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The construction industry is significantly impacted by heavy construction equipment, including bulldozers, excavators, and vehicles. This equipment speeds up building, moves supplies, and builds infrastructure. Using heavy construction equipment correctly can boost productivity and shorten project timelines. Due to their complexity and scale, this equipment must be maintained and repaired. Poor maintenance and repair of heavy construction equipment can reduce performance, damage, and even cause accidents. Due to these problems, this study focuses on the design and development of a simulation training application to enhance the technical skills of workers in maintaining and repairing heavy construction equipment using virtual reality (VR) technology, the development of this application will be carried out using Unreal Engine 5 and thereafter tested and implemented at PT Menara Indonesia or M-Knows Consulting, Indonesia. At the end of this study, the design and development of a VR training simulation application for heavy equipment repair has been successfully completed. After testing the VR application and conducting user acceptance tests, it was concluded that the created VR application greatly assists M-Knows Consulting in training workers to perform maintenance and repair on heavy equipment, with a user acceptance rate of 84%. [ABSTRACT FROM AUTHOR]

The rapid advancement in technology has led to the creation of interactive media across various fields, including education, entertainment, advertising, film, gaming, and animation. However, interactive animations have not achieved the same level of popularity as interactive films and games, often due to their complex story structures, additional production steps, high costs, and the necessity for expertise in game engines to enable interactivity. This paper examines the use of artificial intelligence (AI) tools, particularly Convai within Unreal Engine, to establish a more efficient workflow and reduce production costs in interactive 3D animation. The study compares traditional manual production methods using Unreal Engine and ChatGPT with AI-enhanced workflows that incorporate Convai. The findings indicate that AI tools significantly reduce production time and simplify the creation of interactive features. However, Convai has limitations in flexibility and precision, particularly when it comes to customizing features and animations. While AI tools are beneficial for beginners and those with limited programming experience in Unreal Engine due to their userfriendly nature, manual workflows provide greater flexibility for complex interactions and customizations. The research concludes that AI has substantial potential to improve the production of interactive 3D animation, although further advancements are necessary to enhance support for character and animation customization. [ABSTRACT FROM AUTHOR]

Visualizing the traumatic past is a perennial challenge, particularly for historians of the slave trade. New innovations in digital technologies may offer a transformative framework to address this problem. Epic Games and its Unreal Engine, developed for use in industries such as film and video game production, animation, architectural visualization, and virtual reality simulations, offer one potential avenue to efficiently visualize a challenging past. Epic Games's MetaHuman Creator – high-fidelity digital humans, fully rigged and able to be quickly animated with little more than an iPhone – offer another related means of viscerally recreating the past in ways which offer new insights for researchers and new tools for educators. [ABSTRACT FROM AUTHOR]

This article explores the potential of the Unreal Engine as a tool for creating realistic 3D visualizations of large-scale areas (1000 km2 and more) using GIS datasets. Unlike small-scale visualizations, large-scale visualizations are rare and often not public, which presents significant problems since they present different challenges and require different approaches. This article presents several relevant scientific studies and projects that have successfully used game engines for similar purposes. This case study focuses on the computational techniques used in Unreal Engine for the 3D visualization of GIS data and the potential application of Unreal Engine in large-scale geo-visualizations. It explores the potential for using GIS data within a game engine, including plug-ins that provide additional functionality for working with GIS data, such as the Vitruvio plug-in to implement procedural modeling of buildings. The case study is applied to GIS datasets of the historical Vltava Valley covering an area of 1670 km2 to demonstrate the unique challenges of using Unreal Engine to create realistic visualizations of large-scale historical landscapes. The resulting visualizations are presented. The practical application of this research provides insights into the potential of the Unreal Engine as a tool for creating realistic 3D visualizations of large-scale historical areas. [ABSTRACT FROM AUTHOR]

