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Digital twin technology has evolved from a theoretical concept to practical application, facilitating seamless data exchange between virtual and physical domains. Although there has been progress, the infrastructure industry, which is recognized for its intricate nature and the need for timely action, is still in the first phases of digital twin advancement. A significant obstacle in this field is the absence of established definitions and modeling standards, which impede the precise depiction of infrastructure systems. To address these challenges, this paper proposes a high-precision digital twin modeling method tailored for pumping stations. The method focuses on two key scenarios: first, we construct an overall digital twin model that contains both physical entities and operational processes of pumping stations; second, we design a modeling process applicable to pumping stations by analyzing the deficiencies of the existing standard system. Additionally, we selected the East–West Water Transfer Project in China as a case study to demonstrate the high-precision digital twin model of a pumping station. This model will include essential components, such as the modeling of pumping stations, the operational processes of pumping stations, and the modeling of system operation analysis. Serving as the database for the digital twin, it can complete the automatic inspection of the pumping station, optimization of scheduling, prediction and regulation of energy and carbon emissions, and visualization of results for display and other applications. The model realized the benefits of 100% automatic inspection rate, reduction of eight corresponding operating personnel, and comprehensive cost saving of RMB 2.25 million. The objective of this research is to narrow the divide between theoretical concepts and real-world implementations by pushing the boundaries of digital twin modeling and offering valuable insights for its utilization in the infrastructure industry. It establishes the foundation for progress in the field of digital twin technology in the specific context of intricate infrastructure projects. This project aims to improve the practicality of digital twin technology in real-world situations, namely in the infrastructure industry.

The construction process of cable net structure is complicated, which leads to the strong randomness of construction errors. The safety state of the cable net structure is very sensitive to construction errors. Obtaining the coupling relationship between construction errors and cable force response efficiently and accurately is critical to developing the construction technique of cable structures. This paper proposed an analysis method based on a genetic algorithm optimized back propagation neural network (GA-BPNN) to judge the influence of construction error on the cable force of single-layer orthogonal cable network structures. Taking the speed skating stadium of the 2022 Winter Olympic Games as the research object, this paper analyzed the structure form of the venue. According to the characteristics of cable network structure and GA-BPNN calculation, the principle of construction error analysis was put forward. The influence of construction errors of load-bearing cables and stable cables on cable force response was analyzed. The influence degree of different component errors on structural cable forces was obtained, and the most unfavorable key components were obtained. For the key components, the influence trend of different construction errors on cable force was analyzed, and the fitting formula was given. Driven by GA-BPNN, it can realize the analysis of structural and mechanical responses under the action of multi-type, multi-component, and multi-combination construction errors. The results show that the research method efficiently and accurately obtains the performance law of structural cable force under the influence of construction error, effectively predicts the influencing factors of the structural safety risk, and effectively avoids structural safety accidents caused by construction error. The construction errors analysis method based on GA-BPNN proposed in this paper can provide a reference for similar structural analysis and application.