Using Digital Twin Technology to Conduct Dynamic Simulation of Industry-Education Integration

Abstract

The high accident rate in the construction industry has a major impact on how well projects turn out. Despite substantial investments in safety planning and supervision, there has been a marked increase in the construction industry's accident rate compared to other sectors. Serious games based on VR have recently been used in the study, suggesting that workers are now more safety conscious. However, these situations need many resources to create and are not always realistic. Hence this paper, Digital Twin-based Construction Safety Training Framework (DT-CSTF) with Artificial Intelligence (AI), has been proposed to monitor employees' emotional, mental, and physical well-being in real-time. The report sheds light on the significance of DT technology and its function in Industry 5.0. Using the Unity game engine, the proposed DT-CSTF creates a virtual reality-based training environment (VRTE) prototype that incorporates BIM, construction timetables, and safety requirements. Following this, the suggested structure enables gathering user data about risks and providing tailored feedback. Automated virtual reality game training scenarios are created using data given by digital twins on project intent, project status, safety requirements, and history. Both improved digital twins and periodic construction safety monitoring are anticipated to reap the benefits of dynamic virtual reality training. The proposed management system offers effectiveness of VR-based security training, cost-benefit analysis, monitoring,employee behaviour, safety education values are obtained by the ratio of 96,90 %, 98,33 %, 99,25 %, 95,91 %, 98,66 % respectively

Full text

Uso de la tecnología de gemelos digitales para realizar una simulación dinámica
de la integración de la industria y la educación
Data and Metadata. 2024; 3:422
doi: 10.56294/dm2024422
ORIGINAL
Using Digital Twin Technology to Conduct Dynamic Simulation of Industry-Education
Integration
Anber Abraheem Shlash Mohammad1,2 , Badrea Al Oraini3 , Suleiman Ibrahim Shelash1,4 , Asokan
Vasudevan5 , Mohammad Faleh Ahmmad Hunitie6 , Jin Zhang7 
ABSTRACT
The high accident rate in the construction industry has a major impact on how well projects turn out.
Despite substantial investments in safety planning and supervision, there has been a marked increase in the
construction industry’s accident rate compared to other sectors. Serious games based on VR have recently
been used in the study, suggesting that workers are now more safety conscious. However, these situations
need many resources to create and are not always realistic. Hence this paper, Digital Twin-based Construction
Safety Training Framework (DT-CSTF) with Articial Intelligence (AI), has been proposed to monitor employees’
emotional, mental, and physical well-being in real-time. The report sheds light on the signicance of DT
technology and its function in Industry 5.0. Using the Unity game engine, the proposed DT-CSTF creates a
virtual reality-based training environment (VRTE) prototype that incorporates BIM, construction timetables,
and safety requirements. Following this, the suggested structure enables gathering user data about risks and
providing tailored feedback. Automated virtual reality game training scenarios are created using data given
by digital twins on project intent, project status, safety requirements, and history. Both improved digital
twins and periodic construction safety monitoring are anticipated to reap the benets of dynamic virtual
reality training. The proposed management system oers eectiveness of VR-based security training, cost-
benet analysis, monitoring,employee behaviour, safety education values are obtained by the ratio of 96,90
%, 98,33 %, 99,25 %, 95,91 %, 98,66 % respectively.
Keywords: Digital Twin (DT); Virtual Reality (VR); Workers Safety; Articial Intelligence (AI); Education.
RESUMEN
La alta tasa de accidentes en la industria de la construcción tiene un impacto importante en el resultado de
los proyectos. A pesar de importantes inversiones en planicación y supervisión de la seguridad, ha habido
un marcado aumento en la tasa de accidentes de la industria de la construcción en comparación con otros
sectores. Recientemente se han utilizado en el estudio juegos serios basados en realidad virtual, lo que sugiere
que los trabajadores ahora son más conscientes de la seguridad. Sin embargo, estas situaciones necesitan
muchos recursos para crearse y no siempre son realistas. Por lo tanto, se ha propuesto este documento,
Marco de capacitación en seguridad en la construcción basado en gemelos digitales (DT-CSTF) con inteligencia
articial (IA), para monitorear el bienestar emocional, mental y físico de los empleados en tiempo real.
© 2024; Los autores. Este es un artículo en acceso abierto, distribuido bajo los términos de una licencia Creative Commons (https://
creativecommons.org/licenses/by/4.0) que permite el uso, distribución y reproducción en cualquier medio siempre que la obra original
sea correctamente citada
1Research follower, INTI International University. 71800 Negeri Sembilan, Malaysia.