ABSTRACTThis article demonstrates recent technological advancements enabling the creation of digital twins for expansive real-world terrains. Focusing on the archaeological site Uruk-Warka in southern Iraq the German Archaeological Institute deployed the Delta Quad Pro, a winged UAV equipped with vertical take-off and landing capabilities. This UAV captured 32,000 aerial images of the ancient city and its immediate environment. Each image was precisely geotagged using an integrated DGNSS receiver. Utilizing advanced 3D photogrammetry software, we synthesized these images into a single georeferenced model. The outcome was a detailed triangulated mesh, comprising of one billion triangles and 1024 8k-resolution texture files, representing a 40 square kilometers terrain. When rendered in a game-engine and applying the new technologies Nanite and Streaming Virtual Texture, this massive dataset can be visualized in real-time. The result is ‘Uruk-VR,’ a digital twin of the Uruk-Warka archaeological site, most of which has never been investigated. Basic tools have been implemented to annotate features and measure distances within the Uruk-VR. The methodologies showcased here are scalable for creating digital twins of diverse terrains. Uruk-VR's potential extends to research, education and conservation, exemplifying how game engines can seamlessly integrate vast and diverse geospatial data in 3D space.

The lighting system is one of the fundamental energy systems for buildings. In the traditional approach, designers simulate their predesign on computer-aided software to create a convenient lighting environment, in which users cannot feel the lighting effects and experience the lighting scenarios until the design setup stage.Recently, with the help of the BIM platform and 3D modeling, both design teams and users can evaluate and visually experiment with the project. Users can more easily grasp the details of the project through the 3D model. In this way, the design development process is carried out more efficiently by design teams and users. However, some BIM-based lighting simulation software can only provide static outputs without any interaction between the users and the environment.Further, real-time lighting design with dynamic results will make the design development process efficient, where users will visually feel daylight and artificial light, and experience lighting scenarios with various shading and light control options. This decreases the risk of redesign and revision, which is extremely time-consuming and reduces the efficiency of the lighting design. Therefore, this article proposes a lighting design approach on the integration of BIM and a game engine called Unreal Engine (UE4) for dynamic and user experience- based lighting design.The process flow involves creating a BIM model in Autodesk Revit and exporting it in IFC file format. The IFC file is imported into the Dialux EVO software. At the same time, the BIM Model is converted to the appropriate file format via the Datasmith converter that enables data transformation and is imported into UE4. Once the IES data of the luminaires are imported into UE4, the arrangement of lighting luminaries is done in the model. Illuminance level measurements are carried out with the help of HDR mode. For the verification of the calculation results, the lighting design in UE4 is manually transferred to DialuxEVO and the lighting calculation is carried out for validation of UE4 measurements. Subsequently, the effect of daylight on the illuminance level during sunrise and sunset hours is measured in UE4 for three different days predetermined throughout the year, and the measurements are recorded in a table.In the research, artificial lighting is utilized as a secondary source only when natural daylight is insufficient to achieve maximum daylight utilization. The daylight simulation was conducted under Clear Sky conditions, utilizing a historic building located in the Datça district of the Southern Aegean region of Turkey as a reference. The analysis focused on the sunrise and sunset hours, which are known to have relatively lower levels of available daylight, in order to maximize the use of natural daylight. Results show that between 20% and 24% improvements could be achieved by utilizing real-time evaluation technics.

This study explores how combining virtual reality (VR) and augmented reality (AR) with geographic information systems (GIS) revolutionizes data visualization. It traces the historical development of these technologies and highlights key milestones that paved the way for this study's objectives. While existing platforms like Esri's software and Google Earth VR show promise, they lack complete integration for immersive GIS visualization. This gap has led to the need for a dedicated workflow to integrate selected GIS data into a game engine for visualization purposes. This study primarily utilizes QGIS for data preparation and Unreal Engine for immersive visualization. QGIS handles data management, while Unreal Engine offers advanced rendering and interactivity for immersive experiences. To tackle the challenge of handling extensive GIS datasets, this study proposes a workflow involving tiling, digital elevation model generation, and transforming GeoTIFF data into 3D objects. Leveraging QGIS and Three.js streamlines the conversion process for integration into Unreal Engine. The resultant virtual reality application features distinct stations, enabling users to navigate, visualize, compare, and animate GIS data effectively. Each station caters to specific functionalities, ensuring a seamless and informative experience within the VR environment. This study also delves into augmented reality applications, adapting methodologies to address hardware limitations for smoother user experiences. By optimizing textures and implementing augmented reality functionalities through modules Swift, RealityKit, and ARKit, this study extends the immersive GIS experience to iOS devices. In conclusion, this research demonstrates the potential of integrating virtual reality, augmented reality, and GIS, pushing data visualization into new realms. The innovative workflows and applications developed serve as a testament to the evolving landscape of spatial data interpretation and engagement. [ABSTRACT FROM AUTHOR]

