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This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure. [ABSTRACT FROM AUTHOR]

Despite the pressure of carbon emissions and clean energy, coal remains the economic backbone of many developing countries due to its abundant resources and widespread distribution. The stable supply of coal is also vital for the global economy and remains irreplaceable in the future global energy structure. China has been a major contributor to annual coal output, accounting for nearly 50% worldwide since 2014. However, despite implementing intelligent coal mining technology, China's coal mining industry still employs over 1.5 million underground miners, posing significant safety risks associated with underground mining operations. Therefore, the introduction of coal mining robots in underground mines is an urgently needed scientific and technological solution for upgrading China's and even the world's coal energy industry. The working face needs a shearer, hydraulic support, a scraper conveyor, and other equipment for coordination. The deep integration of intelligent technology with factors such as "humans, machines, the environment, and management" in the workplace is the core content of intelligent coal mines. This paper puts forward an advanced framework for robot technology systems in coal mining, including single robots, robotized equipment, robot crowds, and unmanned systems. The framework clarifies the common key technologies of coal mining robot research and development and the cross-integration with new technologies such as 5G, the industrial internet, big data, artificial intelligence, and digital twins to improve the autonomous and intelligent application of coal mining robots. By establishing a scientific and complete standard system for coal mining robots, we aim to achieve the customized research and development and standardized production of various types of robot. A specific analysis is conducted on the research progress of common key technologies such as the explosion-proof design, mechanical system innovation, power drive, intelligent sensing, positioning and navigation, and underground communication of coal mining robots. The current research and application status of various types of coal mining robots in China are summarized. A new direction for future coal mining robot research and development is proposed. Robotic mining systems should be promoted to enhance the overall intelligence level and efficiency of mining equipment. To develop human–machine environment-integrated robots to improve the autonomy and collaboration level of coal mining robots, the digital twinning of the entire mine robot system should be accelerated; the normalized operation level of coal mine robots should be improved; research on coal mining robots, shield support robots, and transportation robots should be performed; intelligence should be achieved in fully mechanized mining faces; and equipment shield support for fully mechanized mining faces should be provided. The practical process of implementing coal mining robotization is summarized in this paper, and the technical and engineering feasibility of the coal mining machine population is verified. [ABSTRACT FROM AUTHOR]

With the rapid development of novel energy vehicles, power generation, photovoltaics, and other industries, power electronic devices have gained considerable attention. Insulated-gate bipolar transistors (IGBTs) have been widely used in those fields. With the emergence of intelligent manufacturing concepts such as Germany's "Industry 4.0" and China's "Made in China 2025", conventional manufacturing which needs to be upgraded with higher efficiency and yield is rapidly pivoting toward digitalization and intelligence. The digital twin methodology has been extensively used in various industries for constructing virtual models of physical entities, facilitating real-time data interconnection to reduce costs and improve efficiency. This study proposes a modular intelligent IGBT production line based on the digital twin. Real-time data are transmitted from a physical line to a digital line for storage and analysis. The digital line is visualized, and an intelligent management platform containing multiple functions is developed. Additionally, a process simulation database is established to obtain the optimal process parameters. Numerous quality issues that can arise during each process of IGBT packaging are addressed using a problem-solving approach based on the digital twin methodology. Consequently, this digital-twin-based IGBT intelligent production line effectively enhances yield rates and efficiency. IGBT modules with various packaging forms such as ACF, ACE, and ACD are manufactured. [ABSTRACT FROM AUTHOR]