2Digital Marketing Department, Faculty of Administrative and Financial Sciences, Petra University. Jordan.
3Business Administration Department. Collage of Business and Economics, Qassim University. Qassim, Saudi Arabia.
4Department of Business Administration, Business School, Al al-Bayt University. Jordan.
5Faculty of Business and Communications, INTI International University. 71800 Negeri Sembilan, Malaysia.
6Department of Public Administration, School of Business, University of Jordan. Jordan.
7Faculty of Liberal Arts, Shinawatra University. 99 Moo 10, Bangtoey, Samkhok, Pathum Thani, Thailand.
Cite as: Shlash Mohammad AA, Al Oraini B, Shelash Mohammad SI, Vasudevan A, Ahmmad Hunitie MF, Zhang J. Using Digital Twin Technology
to Conduct Dynamic Simulation of Industry-Education Integration. Data and Metadata 2024 ;3:422. https://doi.org/10.56294/dm2024422
Submitted: 04-02-2024 Revised: 08-05-2024 Accepted: 10-07-2024 Published: 11-07-2024
Editor: Adrián Alejandro Vitón Castillo

https://doi.org/10.56294/dm2024422
El informe arroja luz sobre la importancia de la tecnología DT y su función en la Industria 5.0. Utilizando el
motor de juego Unity, el DT-CSTF propuesto crea un prototipo de entorno de capacitación basado en realidad
virtual (VRTE) que incorpora BIM, cronogramas de construcción y requisitos de seguridad. A continuación,
la estructura sugerida permite recopilar datos de los usuarios sobre los riesgos y proporcionar comentarios
personalizados. Los escenarios de capacitación de juegos de realidad virtual automatizados se crean utilizando
datos proporcionados por gemelos digitales sobre la intención del proyecto, el estado del proyecto, los
requisitos de seguridad y el historial. Se prevé que tanto los gemelos digitales mejorados como el monitoreo
periódico de la seguridad de la construcción aprovecharán los benecios de la capacitación dinámica en
realidad virtual. El sistema de gestión propuesto ofrece efectividad de la capacitación en seguridad basada
en realidad virtual, análisis de costo-benecio, monitoreo, comportamiento de los empleados y valores de
educación en seguridad que se obtienen en una proporción de 96,90 %, 98,33 %, 99,25 %, 95,91 %, 98,66 %
respectivamente.
Palabras clave: Gemelo Digital (DT); Realidad Virtual (VR); Seguridad de los Trabajadores; Inteligencia
Articial (IA); Educación.
INTRODUCTION
Despite being a vital component of developing the world’s infrastructure, the construction sector is still
beset by high accident rates that seriously impair project performance and worker safety.(1) A signicant amount
of money has been spent throughout history on safety planning and monitoring to reduce the probability that
these dangers would occur.(2) However, the industry has a higher accident rate than others.(3) The continuance
of this problem calls for novel safety measures beyond present ones. VR, serious games, and other tools have
emerged from new technology.(4) AI and DT in the DT-CSTF allow us to build more realistic training scenarios
and adjust them depending on real-time construction operations and prior safety data.(5) This data may drive
automatic virtual reality training scenarios to keep sta up to date on safety precautions. Continual learning
should reduce workplace accidents, making them safer and more ecient.(6) Industry stresses people-centered
solutions that include intelligent technology and human cooperation. The DT-CSTF platform oers continuous
safety monitoring and improvement, which is a tremendous advance for construction safety training.
(7) Subsequently the new DT-CSTF framework for construction site safety training is cutting-edge. Digital twins
and AI provide dynamic, realistic, and ecient training.(8) This breakthrough enhances VR-based methodologies
and establishes the framework for construction safety improvements, aligning with industry aims.(9) A higher
safety culture and fewer construction accidents are envisaged from this approach.
Flexible manufacturing systems using modern ICT in production automation are transforming manufacturing
processes and management.(10) Innovations form the Industry 5.0 production system paradigm. Smart Factories,
Cyber-Physical Systems (CPS), IoT, and Internet of Services make up Industry 5.0, a value chain organization
technology and idea.(11) The information-driven ow of workpieces machine-by-machine on a workshop oor
facilitated by real-time machine-MES communication is the I5.0 production organization.(12) System in which
natural and human-made systems (physical space) are closely linked with computer, communication, and
control systems is called CPS.(13) CPS may provide industrial machines autonomous control, self-awareness, and
self-management by linking the cyber and physical worlds.