Although digital twins have been established in manufacturing for a long time, they are only more recently making their way into the urban environment and present a relatively new concept for the construction industry. The concept of a digital twin—a model of the physical environment that has a real-time two-way link between the physical and the digital, with the virtual model changing over time to reflect changes in the real world—lends itself well to the continually changing environment of a construction project. Predictive capabilities built into a twin also have great potential for construction planning—including in supply chain management and waste disposal as well as in the construction process itself. Underpinning this opportunity is location data, which model where something is happening and when and can be used to solve a wide range of problems. In particular, location (the power of where) can integrate diverse data sources and types into a single system, overcoming interoperability challenges that are known to be a barrier to twin implementation. This paper demonstrates the power of location-enabled digital twins in the context of a highway construction project, documenting and addressing data engineering tasks and functionality development to explore the potential of digital twins in the context of two case studies—health and safety and construction monitoring. We develop two demonstrators using data from an existing construction project (building on data and requirements from industry partner Skanska) to build twins that make use of the powers of 4D data presentation offered by the Unreal Gaming Engine and CesiumJS mapping, while software development expertise is sometimes available to construction firms, we specifically explore to what extent the no-code approach available within Unreal can be deployed in this context. Our findings provide evidence to construction companies as to the benefits of digital twins, as well as an understanding of the data engineering and technical skills required to achieve these benefits. The overall results demonstrate the potential for digital twins to unlock and democratise construction data, taking them beyond the niche use of experts and into the boardroom. [ABSTRACT FROM AUTHOR]

The seismic response detection and operational safety assessment of high-speed railway (HSR) bridges play a crucial role in ensuring HSR systems' operational safety and reliability. Smartphones have introduced intelligent inspection tools for structural health detection, becoming a new tool for intelligent structural inspection. Combining the public and smartphones is the key to public participation in structural health detection. This study utilizes smartphone-based structural seismic response inspection technology to investigate the framework of public participation in earthquake response inspection and assessment. This system comprises the Smart Bridge Brain (SBB), which integrates data from multiple sources and systems, an assigning mechanism for public participation inspection tasks, and smartphone-based HSR bridge structural seismic response inspection technology. At the same time, the Unreal Engine 5.0 software is used to create a mixed-reality virtual simulation experimental environment to validate the feasibility of this framework. The results indicate that the intelligent optimization of task allocation by the SBB successfully assigns detection tasks to each public participant. Public participants can promptly reach predefined damage structure detection targets and rapidly inspect bridge structural seismic response indicators using smartphones. In addition, this paper also conducts a comprehensive evaluation and analysis of the detection of the work efficiency index (WEI) within the system. Furthermore, optimization strategies for the efficient execution of detection tasks are proposed based on WEI variations influenced by different factors. The system framework is expected to enhance cluster-based HSR bridges' intelligent disaster prevention and mitigation capabilities. [ABSTRACT FROM AUTHOR]

The problems of information isolation, inefficiency, and paper-based data archiving in traditional highway survey and design methods are investigated in this paper. A novel digital sandbox platform framework was developed to promote the efficiency of route design, model data integration, and information sharing. Under the presented framework, an integrated application method for both the Building Information Modeling (BIM) and Geographic Information System (GIS) technologies was designed by using Unreal Engine technology. Firstly, a digital base model was established by integrating multi-disciplinary BIM model data and GIS three-dimensional (3D) multi-scale scene model data. On this basis, using Unreal Engine technology for visualization development, a digital sandbox platform with the data visualization, traffic organization simulation analysis, 3D spatial analysis, component information query, and scene switching functions was developed, which satisfies the 3D visualization and digitalization needs in the current highway planning and design. Additionally, the Analytic Hierarchy Process (AHP) was employed to analyze the impact of digital base model on the development and application of platform modules, including five crucial factors: data accuracy, data representation, multi-source data fusion, data management capability, and scene semantic representation. Finally, the research results indicate that the proposed digital sandbox platform framework provides users with a platform for integrated data management, information sharing, and 3D data visualization, while reducing design time by 30%, total design cost by 12%, and land occupancy rate by 10%. [ABSTRACT FROM AUTHOR]

