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Review

Development and Application of Digital Twin Technique in Steel Structures

by
Linze Shi
1,
Yong Ding
1,2,* and
Bin Cheng
3
1
Department of Civil Engineering, Ningbo University, Ningbo 315211, China
2
National Experimental Teaching Center for Civil Engineering Virtual Simulation, Ningbo University, Ningbo 315211, China
3
School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2024, 14(24), 11685; https://doi.org/10.3390/app142411685
Submission received: 31 October 2024 / Revised: 3 December 2024 / Accepted: 8 December 2024 / Published: 14 December 2024
(This article belongs to the Special Issue Structural Health Monitoring in Bridges and Infrastructure)
Browse Figures
Versions Notes

Abstract

:
Steel structures face significant challenges in long-term maintenance because of complex and unstable service environments. Fortunately, the digital twin technique offers an excellent solution by creating a digital model and continuously updating it with real-time monitoring data. To determine the development and application status of the digital twin technique in steel structures, a review drawn on the latest literature from the past fifteen years was conducted. The bibliometric analysis and innovation discussion of these studies primarily focused on publication details, keyword information, and application specifics. Additionally, significant attention was given to the evolution of digital twin definitions, modeling methodologies, and application fields. The analysis results indicate that the digital twin technique in steel structures has made significant advancements in both its definition and modeling methodologies, thanks to worldwide contributions. Meanwhile, this technique also demonstrates advantages in the applications of material deformation, structural monitoring, infrastructure maintenance, and fatigue assessment. Based on this review of the existing literature, the future development of the digital twin technique in steel structures should focus on model innovation, application expansion, and performance optimization.

1. Introduction

Steel structures are a significant type in civil engineering and have been widely applied in various urban and suburban infrastructures, including wind turbines [1,2], industry systems [3,4,5], high-rise buildings [6], long-span bridges [7,8], hydro-steel structures [9], pipeline networks [10], and so on. However, the long-term maintenance of steel structures faces significant challenges due to complex and unstable operating environments. These conditions not only lead to external corrosion from temperature and humidity but also impact the internal resistance to strength and fatigue. Therefore, construction techniques [11,12,13], design methods [14,15], monitoring approaches [16,17,18], and maintenance programs are indispensable for addressing these challenges in steel structures [19,20]. Traditionally, these four aspects of steel structure application are treated independently, leading to a lack of coherence between different stages of compliance and causing issues with information consistency and coordination. Fortunately, the emergence of the digital twin technique in recent years offers new ideas and solutions to address these problems through data collection [21,22,23], model creation [24,25], data integration [26,27], simulation analysis [28,29], and rational determination [30,31]. This kind of technique actually represents a cutting-edge approach where a virtual replica of a physical object or system is created and continuously updated with real-time data from sensors and Internet of Things devices [32]. As illustrated in Figure 1, the steel beam arch bridge under construction is considered a physical object, and the virtual replica model is timely established by monitoring spatial data from the Internet of Things and real-time monitoring; the whole process can be considered as the application of the digital twin technique.
Recently, researchers have been actively investigating the integration of the digital twin technique with advanced technologies, such as intelligent algorithms and sophisticated instruments [33,34,35,36]. These efforts develop many advanced and predictive models, thereby expanding the potential applications of the digital twin technique in steel structures, as well as neighboring scopes, such as manufacturing [37,38,39], transportation [40,41,42], smart cities [41,43,44,45], aerospace [46,47], and automotive [22,48], with which several correlative technique areas are connected, involving cloud computing [49,50], the Internet of Things [51,52,53,54], material mechanisms [55,56,57,58], and artificial intelligence [59,60]. Therefore, the aim of the development and application of this technique is to not only enable structural simulation and analysis but also enhance efficient monitoring, predictive maintenance, streamlined operations, and informed decision-making.
In order to reveal the current condition of development and application of the digital twin technique in the field of steel structures, by which researchers can subsequently and easily conduct relevant studies, the methodologies of this state-of-the-art review primarily performed by the commercial management software EndNote were used as a definition of objective and scope, literature collection and classification, information extraction and analysis, and innovation summary and discussion. The principle of reference search through preferred reporting items for systematic reviews conforms to reference [61]. Firstly, the objective and scope are focused on the digital twin technique in steel structures. Secondly, this review questions how the corresponding digital twin technique has been developed and applied in recent years. Thirdly, the references were specified to the publication years spanning from 2009 to 2024, which consist entirely of journal articles from the Science Citation Index, considering that it is known for containing high-quality and reliable data. Fourthly, the selection process was performed based on EndNote, by which a total of 140 literatures were collected as references [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140].
The review is organized into four sections. Section 1, gives the introduction of the review background, elaborating on its importance and necessity. Section 2, performs the literature analysis using bibliometric methods, including publication details, keyword information, and usage exploration. Section 3, conducts the innovation discussion for the collected literature, covering the evolution of the digital twin concept, analysis methodologies, and its applications in steel structures. Section 4, presents the review conclusion and provides the recommendations for further research on the digital twin technique in steel research.