Digital transformation of complex real-world systems is usually business process transformation in
enterprise architecture with the right foundation.(14) AI and big dataset-handling technologies revolutionize
digital transformation approaches, tools, and processes. Multidisciplinary and creative material that promotes
society’s well-being and sustainability is essential.(15) It requires support framework openness and cooperation
among all necessary social community players. A digital twin is a modern modeling technique for sustainable
transformation of complex, operational, real-world systems into a collaborative network of stakeholders. Due
to its complexity, such an undertaking requires abstracting and concealing all details until context-dependent
data is accessible during elicitation and analysis.
Contribution of this paper
• Create an Innovative Safety Education Method: this seek a complete DT-CSTF including VRTE,
construction timelines, safety rules, and BIM. This framework will be developed using Unity.
• To improve worker safety and health: AI will enable the DT-CSTF to monitor construction workers’
mental, physical, and emotional wellness in real time. There will get more personalized feedback and
customized training situations based on data, improving workplace safety and eciency.
• Compliance with Industry 5.0 standards is mandatory: DT-CSTF might align with sector 5.0, which
seeks to make building safer and more productive. It need solutions that prioritize people and promote
close collaboration between intelligent systems and humans to accomplish this aim.
Data and Metadata. 2024; 3:422 2

Related work
To promote talent focused on practical applications, the objective of this paper is to suggest novel
approaches for integrating education and industry. Hierarchical analysis and CNN methods were used to study
this integration’s advantages for corporations, educational institutions, and career opportunities. Some think
big data and ITIM systems may help schools better serve students and instructors. AI talent is assessed using
the IFA-HP technique, focusing on critical competencies. Machine learning predictions reduce the computing
burden of operating CPS in SAMPLE. Finally, this section discusses open-source Digital Twin system tools and
their potential and the necessity to integrate them.
Convolutional Neural Network(CNN)
A series of inventive development strategies for the integration of industry and education for businesses are
proposed in this document to cultivate more application-oriented talent that is required by the market. Since
long-term and sophisticated instruction is utilized to evaluate industry-education integration, the indicators
are challenging. Hierarchical analysis is used to create a reasonable and clear evaluation index framework,
and the CNN algorithm is used to calculate the weight value of each index to fully reect the impact of
industry-teaching integration on enterprises. The evaluation system shows the indicating that the mechanism
of industry-education integration promotes the transformation of several courses and helps the employment
issues. The worldwide training cost of new employees, which considerably cuts rm recruiting and training costs
by integrating industry and education. Thus, CNN in this paper can compute the weight value and conclude that
the unique industry-teaching integration method benets all three parties.
ITIM
Big data technology’s incorporation into production processes is becoming standard practice; thus, educational
institutions must adapt their pedagogical approaches to meet the demands of students whose lives are more
reliant on the digital economy. Business-school collaboration evaluation requires consistent criteria and data
visualization. The current teaching method lacks real software experience, overemphasizes theoretical notions,
and lacks problem-oriented statistical modeling and big data statistics training. Big data technologies should
be implemented and developed via industry-education collaboration. This paper analyses higher education
talent training models’ aws and recommends industrial education to ll them. An InterTechnology Information
Management (ITIM) system for excellent education is developed and implemented in the paper to bridge the
industry-education divide. The ITIM system evaluates education quality using a fuzzy algorithm and oers
intelligent functional modules including group administration, nancial management and process-to-process
communication. The talent training model’s experimental ndings have improved teaching eectiveness,
student learning, and theoretical-applied teaching quality by adding dynamic analysis of industrial education.
IFA-HP
The fast growth of AI technology raises job and talent training standards. Industry-education cooperation
helps alleviate the talent shortage posed by industrial demand. Thus, the analysis begins with industry-
education integration. It gather industry recruiting texts and process the AI post system’s needs using the
Latent Dirichlet Allocation (LDA) topic model and Word2Vec and K-means. Then contact analysts and alter
the metrics for education. Finally create a four-dimensional vocational ability grade assessment index system
that includes fundamental AI, database, network, algorithm and design, and research and practice skills. The
index weights are calculated using the Intuitionistic Fuzzy Analytic Hierarchy Process(IFA-HP), which eliminates
expert score uncertainty. The largest weight is given to algorithm and design competence, a crucial factor for AI
professional talent assessment. Assessments of industries emphasize practical second-level indicators including
team spirit, innovation ability, and communication ability, while education emphasizes knowledge and skills
such as programming, applied mathematics, data structures and algorithms.