Gamification techniques have been proven effective in various fields such as education and industry. In this paper, we introduce a novel approach that applies gamification techniques to the identification of road assets in Mobile Laser Scanning (MLS) data. Our method utilises three gamification techniques: avatar (vehicle), point cloud segmentation into levels, and scoring. We implemented these techniques in Unreal Engine and evaluated their performance using three real-world case studies. We also compared two ways of point cloud visualisation: mesh-based and point-based. Our results demonstrate that our gamification approach improves the handling and visualisation of point clouds when compared to other free software such as Cloud Compare. Specifically, the point-based visualisation method provides a more accurate representation of the road environment and the input point cloud and is easier to import into Unreal Engine. However, this method requires more computational resources for visualisation. On the other hand, level segmentation ensures a constant frame rate of 60 frames per second. Furthermore, our gamification approach enhances the experience of road asset identification, making it more enjoyable for the user. However, we acknowledge that the quality of the point cloud remains the primary factor affecting the accuracy of asset identification, regardless of the software used. Overall, our proposed gamification approach offers a promising solution for improving the identification of road assets in MLS data and has the potential to be applied to other fields beyond road asset identification. [ABSTRACT FROM AUTHOR]

В статье приведён обзор подходов к решению проблем создания цифровых двойников с использованием графических движков в задачах информационного моделирования. Приводятся результаты сравнения существующих ре­шений на основе движков Unreal Engine и Unity. Описан процесс подготовки исходных данных по комплексу строительных объектов и территории в целом с использованием программ Archicad и Blender, а также технологии Lidar для воссоздания и уточнения специфических объектов пространства. Выделены три этапа подготовки модели к внедрению в приложение, включающие импорт подготовленных моделей объектов, настройку окружения и визуальных эффектов с использованием Lumen и разработку функций внутри модели. Детализирован процесс создания динамического функционала и проработки компонентов модели, реализуемый с использованием возможностей игрового движка Unreal Engine. Сделаны вывод о пер­спективах применения Unreal Engine для решения архитектурных и инженерных задач.

As the world becomes more interconnected and globalized, the demand for efficient and reliable delivery systems is on the rise. To meet this demand, we propose DecentraliDrone, an innovative drone delivery system that utilizes advanced technology to transport goods autonomously and efficiently. Our system is fully decentralized, meaning that it operates without the need for human intervention and relies on a distributed network of drones and devices to function. One of the key features of DecentraliDrone is its autonomy. The system operates without the need for human intervention, enabling it to operate 24/7 and reducing the risk of accidents caused by human error. Additionally, our system is highly scalable, allowing it to adapt to changing demands and expand its delivery capabilities. There are several challenges that must be addressed in order to fully realize the potential of DecentraliDrone. These include developing robust communication protocols, ensuring the safety and security of the system, and addressing regulatory and legal issues related to autonomous delivery systems.

To improve the rapidity of path planning for drones in unknown environments, a new bio-inspired path planning method using E-DQN (event-based deep Q-network), referring to introducing event stream to reinforcement learning network, is proposed. Firstly, event data are collected through an airsim simulator for environmental perception, and an auto-encoder is presented to extract data features and generate event weights. Then, event weights are input into DQN (deep Q-network) to choose the action of the next step. Finally, simulation and verification experiments are conducted in a virtual obstacle environment built with an unreal engine and airsim. The experiment results show that the proposed algorithm is adaptable for drones to find the goal in unknown environments and can improve the rapidity of path planning compared with that of commonly used methods. [ABSTRACT FROM AUTHOR]