2. Literature Bibliometric Analysis

Based on the collected literature concerning the digital twin technique in steel structures, a bibliometric analysis was fully conducted. In the analysis, the publication details, keyword information, and usage exploration in these literatures were systematically analyzed through quantitative statistics and correlation analysis; meanwhile, the literatures about the digital twin technique used in bridges, buildings, and materials were also analyzed in bibliometrics.

2.1. Publication Analysis

To analyze the publication details of these literatures, the focus was primarily on the publication dates, which can reflect the development of the digital twin technique in steel structures. The number variation of the publications is reflected in Figure 2 from 2009 to 2024, indicating that the digital twin technique has developed significantly in the recent years. Note that the number of publications has increased rapidly from 2020 to 2024, whose trend highlights the growing interest and the active research in the digital twin technique for steel structures [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111]. The yearly variation of the publication numbers aligns with the universal trend where an emerging topic firstly experiences a period of steady growth followed by a rapid increase.
The situation of publications in various journals is subsequently discussed, and Figure 3 provides a clear visual indication of which journals focus on the topic of the digital twin technique in steel structures. Obviously, many publications on this topic have appeared in journals like “Materials Science and Engineering” [35,55] and “Acta Materialia” [110,117], which primarily focus on the properties and microstructure of structural materials, and the results indicate that the digital twin technique is utilized to explore the material mechanisms of steel at the micro level. Meanwhile, some studies on the digital twin technique of steel structures are published in journals around the theme of structural engineering, such as “Engineering Failure Analysis” [19,22], “Engineering Structures” [1,92], and “Structures” [41,50]. It is indicated that newly developing technologies are initially applied at the material level before gradually being adopted at the structural level. Additionally, several journals focused on health monitoring and fatigue assessments are reported based on the digital twin technique, highlighting its significant role in structural maintenance and management.
Literature regions also make significant contributions to advancing the digital twin technique for steel structural engineering. These regions not only indicate the development of this technique, enabling simulation, real-time monitoring, and optimization for structural performance, but also present the cooperation across multiple disciplines for the digital twin technique, involving structural engineering, computer science, and data analytics. Therefore, bibliometric analyses of literature regions and their correlations are of great interest. The regions of the literatures in the current study are displayed in Figure 4.
These literatures are primarily from China [6,21,36], Singapore [1,25,122], Australia [27,50,59], Japan [20,35,67], Korea [58,100,124], the United States [2,16,57], England [3,33,42], Spain [19,48,68], Italy [15,22,66], and Germany [63,72,82], as marked in red on the world map. The black lines connecting these regions indicate research collaborations between countries about the digital twin technique in steel structures. Notably, China, England, America, Singapore, Australia, and Italy have frequent collaborations on this topic.