SAMPLE
Digital twinning allows real-time optimization of CPScontrol and operations using data-driven simulations,
but computing loads are prohibitive. Sequential Allocation using Machine-learning Predictions as Light-weight
Estimates(SAMPLE), a method for sequential allocation utilizing machine-learning predictions as lightweight
estimates, addresses this computing barrier by applying machine learning models learned oine in a predictive
simulation learning context before a real-time decision. SAMPLE rigorously but exiblely blends machine
learning predictions with data from real-time digital twin execution and optimizes the digital twin simulation to
achieve computing eciency for CPS real-time decision-making. Numerical trials show that SAMPLE can make
real-time CPS control and operations choices better than machine learning or simulations.
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Open-source technologies
Using the idea of Digital Twins, this paper lays out the best open-source tools and communication technologies
for modeling manufacturing processes. Many open-source tools and protocols have been created to generate
virtual production system models in recent years. The contributors explain how the Digital Twin idea has
evolved into a fundamental technology for Industry 5.0 automation and control. The organized examination of
relevant open-source software for dierent stages and duties of constructing the Digital Twin system showed
that the available solutions address all elements. Due to its dispersion, specialization, and lack of integration,
this software is seldom used for DT. Thus, integration needs demand extra work to develop full-edged Digital
Twin models using open-source technologies.
Several suggestions for enhancing the combination of business and education are oered in this paper.
This integration’s talent development and employment advantages may be assessed using hierarchical CNN
analysis. It suggests employing big data technologies in schools via the ITIM system to boost learning. The IFA-HP
method evaluates AI talent using key competences. The SAMPLE technique uses machine learning predictions to
improve CPS control. Digital Twin systems’ open-source tools and how to integrate them for Industry 5.0 control
and automation are considered as well.
METHOD
High accident rates have a negative impact on project results in the construction business, even if there
have been signicant expenditures in safety measures. No amount of conventional safety planning or training
has been able to completely eliminate these dangers. There is hope that new technology, such VR, might raise
workers’ awareness of the need of safety measures. The problem is that these VR solutions aren’t realistic
and usually need a lot of resources. This framework strives to combine buildings information modeling (BIM),
building schedules, and safety standards into a dynamic, fully immersive educational atmosphere using the
Unity game engine.
Figure 1. A digital twin frame for use in the building industry
The application of digital twins in a variety of industries, including transportation, electronics, healthcare,
smart cities, 3D warehouses, space communication networks, and more. It adds signicantly to the growing list
of uses for Digital Twin technology. The building sector is likewise making use of the Digital Twin theory. This
article presents a Digital Twin architecture for the construction industry is shown in gure 1. The framework
creates a virtual building model based on four factors: rules, behaviour, geometry, and physics, all of which are
Data and Metadata. 2024; 3:422 4

based on the typical physical building site. The data from the real-life building site and the digital model are
simultaneously uploaded to the cloud, creating a platform for twin data. The twin data platform sorts the data
using the human, machine, material, technique, and environmental factors. The framework may accomplish
several services to the algorithms that drive it, including cost schedule analysis, quality control, safety risk
prediction, decision trees, BP neural network models, support vector machines, and deep belief networks. This
framework has lower construction-related risks and increase the detail level of the process. The equation (1)
explains the cost benet:
The dynamic reaction of the cost benet system is denoted by the provided equation 1 n0, which includes
coecients relating to system sensitivities and damping, denoted as e2 and d0, respectively. The system losses
and outside inputs are represented by l0and g0, respectively. Applying this to DT-CSTF, might stand for the
current data collected from the site, including the emotional, mental, and physical conditions of the workers,
u for dierent risk factors, and v0 for the related adaptations to the simulated environment.
The real-world environment’s base reaction to user input u might be represented by the expression 2∀0×∝0
e0(u) presumably representing factors for equation 2. The error correction that takes into account real-time
data feedback is denoted by v0(u), while more sophisticated interaction model that incorporates multiple data
points is represented by v2m0(u). The ecacy of safety training, as measured by the data (g (h))/n0 normalized
by the dynamic system response. The equation (3) explains the safety education:
The augmented reality’s safety education environment’s time-dependent reaction is probably depicted by
the equation 3 as t^2×v0(t). Here t represents time and v0(t) represents the state of the system at that particular
instant. One possible interpretation of t2x0 t0Z0 (t) is a scaling factor that is associated with particular training
parameters r0
2 and t0. The eect of these parameters on the learning setting over time is represented by (lo(p))/t0 .
