Intelligent bridge construction method driven by digital twin

In recent years, China’s infrastructure construction has expanded into remote, rugged mountainous regions, making bridge construction more challenging than ever before. These difficult environments make collecting data, simulating scenarios, and managing information effectively extremely challenging, resulting in the need for improved quality assurance during construction. To meet these challenges, the concept of digital twinning has emerged as a crucial tool for achieving intelligent construction. By mapping the attributes, structures, states, performances, and behaviors of real-world bridges onto a virtual twin, a highly realistic, interconnected digital representation of the bridge and its surroundings can be created. This virtual geographic environment provides a powerful means of comprehending and controlling the construction more precisely. This paper provides an in-depth analysis of the background and development trends of high-precision digital twinning for bridges and their environment as well as intelligent bridge construction. This paper introduces the theory of virtual geographic environments and digital twinning, and explores the use of dynamic data and simulation models to drive spatial modeling and virtual-real mapping throughout the bridge construction. The proposed approach is divided into four stages. First, a monitoring data correlation fusion model is constructed by integrating space, air, and ground monitoring technology. This step enables data perception of the physical space of the bridge construction and makes information on the bridge construction visible. Second, intelligent modeling methods are studied for the digital twinning scene of bridge construction. This step achieves a refined characterization and accurate description of the bridge construction environment using advanced modeling techniques. Third, dynamic simulation and intelligent prediction methods are established for bridge construction. This step utilizes the bridge construction digital twinning scene and combines it with multisource monitoring data to enable dynamic diagnosis, evaluation, and intelligent prediction of the states and quality of bridge construction. Fourth, the bridge data, simulation models, and modeling knowledge are integrated to establish an intelligent management mechanism for the entire bridge construction. This step enables actively controlling the construction, completing the iterative and interactive evolution, and achieving an intelligent closed-loop of “data perception-simulation analysis-intelligent prediction-optimization control” of bridge construction. To validate our methodology, a case study of a large bridge constructed in a complex, difficult mountainous area is presented. Our approach provides effective theoretical guidance and key technological support for the intelligent construction of large bridges in complex environments. © 2024 Science Press. All rights reserved.