2.2. Keyword Investigation

Keywords are considered as crucial elements in reflecting the central theme of the literature. They not only highlight the main topic but also help the reader understand the relevant studies and the focus of the research. These keywords are all acquired from the provided literature in the references. The keywords with the most frequent occurrences are presented in Figure 5.
It is unsurprising that the keywords “digital twin” and “steel structures” appear most frequently in this distribution, as the literature was collected based on these terms. Additionally, several keywords related to application effects, such as “health monitoring”, “life cycle forecasting”, and “structure maintenance”, are also prominent. The results indicate that the literature on the digital twin technique plays a crucial role in structural health monitoring, condition maintenance, and service life assessment, which are essential aspects of steel structure management. By combining the digital twin model with real-time monitoring data, the model’s properties can be updated promptly, by which the importance of the digital twin technique is exhibited in structural condition management. Other frequently appearing keywords, such as “Finite element”, “Scanning electron”, and “Machine learning”, represent different approaches used in the digital twin technique, meaning that the digital twin model can be established using a finite element analysis, scanning point-cloud data, or machine learning.
The occurrence and correlation of these keywords are further analyzed using a correlation analysis and presented in a two-dimensional color cloud image, as shown in Figure 6. Note that the color gradient from dark to light represents the increasing number of keyword co-occurrences in the literature. The colors along the diagonal lines in Figure 6 indicate the auto-correlation of these keywords, whereas the colors in other areas represent the correlation of multiple keywords. It is obvious that the keyword “digital twin” is closely correlated with several other keywords, such as “Health monitoring”, “Image correlation”, “Finite element” and “Digital storage”. Similarly, “Health monitoring” also shows strong correlation with “Image correlation”, “Finite element”, and “Digital storage”. These correlation results demonstrate that the digital twin technique is widely applied in health monitoring, with extensive use of finite element and image correlation methods. Meanwhile, the keywords “Life cycle” and “Maintenance” are also closely related to other terms shown in the above figure, indicating that the digital twin technique has an advantage in structural maintenance and service life assessments.

2.3. Usage Exploration

The literature about the digital twin technique used in different aspects of steel structures is analyzed in bibliometrics, which can be beneficial for the depth exploration of technical applications. Actually, their usages can be divided into three parts, that is, bridges, buildings, and materials, relating to the publication distribution in Figure 3 and the keyword distribution in Figure 5. The variation in technical usages of the digital twin technique in bridges, buildings, and materials from 2009 to 2024 is shown in Figure 7.
It can be obviously seen that the variation in bridges, buildings, and materials presents distinct differences in the application of the digital twin technique. The technical usage in bridges primarily began in 2018 and then gradually increased with a steady trend as time went by, while these applications in buildings were initiated in 2010 but began to increase in 2019 with a relatively quick, rapid trend. For usage in materials, the literature in these years was published all the time, in which the number of literatures increased at first and then presented a decreasing trend. The variation in technical usages of the digital twin technique in bridges, buildings, and materials in these years obviously indicates that the digital twin technique was first applied for research on materials and was subsequently used in bridges and buildings. This regularity is actually fitted with the one shown in Figure 3.
The above section primarily discusses intriguing information about publications, keywords, and applications from the literature based on a bibliometric analysis, highlighting the importance of the digital twin technique in the monitoring, maintenance, and assessment of steel structures.

3. Literature Innovation Discussion

The innovations in the literature can reflect the development and application of digital twins in steel structures, and, hence, the research in the aforementioned 140 literatures was discussed around technical innovation in this section, mainly focusing on the definition of digital twins, the evolution of methodologies, and their application fields. The chronological order of these literatures typically presents the evolution of the digital twin concept; meanwhile, these technological methodologies and corresponding applications play great roles in the research of the digital twin technique in steel structures.