Figure 2. Virtual replica with the purpose of enhancing construction safety Interplay
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Using the most recent data from the DTCS, the blue-represented dynamic virtual reality module updates VR
games and conducts periodic worker training. The smart VR setting may provide scenario-specic results for
each occurrence by inputting the type of accident and the location into the model. The location and nature
of the danger will determine this action. When a game is played, this scenario will automatically generate
colliders and data collecting capabilities. The digital twin and the workforce both gain from this. In this way,
both the worker and the digital twin get training that may be applied in conjunction with other preventative
measures. This information to use when evaluating a plan’s Safety Key Performance Indicators (SKPI). Figure 2
shows the digital twin’s concept diagram for building security and component interaction. The virtual reality
training module that is always being updated is shown in blue. Accidents are happening at a high rate in the
construction business, which is having a major impact on how well projects are done. VR simulations provide a
more engaging and participatory learning environment for players when compared to traditional safety training
techniques. Unfortunately, most current virtual reality games’ training settings don’t provide the realistic
surroundings or complicated activities needed for real building.
The equation 4 is denoted by ∝0, where Changes in safety circumstances or worker behavior on the building
site may be associated with the variables ∂1z2. The system is subject to external inuences and probabilistic
components through f(-rst)+ √(1-∁r), where it represents a factor that may decay exponentially due to risk ωv or
learning decay represents adjustments depending on real-time feedback t(u-1).
In the equation 5, (τ2v(u,ut))/( δr2) represents the time-dependent reaction of the online training system
scaled by safety-related parameters n (ut). The equation d2(vr) reects the changing response of the system
depending on user input (γ2(k1,k2))/εu. The general performance measure or result depending on user input and
previous data is probably represented by g(u,uh).
The exponential connection among the training parameter l and the user variable uv
, which leads to the
constant k1, is represented by the equation 6. The total eect of the squared learning parameter l2 and user
input ut on the whole training environment is shown by the integral vq across the volume Ut.
Using the built-in Unity Game Engine, the AI Module ows into a virtual reality training environment that
incorporates construction schedules and BIM. To make sure the training situations are reective of real-world
restrictions and requirements, this environment includes Safety Requirements. Users can then participate
in virtual building tasks through the creation of automatic VR game learning scenarios. Scenario Creation,
Gathering Data on Risks and Reactions from Users, and a Customized Feedback Mechanism for Improvements
and Insights are All Made Easier with These Scenarios. Lastly, the system is designed to facilitate Periodic
Building Safety Monitoring, which includes collecting data continuously and making training modications on
the y. This guarantees that safety training is always up-to-date and relevant, which improves site safety
overall and addresses new threats as it arise. The equation (7) explains the eectiveness of Virtual Reality-
Based Security Training analysis:
The impact of user input ut on parameters for training l and n (ut) are shown by the equations 7. The initial
states of the training environment are represented by the variables n0 (1+ ∇p(su)) and l0 (1+ ∇q(uf)), which are
modied by gradients respectively.
Figure 3 shows the overall structure of a construction security education program that aims to improve safety
procedures by utilizing cutting-edge technology. Foundational data, such as Project Intention and Status Data,
Safety Criteria Data, and Historical Data, are rst collected by the system. This data provides crucial context
for the succeeding operations. The AI Module is the brains of the operation; it generates feedback, assesses
Data and Metadata. 2024; 3:422 6

risks, and keeps tabs on users’ mental, emotional, and physical health in real time. Ongoing supervision and
preventative risk management are guaranteed by this module.
Figure 3. Enhanced System for Construction Safety Education
To illustrate the impact of changes in user input on the training environment, the equation 8 ∇l(ut) shows
the change in gradient of customer input. It is probable that the function f(-βl+vt) modies the eect of user
input depending on parameters like the importance 1+st of the training component and user participation (1+
vj sin(αt×vt).