As the world becomes more interconnected and globalized, the demand for efficient and reliable delivery systems is on the rise. To meet this demand, we propose DecentraliDrone, an innovative drone delivery system that utilizes advanced technology to transport goods autonomously and efficiently. Our system is fully decentralized, meaning that it operates without the need for human intervention and relies on a distributed network of drones and devices to function. One of the key features of DecentraliDrone is its autonomy. The system operates without the need for human intervention, enabling it to operate 24/7 and reducing the risk of accidents caused by human error. Additionally, our system is highly scalable, allowing it to adapt to changing demands and expand its delivery capabilities. There are several challenges that must be addressed in order to fully realize the potential of DecentraliDrone. These include developing robust communication protocols, ensuring the safety and security of the system, and addressing regulatory and legal issues related to autonomous delivery systems. • The system ensures faster, accurate deliveries by eliminating human-operated constraints. • Utilizes Advanced Algorithms for Real-time Danger Detection and Collaborative Decision-making. • The system complies with regulations, integrates safety protocols, and prioritizes energy-efficient operations. [ABSTRACT FROM AUTHOR]

The use of simulators is widespread in driver behavioural research. The ability of driving simulators to achieve the high levels of behavioural fidelity desired by behavioural researchers is argued to be resultant of the physical fidelity of the simulator. Whilst attempts to maximise the physical fidelity of simulators have often been focused on the hardware capabilities of the simulator, the software of the simulator has been argued to be as important. This is because the software of a simulator controls the intelligence and the heterogeneity of the behaviours of the simulated vehicles, as well as the quality of the graphics of the simulation. Despite the importance of intelligent simulated agents, previous driving simulator studies have tended to simplify the behaviours of simulated vehicles and the scenarios that are presented to participants. This is particularly true of simulator studies investigating the decision-making of drivers at narrow passages, a relatively unregulated but hazardous situation in which two opposing vehicles must negotiate how to safely pass through a road narrowing, in which the interactive nature of the interaction has often been neglected. Following a review of the requirements for a representative narrow passage driving simulator, it is argued that co-simulation, an approach which combines multiple simulator types to create a global simulation, provides the best approach to creating intelligent simulated agents within an immersive environment for narrow passage behavioural research. As such, the development of a simulator for narrow passage behavioural research that combines SUMO and Unreal Engine is described. In particular, the development of a novel narrow passage behavioural model within SUMO that utilises previous behavioural findings is highlighted. To this end, it is argued that this approach facilitates higher levels of behavioural fidelity for narrow passage interaction studies and provides a framework for the investigation of other driver behaviours.

Rigorous testing of automotive sensors for accuracy and precision is essential before finalizing designs and proceeding with mass production. This testing process plays a crucial role in identifying these sensors’ potential shortcomings, which are essential components in the Advanced Driver Assistance Systems (ADAS). A virtual environment for sensor model simulation and testing provides a significant advantage in experimenting with the sensor in a vast array of realistic scenarios, which may be cost-prohibitive in physical testing. Thus, radar sensors, a commonly used sensor in ADAS, have been modeled using various methodologies in virtual environments. However, there is a need for a radar model that is both computationally efficient and accurate under different combinations of radar reflectivity, directivity, and signal-to-noise ratio (SNR). In this study, we propose a model of a Delphi-ESR 2.5 medium-range automotive radar in the Unreal Engine environment that leverages grid-based DBSCAN for point-cloud clustering and radar equations to calculate the SNR of grid points, facilitating object detection and tracking. The proposed model incorporates the reflectivity and directivity concept of Radar Cross-Section (RCS) to emulate the behavior of an actual radar. The sensor model exhibits superior accuracy and low computational requirement, with a mean error rate of 10.8% and an output rate of 8.7 Hz. This approach provides a reliable means of simulating and testing automotive radar sensors in a virtual environment, contributing to the further advancements of the ADAS systems.

This article explores the potential of the Unreal Engine as a tool for creating realistic 3D visualizations of large-scale areas (1000 km2 and more) using GIS datasets. Unlike small-scale visualizations, large-scale visualizations are rare and often not public, which presents significant problems since they present different challenges and require different approaches. This article presents several relevant scientific studies and projects that have successfully used game engines for similar purposes. This case study focuses on the computational techniques used in Unreal Engine for the 3D visualization of GIS data and the potential application of Unreal Engine in large-scale geo-visualizations. It explores the potential for using GIS data within a game engine, including plug-ins that provide additional functionality for working with GIS data, such as the Vitruvio plug-in to implement procedural modeling of buildings. The case study is applied to GIS datasets of the historical Vltava Valley covering an area of 1670 km2 to demonstrate the unique challenges of using Unreal Engine to create realistic visualizations of large-scale historical landscapes. The resulting visualizations are presented. The practical application of this research provides insights into the potential of the Unreal Engine as a tool for creating realistic 3D visualizations of large-scale historical areas.

