Research on Beam Bridge Structure Optimization and Intelligent Risk Warning System Based on Multi-Task Learning

This paper proposes a beam bridge structure optimization and intelligent risk warning system based on multitask learning. By combining multi-task learning technology, the system realizes the joint modeling of beam bridge structure design optimization and risk warning tasks, and can simultaneously perform structural optimization and health monitoring in the same framework. The system uses a deep learning framework to improve the knowledge transfer ability between tasks through shared representation, improves the computational efficiency in the optimization process, and enhances the accuracy of the warning system. This paper verifies the effect of the system in practical applications through experiments. The experimental results show that the model based on multi-task learning has achieved a performance improvement of about 12 % in beam bridge structure optimization, and the risk warning accuracy rate has reached <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{9 2 \%}$</tex>. Compared with the traditional single-task learning method, the model based on multi-task learning can more comprehensively consider multiple objectives and constraints in structural design, and the optimization effect is more significant. In addition, the intelligent risk warning module can timely identify potential risk points of the bridge and provide a scientific basis for maintenance decisions, thereby enhancing the long-term safety and operation efficiency of the beam bridge.