The proposed method for managing the safety risks associated with raising prefabricated buildings is
depicted in gure 4. To link the hoisting virtual model to the hoisting construction site, the framework employs
the Internet of Things (IoT). An association between virtual and actual mapping is completed by constructing
a two-way data synchronization channel connecting the physical and virtual spaces, based on data transfer
protocols with low latency, high stability, and speed. A prefabricated house hoisting risk couplings study is
carried out using the complex network based on the data mining results obtained from the Apriori algorithm,
which is utilized to identify the principles of association among dierent threat systems or components. The
BIM model is used to nish the virtual space modeling process.
The digital twin architecture, which oers features like decision-making and early warning, relies on the BIM
model to visualize the data service platform. In that manner, both the digital model and the physical location
may communicate and share information in real time. Prefabricated building hoisting safety risk coupling model
construction is the main topic here. One way to study the coupling impact of safety risk variables is to look at
the model’s multi-source data and see how the virtual and actual interact with each other. Lifting danger is
dened along ve dimensions in the data provided by the connections model of the assembly.
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Figure 4. Standardization of prefabrication building hoisting safety risk management
A collective parameter impacting the simulated learning surroundings, the equation 9, ∀n zv probably
indicates the total inuence of various factors on the training system. It is probable that ∝p(1+sp) represents
a scaling factor that is modied by safety-related factors. Integrating a deviation element βn modied by
protection thresholds (δ/φp).
It is probable that the equation 10, z0 is a baseline parameter that impacts virtual instruction circumstances,
showing its early impact on the learning system. The squared modulator function (f) is likely to reect the
signicance of user involvement (u) controlled by further variables ((ef(b))/dw) as suggested by the phrase
(f2×v(u))/(eu2). An external condition (b) and potentially additional variables are modied by the sensitivity of
the training system (l0) and the eect of these conditions on the setting for training (∀(l)(ur)) in the expression g(u).
Equation 11 denotes the analysis of performance e(B,C) shows the size of the dierence between the states
or attributes of two vectors (B-C) by expressing them as the Euclidean distance that exists between them.
Within the framework of the DT-CSTF, this might stand for the discrepancy between the building’s safety
training environment’s ideal or intended condition (B) and its actual state (C).
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The analysis of monitor employees emotional is denoted in the equation 12 e1(ut). The external stimuli βe0.
α∃,(uf), and user feedback e1 (uk)impact the equations that reect their emotional state at time (e1(uf))/q2.
The agents involved face regular, unexpected obstacles on a building site, which adds to the site’s intrinsic
complexity. This is a major contributor to the high accident rates that aect even seasoned employees. By
simulating real-world conditions as much as possible during training, accidents can be drastically decreased.
The workers are given training situations in the dynamic virtual reality games that are built from the digital twin
of their actual building site. As tasks are completed, the training situations are revised accordingly. Workers
are able to execute their jobs, make errors, and gain experience without worrying about harming themselves.
In Figure 5, see the overall plan for creating and implementing the VR training games that are always evolving.
Historical data, safety rules, and an object library are just a few of the digital copies of datasets used to build
the initial set of training situations. The data coming in from the digital twin is then used to update the training
settings on a regular basis. Creating realistic safety training situations calls for precise geometric data, well
marked danger zones, and interactive items. From the project Intent Data, the rst geometry for the training
environment is generated using the as-designed parametric models alongside instructional activities according
to the as-planned method.
The analysis of safety requirements is determined in the equation 13 for the feedback (e1(uf))/q2, modied
by a factor dp
3, is represented. In addition, parameters like risk deviation (∇l), user reactions (ur), and safety
limits (s2
p) impact safety-related factors. The equation (14) is explains the employee behaviour:
Figure 5. A virtual reality (VR) training framework for adaptive building security
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The safety parameter δv(yz) is denoted by elements on employee behavior e2(fe) and the surrounding
environment Df1
f2, and it is connected to a specic component of safety planning. The adjustment factor is
dependent on the ecacy of protection measures (fe) and the variations between two safety variables (Df1
f2).
Furthermore, 2w/s aected by both external circumstances and the ecacy of security preparation which
adds to the thorough examination of safety planning in the framework. The equation (15) explains monitoring
the Well-being and Security of Employees:
Construction safety monitoring is denoted by factors including security norms (∀D) and surroundings re,
which are represented by the equation 15. An adjustment factor that takes into account the eectiveness of
safety measures (f1), the impact of user engagement ke, the square of a safety-related parameter w2, and the
emotional state of employees (e1) is represented.
A new framework called DT-CSTF is being considered to improve construction security instruction by utilizing
the power of AI and DT. Data from twins, including project goals, progress, safety standards, and past data, are
used to automatically create virtual reality training situations. Regular revisions and immersive simulations are
part of this dynamic approach’s plan to boost building security surveillance and the ecacy of safety training
generally.
