摘要
钢-混组合连续梁负弯矩区的受力性能一直是桥梁工程界关注的重点,现有研究主要针对混凝土桥面板受拉提出改进措施,却很少关注钢梁承压这一普遍现象。在钢-混组合梁中,钢梁(尤其是墩顶负弯矩区的钢底板处)承受着全桥最大的压应力。为此,该文以浙江某钢-混组合梁桥为工程背景,提出在施工阶段,在墩顶负弯矩区箱梁内部填充部分现浇混凝土,以减小钢底板承受的压应力。首先,建立该组合梁桥的Ansys有限元模型进行应力分析;然后,以填充混凝土沿纵桥向的长度和竖向厚度为变量参数,以钢梁底板峰值压应力最小值为优化目标,利用改进广义回归神经网络(IGRNN)进行尺寸参数寻优;最后,将预测的最优尺寸代入有限元模型验证准确性。研究结果表明:在墩顶负弯矩区箱梁内部浇筑填充一定量的混凝土,能显著减小钢梁底板所受到的压应力;同时,IGRNN可大幅提高尺寸寻优效率。对于案例组合梁桥,其预测出的最优尺寸及其对应的钢梁底板压应力值与有限元模型计算所得到的压应力值误差在5%以内,且优化后钢梁底板压应力相较于原结构降低了74.9%,优化效果显著。研究成果及方法可为同类型桥梁减小墩顶处钢梁底板压应力及相关问题提供参考。
The mechanical performance of the negative bending moment zone of steel‑concrete composite continuous girders has always been a focus of attention in bridge engineering.Existing research mainly proposes improvement measures for the tension of concrete bridge decks,while paying little attention to the common phenomenon of steel girders bearing pressure.The steel girders in steel‑concrete composite girders,especially the steel bottom plate in the negative bending moment area of the pier top,bear the highest compressive stress of the entire bridge.Therefore,by taking a steel‑concrete composite girder bridge in Zhejiang Province as the engineering background,it is proposed to partially fill the interior of the box girder in the pier-top negative bending moment zone with the cast-in-place concrete during the construction phase,so as to reduce the compressive stress borne by the steel bottom plate.Firstly,an Ansys finite element model of the composite girder bridge was established for stress analysis.Then,by taking the length and vertical thickness of the infill concrete along the longitudinal bridge direction as variable parameters and the minimum peak compressive stress in the steel girder bottom plate as the optimization objective,an improved generalized regression neural network(IGRNN)was used for dimensional optimization.Finally,the predicted optimal dimensions were substituted into the finite element model to verify the accuracy of the predictions.The results indicate that filling a certain amount of concrete into the box girder in the pier-top negative bending moment zone can significantly reduce the compressive stress in the steel bottom plate.Additionally,the IGRNN can greatly improve dimensional optimization efficiency.For the composite girder bridge examined,the compressive stress in the steel bottom plate corresponding to the predicted optimal dimensions is within 5%of the value calculated by the finite element model.The optimized compressive stress in the steel bottom plate is reduced by 74.9%compared to the original structure,indicating significant improvement.The findings and methods can provide a reference for reducing the compressive stress in the steel girder bottom plate at the pier top and related issues for similar bridges.
作者
王亚峰
刘建
柯红军
WANG Yafeng;LIU Jian;KE Hongjun(School of Civil and Environmental Engineering,Changsha University of Science&Technology,Changsha,Hunan 410114,China)
出处
《中外公路》
2025年第6期159-166,共8页
Journal of China & Foreign Highway
基金
国家自然科学基金资助项目(编号:51778069)。
关键词
桥梁工程
钢-混组合梁
有限元模型
改进广义回归神经网络
尺寸优化
bridge engineering
steel-concrete composite girder
finite element model
improved generalized regression neural network
size optimization