3.1. Definition Evolution

The definition of the twin model in the digital twin technique has undergone extensive evolution and development. The literature distribution about the evolution of the digital twin definition can be integrated in Table 1. Initially, the digital twin technique emerged from graphic analysis methods based on digital forms, primarily involving digital image correlation and electron backscatter diffraction. These methods were combined and widely used to study the properties of steel materials, with various application aspects such as surface damage in tensile processes [1,131], deformation behavior of steel [35,125], strain mechanism of steel [57,138], plasticity behavior of steel [38,110], fracture mechanism of metallic materials [44,140], quality evaluation of alloys [75,132], instrumented indentation of steel [79,128], dynamic behavior of metal tests [124,134], etc. The situation suggests that while the method can effectively reveal the deformation, fracture, and dynamic properties of steels, it remains confined to the technical level.
With the sustainable development of computing and the Internet of Things, the digital twin technique has acquired a new definition. A virtual model can be established based on real structures and then updated with real-time monitoring data, represented by the laser scanning and finite element models. This virtual model method is also involved in many typical applications, such as monitoring for steel structures [6,107], maintenance for large-span structures [5,65], research on strip rolling techniques [14,139], additive manufacturing [63,126], prediction for steel manufacturing [26,73], fatigue evaluation of infrastructure [7,92], construction for steel materials [53,99], digitalization in chemical engineering [66,101], etc. From the above literature, the virtual model method presents significant advancements in steel material digitalization, steel component manufacturing, and structural condition maintenance, meaning that this technique has gradually evolved from focusing on steel materials to encompassing steel structures. Real-time monitored data enable the model to iterate, advancing beyond the technical level and gaining importance for the Internet of Things.
Moreover, the digital twin technique is evolving toward a diversity in twin models for real structures, including not only a traditional virtual model but also a surrogate model, such as those based on machine learning algorithms. The surrogate model is considered for application due to the challenge in establishing a virtual model, because no visual digital model is established in this scheme. Note that the surrogate model has also been applied in various fields, including crack identification and location [1,8], improvement of steel production [11,88], mechanical mechanism modeling [64,102], condition monitoring for steel equipment [52,104], prediction and maintenance of structure [30,78], etc.
The evolution and development process of the digital twin technique can be summarized as shown in Figure 8, where the form of the digital twin model varies from a digital image to the virtual model with a scanning and finite element and then to the surrogate model. It can be recognized that the digital twin technique in steel structures is developed with the improvement in computer performance and the Internet of Things. The digital image is just a two-dimensional medium, which is primarily used to analyze the deformation of steel materials. The digital model is regarded as a three-dimensional medium, which can be used to explore the visual display and mechanical properties of steel structures. Moreover, the surrogate model is estimated as a multidimensional model, which can deal with more complex features of structures without an actual virtual model, involving nearly all the properties of steel structures. Obviously, advancements in the Internet of Things and computer performance have driven the digital twin technique and made it well applied in many practical fields.