• This review gives panorama of heritage digitization in China and categorizes digital twin techniques into six levels to address three specific tasks. • Terrestrial LiDAR and VR are the most prevalent digital twin technologies for decades, while sensor-based monitoring is recently prompted by policy incentives and legislations. • Most digital twin projects are dominated by public sectors, i.e., cultural management authorities, universities, and research institutes. • Efficient data utilization and the seamless integration of diverse data sources within cross-disciplinary collaborations are still challenging. Recent decades have witnessed digital twin as a toolkit to collect, process, and utilize graphical, spatial and other attribute data of cultural heritage. It has been widely applied on cultural world heritage sites in China, which have significant differences in ages, types, materials, and history of maintenance and conservation. Although specific cases are reported previously, a comprehensive overview is still lacking. This paper explores the association between digital twin and heritage conservation, classifying digital twin techniques into six levels to tackle three tasks: data acquisition, visualization and application. It systematically reviews Chinese heritage digitization practices, considering variations in complexity and purposes, and demonstrates typological and chronological characteristics as well as the composition of stakeholders. [Display omitted] [ABSTRACT FROM AUTHOR]

Real-time monitoring and forecasting are essential to ensure an on-time and on-demand supply of water diversion projects. However, water transfer systems currently lack spatiotemporal data in a dense resolution, failing to monitor real-time conditions and test plausible scenarios. To address the problem, this paper proposes a novel digital twin framework. It includes a real-time self-correcting model, which combines (1) a hydraulic solver using the one-dimensional Saint-Venant equations; and (2) a method updating hydraulic states driven by field observed data. This framework consists of four phases: preparation, warming up, tuning, and monitoring and predicting. Particularly in monitoring and predicting, an identification method for diagnosing abnormal events is also proposed as one of the functions of the twin model. The model shows beyond 98% similarity to reality based on the metric similarity (S) proposed in this paper on both of two real-world scenarios: a large flow scenario and a normal one. The deviation is generally lower than 5 cm for water level 2 m3/s for discharge. The abnormal situation diagnosis method also provides timely fault detection for daily scheduling. It is anticipated that this framework can be a powerful tool to estimate current canal states and predict change trends, further ensuring the security and efficiency of operations for large-scale water diversion projects. [ABSTRACT FROM AUTHOR]

Existing approaches for the 3D modeling of tunnels suffer from several problems, such as highly difficult data acquisition, redundancy of model data, large computational burden, and the inability of the resulting models to be monolithic. Therefore, solutions to the tunnel network modeling problem for complex structures need to be proposed and elaborated in detail. In this paper, a construction method for a three-dimensional tunnel general monomer model based on parallel pathfinding is proposed. Widely used tunnel CAD drawings are analyzed and read, a disordered arc ensemble intersection trend decision method is developed, and an automatic path extraction solution algorithm for unidirectional modeling of tunnel centerlines is constructed. By constructing and splicing the surface elements of the 3D model, a monomeric 3D tunnel model representing the complex network structure is finally obtained. Moreover, the modeling of shafts is realized based on the monomer model, allowing for the three-dimensional topological relationships between different sub-levels of the tunnel and the ground to be established. The automatic modeling method proposed in this paper is applied to the digital twin platform of a filling project in a mining area in Gansu province, China. The experimental results demonstrate that the 3D tunnel models constructed in this way have a smaller data volume, higher modeling accuracy, and more stable growth of modeling speed. [ABSTRACT FROM AUTHOR]

Due to the complexity of wetland ecosystems, wetlands have a wide area of alternating land and water zones and complex vegetation composition, making it challenging to achieve dynamic displays of virtual wetland scenes using three-dimensional modeling. This study proposes a workflow of game engine-based virtual wetland scene construction for the rapid modeling of virtual wetland scenes. The virtual wetland scene construction work utilized Poyang Lake as the primary research area. It integrated unmanned aerial vehicle data collection technology and geographic information technology with 3D (three-dimensional) modeling of wetland elements and scene program modeling of the game engine to complete the construction and dynamic development of virtual wetland scenes. In addition, it used various virtual reality technologies to display the virtual wetland scene. The virtual scene of Poyang Lake combined with actual data was more realistic and had higher simulation. In reality, the digital wetland scene of Poyang Lake realizes multiple forms of virtual experience and provides users with a profoundly immersive virtual experience. This comprehensive virtual scene workflow in the study can serve as a technical resource for building 3D scenes. It can also provide a technical reference for the digital twin watershed project of Poyang Lake, which has practical application value. [ABSTRACT FROM AUTHOR]