RESULT AND DISCUSSION
Analysis suggests that tailored feedback may drastically reduce accident rates. Consequently, a more
eective safety education system may be created by combining tailored feedback with virtual reality safety
training. This system has the ability to raise workplace safety and minimize occurrences.
Dataset Description
An extensive overview of a wide and comprehensive dataset, precisely produced by the sophisticated
language model is given in this paper.(21) The goal of this comprehensive dataset is to provide a starting point
for investigations into many dierent areas, such as renewable energy, AI, worldwide health trends, and many
more. Subjects Included in the Dataset: A Wide Range of Topics, From Renewable Energy to AI to Global Health
to Digital Media to Space Exploration to Genetic Research to Urbanization and Beyond. Each dataset item has
a model-generated, in-depth answer that covers several facets of the provided subject.
Figure 6. Eectiveness of Virtual Reality-Based Security Training analysis
Workers’ involvement and memorization of safety requirements are enhanced in VR-based safety training
when they get tailored feedback. Quick and relevant feedback is given to workers, which aids in reinforcing
learning and discourages bad behaviours is described in gure 6 and equation (7). This is achieved by tailoring
Data and Metadata. 2024; 3:422 10

training scenarios to meet the needs of each individual and promoting the use of immediate responses. Workers
are more inclined to fully embrace safety practices when they are given the opportunity to understand the
unique safety requirements of their jobs via this personalized approach. Several investigations have shown
that by tailoring feedback to each worker’s unique set of circumstances, the chance of accidents may be
signicantly reduced. In addition, when employees get tailored feedback, they are prone to stay engaged and
motivated throughout training sessions since they will feel that their specic growth is being acknowledged and
guided. Incorporating personalized feedback into VR safety training might lead to a more ecient and eective
safety education system, which in turn could bring about a rise in workplace safety and a fall in incidents. The
analysis of eectiveness is obtained by the ratio of 96,90 % in the proposed method.
Figure 7. Analysis of cost-benet
The DT-CSTF, does need a number of upfront costs to be covered with the help of equation (1). Expensesinclude
things such as sta training, system integration and technology acquisition. The benets is described in gure 7
in the long run, however will more than cover the costs of the upfront cost. Reducing the number of accidents
means less money spent on healthcare and compensation and making workplaces safer means more output
from employees since they aren’t out of commission as often. Reduced need for and associated costs with
frequent retraining sessions is one benet of DT-individualized CSTF’s monitoring and training capabilities,
which contribute to the continuous improvement of safety standards. Although there is a signicant nancial
investment required, the overall improvements in safety and operational eciency achieved by DT-CSTF
provide a compelling return on investment, making it a cost-eective option for the construction industry. In
this proposed method the cost benet analysis ratio is increased by 98,33 %.
Figure 8. Analysis of monitoring on the Well-being and Security of Employees
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https://doi.org/10.56294/dm2024422
Improving safety outcomes for construction workers may be achieved in gure 8 and equation (15) via the
use of DT and AI technology to monitor their emotional, mental, and physical health in real-time. Because it
continuously measures pulse, blood pressure and levels of stress and fatigue, the device may identify potential
health issues before they lead to accidents. Workers will remain in the best possible condition attributable to
this proactive methods, which enables the implementation of prompt interventions through enforced breaks
or job relocation. In addition to allowing for the continual updating of training programs, real-time data
provides substantial insights into the ecacy of safety legislation and worker behaviors. This, in turn, creates
a healthier and more productive work environment. In general, DT-CSTF’s integration of real-time monitoring
contributes to a more health-conscious work environment, which in turn decreases the occurrence of incidents
and enhances the overall welfare of employees. Compared to the existing method the analysis of monitoring on
the well- being and security of employees in the proposed method is obtained by 99,25 %.
Figure 9. Analysis of Managing Employee behaviour
Working within the framework of the DT-CSTF, the integration of AI and DT technologies has a substantial
impact on the well-being of employee is shown in gure 9 and equation (14). Virtual training environments
allow workers to obtain experience and practice in a controlled setting by mimicking real-world scenarios.
This gives employees the chance to learn how to handle potentially harmful circumstances which is shown in
gure. According to this concrete strategy, they are more prepared and condent in their abilities to handle
real challenges on-site. The framework’s real-time monitoring features also allow for continuous evaluations
of employees’ mental and physical well-being, guaranteeing that they are healthy enough to do their jobs.