3.2. Methodology Development

The methodologies of the digital twin technique have experienced tremendous development over the past decade, primarily involving building information modeling (BIM), machine learning algorithms, laser scanning with point clouds, finite element modeling, and digital image correlation. Digital image correlation can be considered the predecessor of digital twins, and, hence, the quantity proportion of the literature for digital twin methodology mainly comprises the other four methods mentioned above, as illustrated in Figure 9. The figure indicates that finite element modeling appears more prevalent than other methods reported in the literature.
The finite element method can be regarded as a digital twin technique equipped with a numerical simulation model, primarily conducted in the program “Abaqus 2024”. It possesses the advantage in mechanical property of high precision, as shown as the mesh element of steel spindle provided by reference [48] in Figure 9. Through comparing the prediction results of the digital twin model with the actual performance of a physical structure, this type of model can simulate structural behavior under different conditions for reflecting its current state, and then the model can be optimized and adjusted to improve the prediction accuracy combined with real-time performance. Some research in the literature focuses on the combination of the finite element model and analysis algorithm to enhance simulation effects [3,109], while some research focuses on novel monitoring techniques for updating finite element models with real-time data [4,117] and others on the application effects of the finite element method [18,119], where the advantages of the finite element model are represented as efficient computing power, intuitive result displays, and high mechanical accuracy.
The BIM method is another digital twin technique stringing structural design, construction, and management together, often performed in the program “Revit 2024”. The core purpose of BIM is to achieve the integration of building information, ensuring that all relevant information throughout the entire life management (e.g., design, construction, and maintenance) is concentrated in a three-dimensional model information database, such as the virtual roll combined with an optical fiber, provided by reference [74] in Figure 9. Based on this method, three-dimensional graphics and an object-oriented technique combined with computer-aided design are utilized in construction projects. Some research in the literature focuses on the establishment of the BIM framework [6,115], some on the data exchange within the BIM model [5,101], and others on the effectiveness of the BIM model in structural management [63,103]. The research findings indicate that the BIM method offers advantages in improving efficiency in structural maintenance and facilitating collaborative information work.
The laser scanning method relies on high-speed laser scanning measurements to obtain three-dimensional coordinates, reflectance, and color data of structural surfaces over large areas with high resolution and speed, usually conducted in the program “CloudCompare 2.13”. It allows for the rapid reconstruction of the three-dimensional point-cloud model, as shown as the steel building measured by terrestrial laser scanning, provided by reference [90] in Figure 9. In the collected literature, some research focuses on the establishment process of point-cloud models through laser scanning [37,90], some on model analysis to improve monitoring accuracy [12,67], and others on model functionality for structural management enhancement [26,122]. Moreover, these research findings highlight the advantages of the laser scanning method, which involves real-time dynamic monitoring, digital functionality, and high measurement efficiency.
The machine learning method can be considered an advanced surrogate model to predict future structural behavior when it is used in the digital twin technique, primarily performed in the program “Python 3.10”. The machine learning model has potential in reflecting the condition and performance of physical structure by real-time data updating, and, therefore, robust support for decision-making can be provided, such as the topological structure of a neural network, provided by reference [78] in Figure 9. In the above literature, some research focuses on algorithm innovation in machine learning [8,105], some on the data-driven effects of the fully trained model [11,88], and others on the combination of the digital twin concept and machine learning [64,102]. This research mainly indicates that the machine learning method obtains superiority in dynamic prediction, structural optimization, and cross-field applications. The literature distribution for the aforementioned methodologies of the digital twin technique is illustrated in Table 2.
It is clearly observed in Figure 9 and Table 2 that the finite element method has occupied the largest proportion in these digital twin methodologies, while the BIM model method presents the second proportion, and the machine learning method has the least proportion. The reason lies in that the digital twin models of steel structures, established by the finite element, BIM model, or laser scanning, all developed fully in recent years, while the machine learning method has just begun to be adopted as a surrogate model of steel structures. Compared with these digital twin methods, the finite element method has an advantage in characterizing structural, mechanical, and parametric analyses and model synchronization and updating. The BIM and laser scanning methods focus on structural visualization and data management. The machine learning method can make full use of structural monitoring data and obtain crucial structural properties, having more potential in future development. Mentioned that the combination of these methods can fuse their results and perform their advantages in the digital twin process, which is from data monitoring, model updating, and structural condition maintaining.