The digital twin is considered the central component of modern industry. It has been adopted in many industrial fields. However, its application in nuclear engineering is very rare, especially for the radioactive waste deposits winch is an urgent and tricky issue. Motivated by this demand and considering China's research & development guidelines for geological disposal of high-level radioactive waste (a three-step strategy by 2050 to construct the radioactive waste repository), a framework of the radioactive waste repository digital twin is proposed. The digital twin uses the framework edge + cloud with a multi-layer structure. It can be adopted in the construction of the radioactive waste repository. It can significantly strengthen the management capability, reduce the operating cost, improve the safety level and deal with accidents more efficiently. The first step for the achievement for the digital twin development of radioactive waste repository based on the framework is also introduced in the paper. The proposed digital twin framework of the radioactive waste repository in this work could be widely used as a reference and easily extended to support management in other industrial fields. [ABSTRACT FROM AUTHOR]

With the development of China's social economy, the pace of people's life is also accelerating. Therefore, in the face of the increasing pressure of life and work, people have begun to pay more and more attention to improving their physical fitness and they also pay more and more attention to sports in their spare time. The knee joint is a very important body part that will be involved in sports. Therefore, in sports, it is very important for healthy sports to pay attention to the state of the knee joint at all times and avoid injury to the knee joint during sports. The three-dimensional visual analysis of the knee joint movement trajectory in sports training refers to the trajectory capture and three-dimensional visual analysis of the knee joint movement in sports training based on the computer-based description and understanding of model technology. The three-dimensional visual analysis of the motion trajectory of the knee joint in sports training is conducive to the attention and analysis of the motion state of the knee joint in sports training, so as to help find problems and avoid knee joint damage during sports training as much as possible. This paper aims to study the 3D visualization analysis of knee joint motion trajectory in sports training based on digital twin and establish a 3D visualization system of knee joint motion trajectory based on digital twin technology, and based on this system, the three-dimensional visualization experiment of knee joint movement trajectory in sports training is carried out. The experiment concluded that the signal-to-noise ratio of the three-dimensional visualization image of the knee joint movement trajectory in sports training based on the three-dimensional visualization system established by the digital twin is 68% and the clarity is 89%. It shows that the system has a good 3D visualization effect. [ABSTRACT FROM AUTHOR]

The safety and effectiveness of marine piling construction are hindered by the display of imprecise spatial information and the potential for collisions due to the complexity of pile positions and attitudes. Digital twin (DT) can solve the above problems in piling construction because of their inherent three-dimensionality and real-time feedback. The present study proposes a system that utilizes the DT framework to construct a collision early warning system intended for marine piling. A five-dimensional model is used to introduce the overall architecture. The main twin objects are piles. To maximize the effectiveness of DT, the five-dimensional model is composed of four independently maintainable development modules. Consequently, a prototype of the system is created. The core of the system comprises an algorithm for pile positioning and an algorithm for collision early warning, especially for complex pile groups. Two distinct implementations are identified: one involves controlling the virtual with the real, while the other involves controlling the real with the virtual. Field experiments are conducted with the aim of establishing a benchmark for collision early warning during the construction process. The prototype of the system is capable of producing a simulated environment through a dual-layer mapping approach, which involves pile-boat mapping and early-warning feedback mapping. The presented system in a practical wharf project showcases both the capacity for three-dimensional visualization of collision warning and the ability to provide feedback for boat control. The research enables more intuitive and effective pile positioning. [ABSTRACT FROM AUTHOR]

