bright sunlight which causes visualization
problems during the daytime. To resolve this issue, it is recommended
that stakeholders carry out field experimentation either during overcast
weather conditions or close to sunset timings. The accuracy and reli­
ability of wireless sensors integrated with AR applications may also be
affected by environmental factors such as temperature, humidity, and
vibration. Therefore, further research is needed to overcome these
limitations and resolve interoperability issues among different model­
ling systems. Additional real-world case studies should be investigated
to validate the potential of the proposed IBDTP and demonstrate the
value of it. For example, the cost of implementing the AR-based SHM
system needs to be investigated through various types of case studies
with the proper implementation to provide practical implications on the
net-benefits of its adoption.
6. Conclusion
This research has implemented the proposed framework of an
Immersive Bridge Digital Twin Platform (IBDTP) of Structural Health
Monitoring (SHM) system on a real-life bridge structure. The study uses
the Finite Element (FE) analysis method to identify prone zones for
monitoring bridge damages (Svendsen, 2021; Fawad et al., 2022), which
helped to design the bridge monitoring system (Fawad et al., 2024). This
system involved heavy-duty IoT sensors including FlatMesh 3 G
Gateway, Crack Sensor Node, Tilt Meter Node, and Optical Displace­
ment Sensor Node used for monitoring longitudinal and vertical
displacement, rotation angels of the bridge deck, and monitoring of
bridge cracks. These IoT sensors help to connect the bridge monitoring
parameters with the web-based platform which is further linked with the
IBDTP (Fawad et al., 2023). This IoT-based web platform is linked with
the BIM reality model of the bridge, developed using the Scan-to-BIM
approach, to lay down the foundations for the development of IBDTP.
The Scan-to-BIM approach involves the 3D laser scanning of the bridge
using a high-accuracy automated portable 3D laser scanner
11
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M. Fawad et al.
The proposed IBDTP not only serves as a base framework for the
immersive Digital Twin of the bridge SHM system but also addresses the
limitations associated with traditional SHM methods, particularly con­
cerning data management and the visualization of three-dimensional
structural data. Moreover, it provides a comprehensive framework as
a base to guide future practices of digital twining of infrastructure to
enable proactive decision-making of infrastructure managers. Future
research work and model testing will be conducted to resolve interop­
erability issues among different modelling systems and additional realworld case studies will be investigated to validate the potential of the
proposed IBDTP, including a detailed comparison of the limitations and
constraints of IBDTP applications in different types of building and
infrastructure projects.
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Author statement and contributions
We declare that this manuscript is original, has not been published
before and is not currently being considered for publication elsewhere.
We confirm that the manuscript has been read and approved by all
named authors and that there are no other persons who satisfied the
criteria for authorship but are not listed. We further confirm that the
order of authors listed in the manuscript has been approved by all of us.
We understand that the Corresponding Author is the sole contact for the
Editorial process and is responsible for communicating with the other
authors about progress, submissions of revisions and final approval of
proofs.
CRediT authorship contribution statement
Marcin Jasinski: Writing – review & editing, Investigation,
Conceptualization. Piotr Lazinski: Writing – review & editing, Inves­
tigation, Conceptualization. Kalman Koris: Writing – review & editing,
Investigation, Conceptualization. Dawid Piotrowski: Writing – review
& editing, Investigation, Conceptualization. Qian Chen: Writing – re­
view & editing, Methodology, Conceptualization. Mateusz Uscilowski:
Writing – review & editing, Visualization, Investigation, Formal anal­
ysis, Conceptualization. Muhammad Fawad: Writing – review & edit­
ing, Writing – original draft, Visualization, Validation, Project
administration, Methodology, Investigation, Formal analysis, Data
curation, Conceptualization. Marek Salamak: Writing – review &
editing, Supervision, Resources, Project administration, Methodology,
Investigation, Conceptualization.
Declaration of Competing Interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
Data will be made available on request.
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