Digital twins are increasingly gaining popularity as a method for simulating intricate natural and urban environments, with the precise segmentation of 3D objects playing an important role. This study focuses on developing a methodology for extracting buildings from textured 3D meshes, employing the PicassoNet-II semantic segmentation architecture. Additionally, we integrate Markov field-based contextual analysis for post-segmentation assessment and cluster analysis algorithms for building instantiation. Training a model to adapt to diverse datasets necessitates a substantial volume of annotated data, encompassing both real data from Quebec City, Canada, and simulated data from Evermotion and Unreal Engine. The experimental results indicate that incorporating simulated data improves segmentation accuracy, especially for under-represented features, and the DBSCAN algorithm proves effective in extracting isolated buildings. We further show that the model is highly sensible for the method of creating 3D meshes. [ABSTRACT FROM AUTHOR]

This article demonstrates recent technological advancements enabling the creation of digital twins for expansive real-world terrains. Focusing on the archaeological site Uruk-Warka in southern Iraq the German Archaeological Institute deployed the Delta Quad Pro, a winged UAV equipped with vertical take-off and landing capabilities. This UAV captured 32,000 aerial images of the ancient city and its immediate environment. Each image was precisely geotagged using an integrated DGNSS receiver. Utilizing advanced 3D photogrammetry software, we synthesized these images into a single georeferenced model. The outcome was a detailed triangulated mesh, comprising of one billion triangles and 1024 8k-resolution texture files, representing a 40 square kilometers terrain. When rendered in a game-engine and applying the new technologies Nanite and Streaming Virtual Texture, this massive dataset can be visualized in real-time. The result is 'Uruk-VR,' a digital twin of the Uruk-Warka archaeological site, most of which has never been investigated. Basic tools have been implemented to annotate features and measure distances within the Uruk-VR. The methodologies showcased here are scalable for creating digital twins of diverse terrains. Uruk-VR's potential extends to research, education and conservation, exemplifying how game engines can seamlessly integrate vast and diverse geospatial data in 3D space. [ABSTRACT FROM AUTHOR]

Copyright of Laser Technology is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Sciences Institute is the property of Lublin University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unmanned aerial vehicles (UAVs) have emerged as a highly efficient means of monitoring landslide-prone regions, given the growing concern for urban safety and the increasing occurrence of landslides. Designing optimal UAV flight routes is crucial for effective landslide monitoring. However, in real-world scenarios, the testing and validating of flight path planning algorithms incur high cost and safety concerns, making overall flight operations challenging. Therefore, this paper proposes the use of the Unreal Engine simulation framework to design UAV flight path planning specifically for landslide monitoring. It aims to validate the authenticity of the simulated flight paths and the correctness of the algorithms. Under the proposed simulation framework, we then test a novel flight path planning algorithm. The simulation results demonstrate that the model reconstruction obtained using the novel flight path algorithm exhibits more detailed textures, with a 3D model simulation accuracy ranging from 10 to 14 cm. Among them, the RMSE value of the novel flight route algorithm falls within the range of 10 to 11 cm, exhibiting a 2 to 3 cm improvement in accuracy compared to the traditional flight path algorithm. Additionally, it effectively reduces the flight duration by 9.3% under the same flight path compared to conventional methods. The results confirm that the simulation framework developed in this paper meets the requirements for landslide damage monitoring and validates the feasibility and correctness of the UAV flight path planning algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, watercraft and ship simulation is summarized, and the way that it can be extended through realistic physics is explored. A hydrodynamic, data-driven, immersive watercraft simulation experience is also introduced, using the Unreal Engine to visualize a Landing Craft Utility (LCU) operation and interaction with near-shore waves in virtual reality (VR). The VR application provides navigation scientists with a better understanding of how coastal waves impact landing operations and channel design. FUNWAVE data generated on the supercomputing resources at the U.S. Army Corps of Engineers (USACE) Engineering Research and Development Center (ERDC) are employed, and using these data, a graphical representation of the domain is created, including the vessel model and a customizable VR bridge to control the vessel within the virtual environment. Several dimension reduction methods are being devised to ensure that the FUNWAVE data can inform the model but keep the application running in real time at an acceptable frame rate for the VR headset. By importing millions of data points output from the FUNWAVE version 3.4 software into Unreal Engine, virtual vessels can be affected by physics-driven data. [ABSTRACT FROM AUTHOR]

