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基于IGRNN的组合梁箱内填充混凝土尺寸优化
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长沙理工大学土木工程学院

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中图分类号:

U443.35

基金项目:

国家自然科学基金(编号:51778069)


Optimization of Filling Concrete Size in Composite Beam Box Based on IGRNN
Author:
Affiliation:

School of Civil Engineering, Changsha University of Science and Technology

Fund Project:

The National Natural Science Foundation of China(51778069)

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    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,现有的相关研究主要针对混凝土桥面板受拉提出改进措施,却很少考虑到钢梁承压这一普遍现象。钢混组合梁中的钢梁,尤其是墩顶负弯矩区的钢底板处,承受着几乎全桥最大的压应力。为此,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,用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IGRNN(改进广义回归神经网络)对其进行尺寸参数寻优,最后将预测的最优尺寸结果代入有限元模型,验证预测结果的准确性。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块,能显著减小钢梁底板所受到的压应力;同时,IGRNN也能极大程度地提高尺寸寻优效率,对案例组合梁桥,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,且优化后的钢梁底板压应力相较于原结构降低了74.9%,效果很好。此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。

    Abstract:

    The mechanical performance of the negative bending moment zone of steel-concrete composite continuous beams has always been a focus of attention in bridge engineering. Existing research mainly proposes improvement measures for the tension of concrete bridge decks, but rarely considers the common phenomenon of steel beams bearing pressure. The steel beams in the steel-concrete composite beam, especially the steel bottom plate in the negative bending moment area of the pier top, bear almost the maximum compressive stress of the entire bridge. Therefore, taking a steel-concrete composite beam bridge in Zhejiang as the engineering background, it is proposed to fill a part of the cast-in-place concrete inside the negative bending moment area of the pier top box girder during the construction phase to reduce the compressive stress borne by the steel bottom plate. Firstly, an Ansys finite element model of the composite beam bridge is established for stress analysis. Then, the length and vertical thickness of the filled concrete along the longitudinal direction of the bridge are used as variable parameters, and the minimum peak compressive stress of the steel beam bottom plate is taken as the optimization objective. IGRNN (Improved Generalized Regression Neural Network) is used to optimize its size. Finally, the predicted optimal size result is substituted into the finite element model to verify the accuracy of the prediction results. The research results indicate that pouring and filling a certain amount of concrete blocks into the negative bending moment area of the pier top inside the box beam can significantly reduce the compressive stress on the bottom plate of the steel beam; At the same time, IGRNN can greatly improve the efficiency of size optimization. For the case of composite beam bridges, the predicted optimal size and corresponding steel beam bottom plate compressive stress value are within 5% of the compressive stress value calculated by the finite element model, and the optimized steel beam bottom plate compressive stress is reduced by 74.9% compared to the original structure, with good results. This research achievement and method can provide reference for reducing the compressive stress of the steel beam bottom plate at the pier top and related issues for similar bridges.

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  • 收稿日期:2023-11-21
  • 最后修改日期:2024-03-13
  • 录用日期:2024-03-20
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