3.3. Application Field

Based on the definition evolution and the methodology development of the digital twin technique in the recent fifteen years, this technique has been applied in numerous fields of steel structures, referring to the collected literature. Specifically, these application fields in steel structure primarily involve four parts, which are material deformation, fatigue assessment, infrastructure management, real-time monitoring, and mechanical deformation property. The quantity proportion of the collected literature for these application fields is shown in Figure 10, from which the application field of the digital twin technique can be considered as broad-ranging.
There is much research focusing on material deformation characteristics using the digital twin technique, such as elastic deformation, plastic displacement, fatigue failure, and alloy creepage. As shown in the diagram presented in Figure 10 from reference [69], the deformation and lifecycle analysis of steel materials were analyzed by an experimental study and a finite element analysis, based on which the predicted strain data from FEA simulations could map crucial strain and material deformation to test specimens. These characteristics can be reflected and simulated to varying degrees within the digital twin model; meanwhile, the indicators of material deformation in the model encompass the deformation mechanism [57,138], deformation observation [50,83], high-performance material deformation [20,121], and complex deformation conditions [55,91]. Through establishing the high-precision digital twin model and integrating an advanced simulation technique, the deformation mechanisms of materials are thoroughly studied, the complex deformation parameters are optimized, and the preventive measures are formulated.
Infrastructure management is a prominent application area for the digital twin technique, particularly in critical structures, such as bridges, buildings, turbines, silos, and tanks. A digital twin model with an early warning mechanism of structural condition is usually the first step to establish by considering external dynamic loads. After that, the efficiency and safety of structural management are enhanced significantly by providing detailed maintenance guidance based on these models. Such as the diagram presented in Figure 10 from reference [36], bridge infrastructure was monitored by laser scanning to acquire raw data. After that, the digital twin model could be achieved based on point clouds and quality inspection. At last, infrastructure management could be conducted based on the established digital twin model. Many studies have also reported the application of this type in the maintenance of large-scale buildings [5,65,82], super-long bridge management [36,95], advanced steel manufacturing [14,99], and mechanical property evaluation [39,122]. These application cases clearly demonstrate that the advantages and potential of infrastructure management and life cycle forecasting are based on the digital twin technique, which can optimize resource allocation and management decisions by simulating various operations and maintenance schemes.
Fatigue assessment utilizing the digital twin technique has been applied in bridges, aircrafts, and other types of steel structures. As shown in the diagram presented in Figure 10 from reference [24], the fatigue information was firstly captured by a depth camera, and then the crack node in the image coordinate was determined. After that, the crack surface topology was reconstructed, and, ultimately, the three-dimensional topology of the crack was generated based on the digital twin technique. Subsequently, fatigue assessment could be conducted based on crack topology. Many studies have also reported its applications in various subjects, such as fatigue crack detection [1,34], crack growth monitoring [25,76], fatigue crack prediction [7,92], and fatigue resistance evolution [68,81]. Based on these research results, the digital twin technique proves to be advantageous in fatigue assessment through dynamic load feedback and fatigue safety enhancement, particularly in fatigue-prone infrastructures such as steel turbines, steel pipes, steel bridges, and steel storage structures, where fatigue crack detection, fatigue life estimation, and reinforcement decision-making play important roles in ensuring the safety of citizen lives and properties.
Real-time monitoring is another advanced application of the digital twin technique that has the potential to be integrated with other emerging techniques, such as the Internet of Things, big data, cloud computing, and artificial intelligence. The integration enables real-time perception, simulation, prediction, and optimization of physical structure, and, therefore, the early warning of structural conditions can be issued. As shown in the diagram presented in Figure 10 from reference [107], it was constraint monitoring in anti-erosion sensing revetment. Real-time monitoring in this situation was performed, in which the steel wire yield stress can be fully detected by the optical fiber continuous sensor. Many studies have also reported applications of real-time monitoring in structural condition identification [4,107], steelmaking monitoring [12,115], large-scale facility manufacturing [17,104], and structural measurement [31,103]. It is obvious that the digital twin technique can accurately and timely reflect structural behavior, potential problems, and failure tendency influenced by exterior dynamic loads, combined with data acquisition, transmission, processing, analysis, and model updating, thereby providing a scientific basis for decision-making.
The digital twin technique also has other applications in steel structure, such as research on special material properties [60,75], advanced measurement techniques [18,79], typical manufacturing processes [29,109], and specialized industries [53,119]. As an advanced simulation and management technique for steel structure, the digital twin technique offers significant advantages in areas where traditional methods fall short, poising for widespread adoption in Industry 4.0 intelligent manufacturing and smart city infrastructure.
The literature distribution related to the application fields of the digital twin technique is presented in Table 3. It can be concluded that the digital twin technique can be applied in various fields of steel structure, especially in healthy monitoring and condition maintaining. Note that different application fields may be suitable for different digital twin methodologies. Specifically, laser scanning is often used in real-time monitoring, finite element analysis is usually used to explore material deformation, and the BIM model is actually used in online systems for infrastructure management and life cycle forecasting, while machine learning has an advantage in dealing with large amounts of data in figure assessments.