The seismic response detection and operational safety assessment of high-speed railway (HSR) bridges play a crucial role in ensuring HSR systems’ operational safety and reliability. Smartphones have introduced intelligent inspection tools for structural health detection, becoming a new tool for intelligent structural inspection. Combining the public and smartphones is the key to public participation in structural health detection. This study utilizes smartphone-based structural seismic response inspection technology to investigate the framework of public participation in earthquake response inspection and assessment. This system comprises the Smart Bridge Brain (SBB), which integrates data from multiple sources and systems, an assigning mechanism for public participation inspection tasks, and smartphone-based HSR bridge structural seismic response inspection technology. At the same time, the Unreal Engine 5.0 software is used to create a mixed-reality virtual simulation experimental environment to validate the feasibility of this framework. The results indicate that the intelligent optimization of task allocation by the SBB successfully assigns detection tasks to each public participant. Public participants can promptly reach predefined damage structure detection targets and rapidly inspect bridge structural seismic response indicators using smartphones. In addition, this paper also conducts a comprehensive evaluation and analysis of the detection of the work efficiency index (WEI) within the system. Furthermore, optimization strategies for the efficient execution of detection tasks are proposed based on WEI variations influenced by different factors. The system framework is expected to enhance cluster-based HSR bridges’ intelligent disaster prevention and mitigation capabilities.

Although digital twins have been established in manufacturing for a long time, they are only more recently making their way into the urban environment and present a relatively new concept for the construction industry. The concept of a digital twin—a model of the physical environment that has a real-time two-way link between the physical and the digital, with the virtual model changing over time to reflect changes in the real world—lends itself well to the continually changing environment of a construction project. Predictive capabilities built into a twin also have great potential for construction planning—including in supply chain management and waste disposal as well as in the construction process itself. Underpinning this opportunity is location data, which model where something is happening and when and can be used to solve a wide range of problems. In particular, location (the power of where) can integrate diverse data sources and types into a single system, overcoming interoperability challenges that are known to be a barrier to twin implementation. This paper demonstrates the power of location-enabled digital twins in the context of a highway construction project, documenting and addressing data engineering tasks and functionality development to explore the potential of digital twins in the context of two case studies—health and safety and construction monitoring. We develop two demonstrators using data from an existing construction project (building on data and requirements from industry partner Skanska) to build twins that make use of the powers of 4D data presentation offered by the Unreal Gaming Engine and CesiumJS mapping, while software development expertise is sometimes available to construction firms, we specifically explore to what extent the no-code approach available within Unreal can be deployed in this context. Our findings provide evidence to construction companies as to the benefits of digital twins, as well as an understanding of the data engineering and technical skills required to achieve these benefits. The overall results demonstrate the potential for digital twins to unlock and democratise construction data, taking them beyond the niche use of experts and into the boardroom.

The problems of information isolation, inefficiency, and paper-based data archiving in traditional highway survey and design methods are investigated in this paper. A novel digital sandbox platform framework was developed to promote the efficiency of route design, model data integration, and information sharing. Under the presented framework, an integrated application method for both the Building Information Modeling (BIM) and Geographic Information System (GIS) technologies was designed by using Unreal Engine technology. Firstly, a digital base model was established by integrating multi-disciplinary BIM model data and GIS three-dimensional (3D) multi-scale scene model data. On this basis, using Unreal Engine technology for visualization development, a digital sandbox platform with the data visualization, traffic organization simulation analysis, 3D spatial analysis, component information query, and scene switching functions was developed, which satisfies the 3D visualization and digitalization needs in the current highway planning and design. Additionally, the Analytic Hierarchy Process (AHP) was employed to analyze the impact of digital base model on the development and application of platform modules, including five crucial factors: data accuracy, data representation, multi-source data fusion, data management capability, and scene semantic representation. Finally, the research results indicate that the proposed digital sandbox platform framework provides users with a platform for integrated data management, information sharing, and 3D data visualization, while reducing design time by 30%, total design cost by 12%, and land occupancy rate by 10%.