Personalized feedback and exible training programs provide extra support for individual needs, promoting a
comprehensive safety strategy. Taking a holistic view of the problem helps to reduce accident rates and fosters
a positive work environment where employees’ safety and well-being are valued. Therefore results in higher
levels of contentment with one’s work and total output. The analysis of managing employee behaviour ratio is
95,91 % in the proposed method.
The incorporation of VR-based safety training represents a signicant advancement in the eld of construction
safety education is explain in the gure 10 and equation 3. The development of comprehensive and realistic
recreations of construction sites has allowed employees to engage in practical training without exposing
themselves to the risks associated with working in a real-world setting. Standard training techniques have a
hard time recreating real-world emergencies, but this method allows for the practice of safety procedures in a
range of conditions. Worker retention is enhanced by using VR technology since it increases the possibility that
workers will remember and apply what they have seen personally. Not only does this approach improve workers’
safety capacities, it additionally also fosters a culture of continuous learning and growth inside the company.
Consequently, a powerful technique that can be used to increase construction site safety and decrease accident
rates is the integration of VR into safety training programs. The analysis of safety education is obtained by the
ratio of 98,66 % in the proposed method.
Safety training in the construction industry may be greatly improved with the use of virtual reality by
creating realistic training scenarios and providing personalized feedback. There is a decrease in accident rates
because the tailored approach improves worker involvement and retention of safety requirements. Reduced
Data and Metadata. 2024; 3:422 12

healthcare expenses, fewer need for retraining and increased production are just a few of the long-term
benets that outweigh the initial expenditures. Utilizing DT and AI technology to monitor workers’ health in
real-time allows for preventive steps, which further enhance safety outcomes. A safer and more productive
workplace is the inevitable consequence of this all-encompassing plan’s execution since employees will be
better equipped, healthier and more motivated. To enhance safety and reduce incident frequency, VR may be
included into training programs.
Figure 10. Analysis of Safety Education
CONCLUSIONS
The construction industry continues to confront safety concerns that necessitate innovative approaches
that surpass conventional methods. Using modern technology to provide a realistic and ever-changing training
environment, the proposed DT-CSTF with Articial Intelligence oers a groundbreaking approach. By integrating
Building Information Modeling (BIM), construction schedules and safety standards into a VRTE using the Unity game
engine, DT-CSTF may increase the immersion and eciency of safety training. By combining real-time worker
health monitoring with adaptive training situations, it can guarantee that each worker receives customized
and pertinent safety teaching. This framework aligns with the human-centric approach of sector and aims to
reduce accidents in the construction sector while strengthening safety culture. It does this by addressing the
limitations of existing VR-based techniques.
Future work will primarily concentrate on rening the DT-CSTF to increase both its accuracy and the user
experience. Incorporating more sophisticated AI algorithms to enhance real-time monitoring of workers’
mental and physical states will be prioritized. Adding additional diversity to the virtual reality situations with
respect to building operations and related dangers is another important step toward ensuring comprehensive
training coverage. Also will conduct eld trials to see how well the framework works in actual settings. The
results of these tests will be valuable for making incremental changes. In an attempt to enhance the system’s
predictive capabilities, there will be an eort to establish partnerships with construction businesses to gather
a considerable quantity of data. The eventual goal is to have the DT-CSTF become a standard training tool for
the construction industry, which will lead to major improvements in worker safety and protection.
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FINANCING
The authors did not receive nancing for the development of this research.
Data and Metadata. 2024; 3:422 14

CONFLICT OF INTEREST
The authors declare that there is no conict of interest.
AUTHOR CONTRIBUTION
Conceptualization: Anber Abraheem Shlash Mohammad, Badrea Al Oraini.
Methodology: Suleiman Ibrahim Shelash Mohammad, Asokan Vasudevan.
Investigation: Mohammad Faleh Ahmmad Hunitie, Badrea Al Oraini, Suleiman Ibrahim Shelash Mohammad.
Data Curation: Anber Abraheem Shlash Mohammad, Badrea Al Oraini, Mohammad Faleh Ahmmad Hunitie.
Writing - Original Draft: Mohammad Faleh Ahmmad Hunitie, Suleiman Ibrahim Shelash Mohammad.
Writing - Review and Editing: Asokan Vasudevan, Jin Zhang.
Supervision: Jin Zhang.
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