1. Introduction
Prestressed steel structures have the advantages of strong spanning capacities, beautiful shapes, light weights and short construction periods, and they are used in public buildings, such as in large stadiums [
1]. At present, prestressed steel structures mainly include the cable dome structure [
2]; the wheel spoke structure [
3]; the cable truss structure, without the inner ring space [
4]; the cable net structure [
5]; the cable net shell structure [
6] and so on. Prestressed steel cable is widely used in cable dome structures, and the compression rod is less and shorter, so that it can give full play to the tensile strength of the steel, and the structural efficiency is very high. The cable dome structure was applied to the gymnastics hall of the Seoul Olympic Games in South Korea. Radiated cable truss is mainly composed of an external pressure ring, cable truss and an inner pull ring. The Jabir Ahmed Stadium in Kuwait is a single-layer spoke cable truss. The ringless prestressed cable-supported structure system includes two categories: the ringless prestressed cable-supported structure system, and the ringless prestressed string-supported structure system. The Beijing Olympic Badminton Pavilion applies a cableless dome structure. The Beijing Winter Olympic Speed Skating Hall is the largest single-layer orthogonal cable-net structure in the world. The accurate safety maintenance of long-span spatial structures is also an important standard for the measurement of the national construction technology and level. The mechanical properties of the components directly determine the safety performance of the structure [
7]. In the structure, different design parameters have also become important factors that affect the structural safety state. Since large-span spatial structures are mostly used in buildings of high importance, the safety performance of the structures is strictly required [
8,
9,
10].
Guo et al. [
11] investigated the effect of the initial cable length error in the prestressing state on the sensitivity of prestressed cables to the length error. By controlling the length error, the prestress level during the cable tensioning was effectively improved. In order to ensure the stability of the construction process of cable dome structures, Zhang et al. [
12] propose a joint-square double-strut cable dome structure. This structure effectively improves the safety control accuracy of the structure. Wang et al. [
13] analyzed the most active parameters (e.g., the cable force) in the construction process of spatial-structure prestressed cables in the whole process of prestressed cable tension. The safety of the construction process structure is ensured by analyzing the cable force. Arezki et al. [
14] investigated the effect of temperature variation on the safety performance of cable truss structures and cables. Basta et al. [
15] studied the quantitative evaluation of the decomposability of the cable-net structure on the basis of building information modeling (BIM).
By analyzing the research of the abovementioned scholars, new structural forms and efficient technical methods are studied for the safety maintenance of prestressed steel structures. With the development of a new generation of information technology and the promotion of industrial information systems, the application of intelligent technology to engineering construction has become a research hotspot. The application of DTs and intelligent algorithms in engineering practice can significantly improve the accuracy and intelligence of the structural performance analysis [
16,
17]. The integration of DTs and intelligent algorithms can realize the virtual simulation of the safety state of prestressed steel structures, and it can form the association mining between the design parameters and the mechanical parameters. Finally, the key factors that affect the structural safety performance were obtained in order to achieve the precise maintenance of structural safety.
DTs simulate and depict the state and the behavior of physical entities with a dynamic virtual model with high fidelity. As a link between the real physical world and the virtual digital space, it is the key enabling technology for the realization of intelligent construction [
18,
19]. Artificial intelligence has been applied in many disciplines and has formed a variety of intelligent algorithms [
20], which can extract high-level features from the original data for perceptual decision making, and can improve the objectivity and accuracy of the information evaluation [
21]. Liu et al. [
22] propose a DT-driven dynamic guidance method for fire evacuation. The method integrates the Dijkstra algorithm to realize the real-time acquisition of environmental information, the three-dimensional visualization of the indoor layout and evacuation path planning. Lu et al. [
23] integrated DTs, machine learning and data analysis to create a simulation model that represents and predicts the current and future conditions of physical counterparts. The integration of technology promotes the implementation and development of smart cities. Acharya et al. [
24] propose a visual positioning method to achieve the real-time and accurate positioning of indoor buildings. The 3D indoor model is used to eliminate the image-based indoor environment reconstruction requirements, and the deep convolution neural network is fused to fine-tune the image. Random forest (RF) has strong advantages in data processing, especially data classification. Bhuiyan et al. [
25] conducted a comprehensive assessment of 17 developed economies by using RF methods, a fuzzy decision-making test and assessment laboratory methods. For a structural health monitoring and reliability analysis, Liu et al. [
26] studied an uncertain dynamic load identification strategy combined with the Kalman filter algorithm and the RF model. Soleimani [
27] propose a machine learning algorithm (the RF ensemble learning method) to evaluate the importance of the modeling parameters for estimating the seismic demand. The results of the RF analysis are helpful to better understand the seismic performances of bridges. Therefore, the integration of DTs and intelligent algorithms provides new ideas and methods for the intelligent transformation and upgrading of the construction industry. Driven by DTs, the high-fidelity twin model of the structure is established. By extracting the structural safety indicators and their corresponding influencing factors in the twin model, the importance of various factors is analyzed by RF, which provides a reliable basis for structural safety control and maintenance.
In view of the demand for the intelligent analysis of the construction safety of prestressed steel structures, the advantages of DTs and intelligent algorithms are combined. Prestressed steel structures are mostly used in large public buildings, and their dynamic behavior needs real-time simulation. Therefore, building a high-fidelity simulation model based on DTs is a key step. In this study, an intelligent analysis method for the safety-influencing factors of prestressed steel structures that is based on DTs and random forest (RF) is proposed. Firstly, the construction method of the high-precision twinning model of the structure is formed on the basis of the weighted average method. By analyzing the fusion mechanism of DTs and RF, an intelligent analysis model of the structural-safety-influencing factors is formed from five dimensions. Driven by the analysis model, the correlation mechanism between the design parameters and the mechanical parameters is established. Design parameters are the influencing factors of the structural safety. The safety performance of the structure is reflected according to the mechanical parameters. The key design parameters that affect the structural safety performance are obtained by the classification of the mechanical parameters. When the structural mechanical parameters exceed the limit, the key design parameters can be corrected to accurately formulate the safety maintenance measures. The resulting theoretical method is applied to the analysis of the influencing factors of the safety performance of beam string structures. By analyzing the influence of the change in the design parameters on the mechanical parameters in the test structure, the correction of the key influencing factors can significantly improve the safety performance of the structure. This study provides a reliable basis for structural health monitoring by analyzing the key factors that affect the structural safety performance.
Author Contributions
Conceptualization, H.Z.; methodology, H.Z.; software, H.Z.; validation, H.Z. and Y.W.; writing—original draft preparation, H.Z.; writing—review and editing, H.Z.; project administration, H.Z.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The data presented in this study are available upon request from the corresponding author. The data are not publicly available because of confidentiality.
Acknowledgments
The authors would like to thank the South China University of Technology, Guangzhou, China, for their support throughout the research project.
Conflicts of Interest
The authors declare no conflict of interest. The funders had no role in the study’s design; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results.
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