Research on Deflection Risk Assessment for Steel Box Girder Bridges Based on GA-BP Neural Network

Abstract

Steel box girder bridges constitute a pivotal structural component in modern bridge engineering, confronting intricate mechanical environments and dynamic conditions during construction, with a particularly notable risk of deflection. Risk assessments predominantly rely on traditional mechanical analyses and empirical judgments, which need help to fully capture the dynamic construction changes and latent risks. This study introduces an innovative risk assessment methodology grounded in finite element analysis (FEA) and optimized by a genetic algorithm-enhanced back propagation neural network (GA-BP) to address these limitations. This approach entails constructing an FEA model to precisely simulate and predict the mechanical behavior during the construction phase, with field data validation ensuring the model’s accuracy. The GA-BP assessment model is established by further incorporating the genetic algorithm to optimize the BP neural network, enabling comprehensive, systematic, and efficient risk assessment. Through practical application case studies, this methodology demonstrates the ability to accurately identify the critical risk factors influencing deflection during the construction phase of steel box girder bridges, providing a scientific basis for construction control. This research holds significant theoretical value and practical significance, and it offers a scientific foundation for risk management, construction optimization, and safety assurance in future bridge engineering projects, thereby enhancing the overall quality and safety of bridges.