基于神经网络优化算法对连续刚构桥梁施工变形预测研究
Research on Construction Deformation Prediction of Continuous Rigid-Frame Bridges Based on Neural Network Optimization Algorithm
DOI: 10.12677/hjce.2026.153073, PDF,   
作者: 付民祖, 游其勇：武汉轻工大学土木工程与建筑学院，湖北 武汉
关键词: 深度学习；桥梁施工；变形监测；预测精度；Deep Learning； Bridge Construction； Deformation Monitoring； Prediction Accuracy
摘要: 本文意在对深度学习于连续刚构桥梁施工变形监测范畴的运用展开探讨，尤其是借助深度卷积神经网络优化算法达成高精度的预测。该项研究起初阐释了桥梁施工变形监测的必需性以及深度学习在该领域的应用现况，明晰了研究旨在凭借优化算法增进预测精度的目标，同时突出了此研究对于桥梁工程安全、施工管理以及技术进步的重大贡献。
Abstract: This paper aims to explore the application of deep learning in the field of construction deformation monitoring for continuous rigid-frame bridges, especially the achievement of high-precision prediction by virtue of the optimized algorithm of deep convolutional neural network. This study first expounds the necessity of bridge construction deformation monitoring and the current application status of deep learning in this field, clarifies the research objective of improving prediction accuracy by means of optimized algorithms, and highlights the significant contributions of this research to the safety, construction management and technological progress of bridge engineering.
文章引用：付民祖, 游其勇. 基于神经网络优化算法对连续刚构桥梁施工变形预测研究[J]. 土木工程, 2026, 15(3): 257-266. https://doi.org/10.12677/hjce.2026.153073

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