公路梁桥支座脱空度预测模型及优化算法应用研究

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振动与冲击 ›› 2024, Vol. 43 ›› Issue (15) : 218-227.

论文

马世纪1，2，乔兰1，2，邓乃夫1，2，李庆文1，2，陈璐3

作者信息 +

Prediction model and optimization algorithm application of support void degree for highway beam bridges

MA Shiji1,2, QIAO Lan1,2, DENG Naifu1,2, LI Qingwen1,2, CHEN Lu3

Author information +

文章历史 +

摘要

桥梁支座脱空度是桥梁结构的常见病害，对桥梁的安全性和使用寿命产生严重影响。因此，准确获取支座的脱空信息成为一个重要的研究课题。本研究提出了一种两阶段的支座脱空检测方法，第一阶段，基于柔度矩阵对角差值比指标（FDMCR）实现脱空支座定位，第二阶段采用BP(back propagation)神经网络进行支座脱空度的预测。研究针对单跨简支梁桥和三跨连续梁桥，通过有限元模拟实现了梁桥理论振型、自振频率的获取，并利用激光多普勒测振系统对室内单跨梁桥开展模态测试，获取实测数据，验证了所提方法的可行性。此外，针对单跨梁桥，分析了单目标粒子群优化算法、混合蛙跳算法、人工蜂群算法（artificial bee colony,ABC）优化算法和多目标非支配排序遗传算法（NSGA-Ⅱ）优化所构建BP模型的效果。研究结果表明，两阶段的方法可以有效的实现脱空定位和脱空度预测，而优化算法则能够提高预测模型的预测效果，尤其是ABC算法在单跨梁桥脱空度预测模型上表现出更低的预测误差，同时基于支座位置属性构建多目标函数能够在缓解单目标中各支座预测效果不均匀的问题。本研究对于预测公路梁桥支座脱空度具有重要的实际意义，并为类似问题的解决提供了新的思路和方法。

Abstract

Bridge bearing disengagement is a common structural defect in bridge structures that has a significant impact on the safety and service life of bridges. Therefore, accurately obtaining the disengagement information of the bearing becomes an important research topic. This study proposes a two-stage method for bearing disengagement detection. In the first stage, the bearing position is determined based on the Flexibility Matrix Diagonal Matrix Change Rate indicator (FDMCR). In the second stage, a BP neural network is used to predict the bearing disengagement degree. The study focuses on single span simply supported and triple span continuous beam bridges and uses finite element simulation to obtain the theoretical mode shapes and natural frequencies of the bridges. Modal testing is conducted on an indoor single-span beam bridge using a laser Doppler vibrometer system to obtain experimental data and validate the proposed method's feasibility. Therefore, for single-span beam bridges, the single-objective PSO/SFLA/ABC optimization algorithm and the multi-objective and non-dominant sorting genetic algorithm (NSGA-II) are applied to optimize the BP model and analysis. The research results show that the two-stage method can effectively achieve disengaged bearing position determination and degree prediction. The optimization algorithm can improve the prediction performance of the models, especially the ABC algorithm, which exhibits lower prediction errors in the clearance prediction model for single-span beam bridges. Additionally, constructing a multi-objective function based on the bearing position attributes can alleviate the issue of uneven prediction performance among the bearings in a single-objective scenario. This study is of great practical significance for predicting highway beam bridge bearing disengagement and provides new ideas and methods for similar problems.

导出引用

马世纪1，2，乔兰1，2，邓乃夫1，2，李庆文1，2，陈璐3. 公路梁桥支座脱空度预测模型及优化算法应用研究[J]. 振动与冲击, 2024, 43(15): 218-227

MA Shiji1,2, QIAO Lan1,2, DENG Naifu1,2, LI Qingwen1,2, CHEN Lu3. Prediction model and optimization algorithm application of support void degree for highway beam bridges[J]. Journal of Vibration and Shock, 2024, 43(15): 218-227

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