In this article, we explore how the recent proliferation of the virtual production stage with its promise of real-time visual effects has not only changed the working environment of the film studio, but also introduced an urgent need for new training and teaching approaches – not only for current and future film students, but also for educators and practitioners. This case study examines a research project involving students from Ravensbourne University London, UK, in collaboration with industry partner Mo-Sys Engineering, a leading manufacturer of virtual production solutions. The complex demands of new, multidisciplinary job roles in virtual production have created a skills shortage in the UK film and television industry. We illustrate the challenges of training not only film students but also educators for a continually changing workplace dominated by disruptive technologies and shifting skill sets.

Abstract Digital Twins (DTs) have attracted great attention in the energy sector. Game engines have been suggested to model DTs of their physical counterparts because they provide realistic graphics, lighting-, fluid- and physics engines that simulate the real world. However, the application of game engines to develop DTs for photovoltaics (PVs) has not yet been discussed in the literature. Therefore, this paper assesses the built-in game engine features' ability to support the DT development of PVs with Unreal Engine 5. This paper mainly focuses on visual representation because the surrounding environment significantly impacts PV deployment, and the existing software tools do not allow the study of the environmental factors at the early planning phase of a project’s lifecycle. Furthermore, this paper investigates the position of the sun, shadows and reflections from nearby objects that influence the PVs' power output, and if the built-in light engine can be used for planning the deployment of PVs. The result shows that in-game objects in the environment can be used to affect the simulated PV output estimate over a year. It also indicates that applying Unreal Engine 5 to model PV systems that rely on mirroring real-world behaviour is promising if accurate data is used in the modelling. Real data and mathematical PV models are necessary since Unreal Engine 5’s Lumen subsystem cannot provide realistic solar radiance on PVs for a given location on earth.

Abstract Digital twin technologies have become popular in wind energy for monitoring and what-if scenario investigation. However, developing a digital representation of the wind is challenging, especially due to the digital twin platform constraints. Game engines might be possible to solve this issue, especially since game engines have been used for product design, testing, prototyping, and also digital twins. Therefore, this study investigates the potential of developing a digital twin of wind power in the Unreal game engine. A case study of two types of wind turbines (Vestas V164-8 and Enercon E-126 7.580) and one location (Esbjerg, Denmark) is chosen for this study. The digital twin includes the environment with historical wind data and the visual representation of the wind turbine with a wind power production model and the estimated production in the given wind conditions of the area. The results show that game engines are viable for building entire digital twins where a realistic graphical user interface is required. Unreal Engine 5 provides the tools for modelling the landscape, surrounding water, and lighting. In addition, the Unreal Engine ecosystem provides vast amounts of content, such as 3D assets and game logic plugins, easing the digital twin development. The results prove that digital twins built in Unreal Engine 5 have great potential development of digital twins and user interfaces for communicating with a digital twin. The developed digital twin allows for further extension to benefit future digital twins utilizing wind turbines.

In this article, we explore how the recent proliferation of the virtual production stage with its promise of real-time visual effects has not only changed the working environment of the film studio, but also introduced an urgent need for new training and teaching approaches – not only for current and future film students, but also for educators and practitioners. This case study examines a research project involving students from Ravensbourne University London, UK, in collaboration with industry partner Mo-Sys Engineering, a leading manufacturer of virtual production solutions. The complex demands of new, multidisciplinary job roles in virtual production have created a skills shortage in the UK film and television industry. We illustrate the challenges of training not only film students but also educators for a continually changing workplace dominated by disruptive technologies and shifting skill sets. [ABSTRACT FROM AUTHOR]