• A framework of fault diagnosis with multiple validation of results is proposed. • A method for improving the performance of minor faults through digital twin models is proposed. • A digital twin model that can reflect the fault state of physical system is proposed. A subsea production system is essential for the subsea production of oil and gas. Real-time monitoring can ensure safe production. A subsea production control system is the core of the subsea production system and the top priority to be monitored. Minor faults refer to faults with weak characteristics and are difficult to be found. They are common and pose significant risks in subsea production system. Safe operation of the system can be guaranteed to the greatest extent with a timely detection and handling of minor faults. Digital Twin driven fault diagnosis is an effective method to monitor the subsea production control system. However, the combination of digital twin and fault diagnosis is not comprehensive, especially in data interaction. It leads to the fact that digital twin cannot improve fault diagnosis accuracy significantly, mainly when a minor fault occurs. A cross-validation enhanced digital twin-driven fault diagnosis methodology for minor faults of the subsea production control system is proposed to achieve high accuracy. Control, loss and fault parameters are presented and used for building a digital twin model. Bayesian Networks are used to construct a fault diagnosis model. Single and cumulative errors are used to measure the difference between digital twin models and physical systems. A verification feedback method is used to check the diagnosis results. Data of four days of a subsea production system in the South Sea of China is used to demonstrate the proposed methodology. The results show that the method can improve the diagnosis accuracy for minor faults effectively. [ABSTRACT FROM AUTHOR]

Efficient management of construction progress requires regular progress tracking, but monitoring progress on a daily basis can be both time-consuming and labor-intensive since the meticulous manual data processing is involved. Soil-foundation construction entails multiple uncertain underground safety risks, the elimination of which requires significant time and effort, thereby increasing the likelihood of schedule overruns. Automation and augmentation of progress monitoring are necessary for soil-foundation construction. There are two primary research challenges in the context of soil-foundation construction: the precise differentiation of objects with similar apparent characteristics, and the accurate detection of partially obscured objects. The existing approaches primarily address such issues by image post-processing rather than augmenting image feature inference. To enhance the efficiency of progress monitoring and facilitate unmanned inspection, an integrated computer vision-based framework for the tracking of soil-foundation construction progress was proposed. An improved SOLOv2 was utilized to qualitatively and quantitatively recognize the construction progress. Subsequently, the actual and differential progress could be integrated into streamlined BIM based on a self-adaptive grid-based mapping method. The approach was applied to a campus building project in China, resulting in a high segmentation accuracy (mAP = 90.9%). The improved SOLOv2 was found to surpass other state-of-the-art segmentation algorithms. Further, the impact of grid size on the mapping accuracy of construction progress was explored. The present study promotes automated schedule tracking and provides a feasible approach for developing a digital twin of soil-foundation construction. • Proposed a soil-foundation construction progress automated recognition framework. • Proposed an improved instance segmentation approach based on NAS-FPN. • Provided a enhanced feature extraction algorithm leveraging Swin Transformer. • Described a self-adaptive grid-based mapping method for progress visualization. • Applied the framework to a campus building project with a mAP of 90.9%. [ABSTRACT FROM AUTHOR]

The energy-saving potential of original equipment manufacturers (OEMs) for electronic products is substantial owing to the huge market size of assembling industry in China. A semi-automatic electronic assembly line (SAEAL) for smart-phones is introduced, and its digital twin (DT) enabled energy-saving platform is developed as the hardware foundation. We propose a stochastic dynamics model via max-plus algebra to characterize the spatio-temporal nature of state transition, which provides dynamical opportunity windows for further energy-saving control strategy. Accordingly, model linearization is conducted to obtain linear state and output equations. Then, a model predictive control (MPC) approach for energy saving optimization is provided to determine the optimal start-stop control signal. The dynamics modeling and MPC approach have been applied and verified in the case of assembly line. The results show that the optimization approach could reach a proportion of 49% for the equipment sleep time of total running time and save a considerable amount of energy for normal production of OEMs. [Display omitted] • A digital twin prototype system of smart-phone assembly line is developed as the testing platform for energy-saving control. • The assembling behaviors of assembly line are depicted by linear state equations via max-plus semiring algebra. • A spatio-temporal dynamics model is provided to evaluate the system performance based on max-plus semiring algebra. • An energy-saving MPC approach is proposed to determine the start-stop control sequences of automatic machines. • The equipment's sleeping time can reach a proportion of 49% of total running time without production efficiency loss. [ABSTRACT FROM AUTHOR]