4. Conclusions and Recommendations

This review about the development and application of the digital twin technique in steel structures, based on the latest literature from the past fifteen years, was conducted to analyze its development in recent years and to discuss its application in the field as follows:
  • The bibliometric analysis of the literature focused on the publication details, keyword information, and application specifics, indicating that the digital twin technique in steel structures had developed rapidly, with most publications hailing from the Asia–Pacific and European regions. The keywords emphasized that the function of the digital twin technique can be the monitoring, maintenance, and assessment of steel structures. Meanwhile, usage exploration around bridges, buildings, and materials indicated that the digital twin technique was first applied in the research on material aspects and then used in bridges and buildings.
  • The discussion of the literature innovations primarily addressed the evolution of digital twin definitions, the development of modeling methodologies, and the corresponding application fields. These definitions evolve from a digital image to a virtual model and then to a surrogate model, and these models can gradually deal with more complex features of a structure. Meanwhile, these methodologies demonstrate their respective advantages in monitoring data usage, model visualization, and updating. Moreover, the digital twin technique is suitable to apply in various fields of steel structure, especially in healthy monitoring and condition maintenance, while different application fields may be appropriate for different digital twin methodologies.
Based on the above conclusion, it is obvious that future research orientations for the digital twin technique in steel structures mainly lie in the innovation of the digital twin model, the expansion of application situations, and the optimization of model performance as follows:
  • Model innovation should focus on the multiple-dimension model and high-precision modeling, where the established model not only has the ability of data utilization with large amounts and heterogeneous properties but also can reflect the multi-property of a steel structure, such as the surrogate model combined with machine learning.
  • Application expansion means that the digital twin technique should obtain potential to be applied in the broader field of steel structures, although it already has many usages. Meanwhile, digital twin methodology and its application should get multidisciplinary fusion and boundary crossing; for example, the material damage prediction can also use digital twin models combined with verification tests in real conditions.
  • Performance optimization should concentrate on the improvement of the functions of the digital twin model to extract structural properties. As the key problem of structural maintenance, structural life cycle forecasting is achieved by BIM, while the issue can be explored more broadly to achieve better performance using other digital twin methods like the finite element model or machine learning.

Author Contributions

Conceptualization, Y.D. and B.C.; methodology, L.S. and B.C.; software, L.S.; validation, L.S.; formal analysis, L.S.; investigation, L.S.; resources, L.S.; data curation, L.S.; writing—original draft preparation, L.S.; writing—review and editing, L.S. and Y.D.; visualization, L.S.; supervision, Y.D. and B.C.; project administration, Y.D. and B.C.; funding acquisition, Y.D. and B.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Science Foundation for Distinguished Young Scholars (No. 52325805), the National Natural Science Foundation of China (No. 52078256), and the Natural Science Foundation of Zhejiang Province (No. LTGS24E080002).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare no conflicts of interest.

