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The mechanical performance analysis of the members is the primary basis for evaluating the hoisting quality and safety of the valve hall grid structure. Ordinarily, manual analysis of monitoring data and on-site experience inspection are employed to structural judgment, but it is challenging to evaluate the correlation of the various members and the overall safety of a valve hall. In this paper, an intelligent correlation real-time analysis method based on a BPNN (Back Propagation Neural Network) for the mechanical properties of members is proposed to intelligently control the safety of valve hall grid structure hoisting. The correlation between the mechanical properties of multi-points in the grid structure is used to model the target measuring points. In addition, an intelligent real-time analysis system is used to manage and apply the mechanical property correlation and abnormality of members in real-time. Then, the model is applied to a super-span valve hall in South China, and the application effect is good. The mechanical property correlation model can accurately reflect the mechanical state of the valve hall grid structure hoisting process. Simultaneously, it can effectively pinpoint hidden dangers and locate risk members. It provides a new reference for the normal operation and maintenance of a super-span valve hall grid. [ABSTRACT FROM AUTHOR]

In the process of using a long-span converter station steel structure, engineering disasters can easily occur. Structural monitoring is an important method to reduce hoisting risk. In previous engineering cases, the structural monitoring of long-span converter station steel structure hoisting is rare. Thus, no relevant hoisting experience can be referenced. Traditional monitoring methods have a small scope of application, making it difficult to coordinate monitoring and construction control. In the monitoring process, many problems arise, such as complicated installation processes, large-scale data processing, and large-scale installation errors. With a real-time structural monitoring system, the mechanical changes in the long-span converter station steel structure during the hoisting process can be monitored in real-time in order to achieve real-time warning of engineering disasters, timely identification of engineering issues, and allow for rapid decision-making, thus avoiding the occurrence of engineering disasters. Based on this concept, automatic monitoring and manual measurement of the mechanical changes in the longest long-span converter station steel structure in the world is carried out, and the monitoring results were compared with the corresponding numerical simulation results in order to develop a real-time structural monitoring system for the whole long-span converter station steel structure's multi-point lifting process. This approach collects the monitoring data and outputs the deflection, stress, strain, wind force, and temperature of the long-span converter station steel structure in real-time, enabling real-time monitoring to ensure the safety of the lifting process. This research offers a new method and basis for the structural monitoring of the multi-point hoisting of a long-span converter station steel structure. [ABSTRACT FROM AUTHOR]