International Marine Organization is promoting stricter carbon emission reduction targets, and the influx of environmental regulations presents compliance risks for maritime sector. Stakeholders need to take decarbonization performance indicators into account in ship routing to maintain regulatory compliance. To meet decarbonization goals, a decision support method related to environmental regulatory compliance is necessary for voyage optimization. To this end, this paper presents a digital twin-enabled framework for real-time decarbonization regulatory compliance prognosis in ship routing. The proposed framework tracks real-time carbon intensity and identifies opportunities for reducing operational risks associated with decarbonization goals. The digital twin approach leverage real-time environmental and operational data for improved estimation of vessel-specific regulatory compliance probability along the voyage. An improved dynamic programming algorithm is used for ship routing. The presented framework is versatile in dealing with various decarbonization regulations to provide routing decision support. An illustrated case of a container ship sailing across the South China sea is presented to demonstrate the applicability of the framework. [Display omitted] • A digital twin framework for prognosis of ship decarbonization regulatory compliance is developed. • Regulatory compliance is estimated by probabilistic analysis from a data-driven emission model. • The proposed framework is versatile to deal with different decarbonization regulations including EEXI and CII. • A case study of real-time ship routing considering compliance probability is presented. [ABSTRACT FROM AUTHOR]

Reactor Operation Digital Twin (RODT) is now receiving increasing attention and investment in nuclear engineering domain. A prototype of a RODT was first brought out by Gong et al. at Nuclear Power Institute of China. The RODT contains a forward solver for online real-time simulation and an inverse problem solver for parameter identification and state estimation. To further improve the efficiency and accuracy of RODT and promote the practical deployment of RODT (i) we first propose an advanced differential evolution algorithm to upgrade the inverse solver; (ii) then we bring out a systematical uncertainty quantification of RODT considering assimilation noisy observations. The accuracy and validity of the proposed RODT are tested along one cycle operating stage of HPR1000, at various operating temperatures, control rod steps, general power level and the inlet temperature of a practical nuclear reactor core. Numerous numerical results confirm its potential for practical engineering applications for on-line parameter identification and state estimation. • An advanced differential evolution algorithm is implemented into the proposed RODT. • Observation denoising is realized with PCA followed by a normalization process. • Numerical tests on HPR1000 proof its efficiency and accuracy of the improved RODT. [ABSTRACT FROM AUTHOR]

All walks of life in the world have entered the "big data era", and China's film and television industry is also developing rapidly. Digital twins can also have a huge impact on how film and television are created. This paper aims to study the theoretical knowledge of digital twins and film and television creation methods, and propose an image and video super-resolution reconstruction algorithm based on video computing and reconstruction. Both algorithms make perfect sense in digital twins. This paper proposes how to study the impact of digital twins on the way of film and television creation. It can be seen from the data in Table 1 of the experiment in this paper that the director's attention to the way of film and television creation has increased from 54% in 2013 to 70% in 2019. It can be seen that the decoding quality of the two reconstruction algorithms is higher than that of the traditional reconstruction algorithm. It can be seen that it is very meaningful to explore the impact of digital twins on the way of film and television creation.Digital twin is a multi-disciplinary, multi-physical, multi-scale, multi-probability simulation process that makes full use of physical models, sensor updates, and operating history to complete the mapping in virtual space, thereby reflecting the full life cycle of the corresponding physical equipment. process. A digital twin is a concept beyond reality that can be viewed as a digital mapping system of one or more important, interdependent equipment systems. [ABSTRACT FROM AUTHOR]