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Applsci 14 11685 g001
Figure 1. Application of the digital twin technique in a steel structure.
Figure 1. Application of the digital twin technique in a steel structure.
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Figure 2. Publication development in recent years.
Figure 2. Publication development in recent years.
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Figure 3. Publication distribution of the literature.
Figure 3. Publication distribution of the literature.
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Figure 4. Publication regions of current research.
Figure 4. Publication regions of current research.
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Figure 5. Keyword distribution of the literature.
Figure 5. Keyword distribution of the literature.
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Figure 6. Occurrence and correlation of keywords.
Figure 6. Occurrence and correlation of keywords.
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Figure 7. Literature about bridges, buildings, and materials in recent years.
Figure 7. Literature about bridges, buildings, and materials in recent years.
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Figure 8. Evolution and development of the digital twin technique [10,81,95,105].
Figure 8. Evolution and development of the digital twin technique [10,81,95,105].
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Figure 9. Quantity proportion of the literature for digital twin methodology [48,74,78,90].
Figure 9. Quantity proportion of the literature for digital twin methodology [48,74,78,90].
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Figure 10. Quantity proportion of the literature on digital twin application [24,36,60,69,107].
Figure 10. Quantity proportion of the literature on digital twin application [24,36,60,69,107].
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Table 1. Literature distribution of the digital twin definition.
Table 1. Literature distribution of the digital twin definition.
DefinitionAspectReferences Number
First step:
Digital form		Surface damage in tensile process[10,82,108,118,131]
Deformation behavior of steel[35,55,56,94,97,125]
Strain mechanism of steel[57,83,85,91,96,127,138]
Plasticity behavior of steel[38,58,100,110]
Fracture mechanism of metallic materials[44,68,76,123,140]
Quality evaluation of alloys[75,87,113,129,130,132]
Instrumented indentation of steel[79,128]
Dynamic behavior of metal tests[124,134]
Second step:
Virtual model		Monitoring for steel structures[6,16,17,62,104,107]
Maintenance for large-span structures[5,34,45,65,72,104]
Research on strip rolling technique[14,27,46,115,139]
Additive manufacturing[51,63,76,126]
Prediction for steel manufacturing[26,28,29,48,73]
Fatigue evaluation of infrastructure[7,16,24,34,40,81,92]
Construction of steel materials[53,70,99]
Digitalization in chemical engineering[66,101]
Third step:
Surrogate model		Crack identification and location[1,8]
Improvement of steel production[11,13,21,88]
Mechanical mechanism modeling[23,64,102]
Condition monitoring for steel equipment[52,104]
Prediction and maintenance of structure[30,54,78]
Table 2. Literature distribution of digital twin methodology.
Table 2. Literature distribution of digital twin methodology.
MethodSubjectReferences Number
Finite elementAlgorithm fusion[3,7,9,24,31,43,48,70,81,89,109]
Monitoring technique[4,16,39,49,59,77,93,104,107,117]
Application effect[18,22,25,32,40,41,55,69,80,92,119]
BIM modelDate exchange[6,14,28,34,115]
Framework establishment[5,27,53,62,66,99,101]
Management effectiveness[29,63,72,74,103]
Laser scanningPoint-cloud analysis[37,45,84,90]
Model establishment[12,17,19,33,36,67]
Model function[26,50,95,122]
Machine learningAlgorithm innovation[1,8,105]
Data-driven effect[11,23,54,88]
Model fusion[30,52,60,64,78,102]
Table 3. Literature distribution of digital twin application.
Table 3. Literature distribution of digital twin application.
Application FieldSubjectReferences Number
Material
deformation		High-performance material deformation[20,35,37,87,94,106,108,109,112,118,121]
Deformation observation[50,69,83]
Complex deformation condition[47,55,56,91]
Deformation mechanism[57,85,97,100,113,124,125,127,128,129,130,131,132,133,134,136,137,138]
Infrastructure
management		Large-scale facility maintenance[5,19,33,65,70,82]
Bridge management[36,40,41,80,95]
Advanced steel manufacturing[14,21,26,59,77,99]
Steel mechanic assessment[39,54,78,84,89,102,122]
Fatigue
assessment		Fatigue crack detection[1,8,10,22,34]
Crack growth monitoring[25,76]
Fatigue crack prediction[7,16,24,73,92]
Fatigue resistance evolution[68,81]
Real-time
monitoring		Structural condition identification[4,6,45,52,62,74,107]
Steelmaking monitoring[12,13,51,63,88,115,126]
Large-scale facility manufacturing[17,39,71,104]
Structural measurement[31,88,96,103]
Other
applications		Special material property[11,15,23,38,42,44,49,60,64,75,98,101,111,117]
Advanced measurement[18,27,28,30,58,79]
Typical manufacturing process[29,46,72,109]
Special industry[53,66,119]
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Shi, L.; Ding, Y.; Cheng, B. Development and Application of Digital Twin Technique in Steel Structures. Appl. Sci. 2024, 14, 11685. https://doi.org/10.3390/app142411685

AMA Style

Shi L, Ding Y, Cheng B. Development and Application of Digital Twin Technique in Steel Structures. Applied Sciences. 2024; 14(24):11685. https://doi.org/10.3390/app142411685

Chicago/Turabian Style

Shi, Linze, Yong Ding, and Bin Cheng. 2024. "Development and Application of Digital Twin Technique in Steel Structures" Applied Sciences 14, no. 24: 11685. https://doi.org/10.3390/app142411685

APA Style

Shi, L., Ding, Y., & Cheng, B. (2024). Development and Application of Digital Twin Technique in Steel Structures. Applied Sciences, 14(24), 11685. https://doi.org/10.3390/app142411685

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