The thermal power sector, one of the major CO 2 emissions sources, is urged quicker steps to come up with an action that enables coal savings and carbon emissions reduction in China. Flexible operation is a growing trend among thermal power plants owing to the ever-increasing installation of renewable energy power resources. However, flexible operation comes with performance degradation, energy consumption growing and CO 2 emissions increment. This study proposes a digital twin system for a direct air-cooling unit to determine the optimal back-pressure and corresponding fan frequency modulation under various operating conditions to minimize the coal consumption and maximize the unit profit. Thus, a hybrid model of the digital twin system integrating an extreme gradient boosting algorithm tuned by a particle swarm optimization algorithm and a domain knowledge-based analytical formulation for the optimal back-pressure is proposed. The proposed digital twin system is applied to a 600 MW on-duty direct air-cooling power unit. In the industrial case study, the proposed system saved 535.526 tons of standard coal and decreased CO 2 emissions by 1335.068 tons within two weeks, which is remarkable. • PSO-XGBoost works well in calculating the discharged steam enthalpy online. • The optimal back pressure varies with the flexible operation. • A proposed hybrid model integrating historical data and domain knowledge. • Proposed digital twin reduces 535.526 tons of standard coal within two weeks. [ABSTRACT FROM AUTHOR]

With the rapid growth of China's economy, applied research on hydropower engineering has received an increasing amount of attention. However, since hydraulic electromechanical devices often work in actual industrial manufacturing environments at high loads for a long time, their health status is hardly predicted. By introducing digital twins technique, this paper proposed a predictive maintenance model for electromechanical devices to solve the problems. Firstly, multiple sensors are implemented on critical parts of the hydraulic electromechanical devices to collect devices' physical and spatial signals. Secondly, constructing the digital twins model of electromechanical devices with the sensing data and the devices' structural characteristics. Finally, by transfer learning, a comprehensive and reliable fault diagnosis method is designed to predict the remaining life of the devices and make decisions for facilities maintenance. Experiments show that the proposed model performs the best accuracy rate compared with the other methods. [Display omitted] • Use edge devices to integrate heterogeneous data in smart water conservancy. • Build four kinds of models with digital twins for qualitative analysis. • Train neural networks with digital twins' real-time data to make fault diagnoses. • Make safety assessment criteria for smart water devices with diagnosis results. [ABSTRACT FROM AUTHOR]

• A higher accuracy engine performance simulation model is developed and calibrated. • A GT-Simulink coupling simulation platform with NSGA II is established. • The SVM model is established and calibrated by the experimental data. • Integrated platform for simulation-optimization and verification is developed. • Maximum fuel consumption and NOx reduction rates reach 5.0% and 70% respectively. This paper is pioneered in developing digital twins by GT-Power software and multi-objective evolutionary optimization (MOEO) using NSGA II algorithm for an Atkinson cycle gasoline engine under China VI emissions standards. Firstly, an experimental investigation is conducted and relevant experimental data is obtained. Based on this, the corresponding 1D GT-Power simulation model is established and calibrated according to the obtained test data. Secondly, the four decision variables including the spark advance angle (SA), exhaust gas recirculation (EGR) rate, exhaust variable valve timing (VVT-E) and intake variable valve timing (VVT-I) are input into the simulation model, the optimal values of the decision variables will be determined via MOEO to minimize NOx emissions and the brake specific fuel consumption (BSFC). Thirdly, based on the data obtained from the scanning test, a machine learning method is used to build an engine performance prediction model through the support vector machine (SVM) regression algorithm. The inputs (control parameters obtained from the optimization process) including SA, EGR, VVT-I and VVT-E are imported to predict the performance output of the engine. The results show that under the engine control parameters obtained by the NSGA II algorithm, the simulation values of engine performance parameters have been greatly optimized, the decreasing extent of fuel consumption is about 5.0%, besides, the decreasing extent of NOx is about 70%. What is more, the increased EER and EEE is up to 6.21% and 2.26%, respectively. And then most of the predicted values obtained by machine learning have been optimized. For BSFC, in general, the simulation value and the predicted value are in good agreement at the smaller value, indicating that the simulation model and the regression prediction model basically achieve the same value at the lower BSFC of the engine. For NOx, the simulated and predicted values have all been optimized. Furthermore, the method and platform developed in this paper will help to carry out a series of related work in the field of vehicle energy flow distribution and optimization when changing different control strategies and optimization methods in the future. Besides, the above work provides a reliable theoretical basis and digital model support for the development of energy-saving and efficient Atkinson cycle engines, which further drives the application of Atkinson cycle engines in new energy vehicles. [ABSTRACT FROM AUTHOR]