摘要
为科学地监测和预测实际运营中大型阻尼器的性能演化,将斜拉桥主梁在自然环境中的纵向振动等效为单自由度体系随机激励下响应问题,推导出白噪声激励下响应功率谱.通过在桥上和阻尼器上安装低频加速度传感器,在3 a时间内3次对实桥纵向振动的重复试验,并采用离散小波变换方法提取主梁纵向低频振动成分,按最小二乘法原理与白噪声激励下的响应功率谱公式拟合,近似得出纵向振动的系统阻尼比参数.试验结果表明:斜拉桥体系简化后,能够准确识别纵向振动的系统阻尼比;相比传统滤波方法,离散小波对信号分解重构后能够更加准确地保留低频成分;系统阻尼比衰减表明苏通大桥的塔梁阻尼器性能在缓慢退化.定期运用该方法对大型的塔梁阻尼器进行现场试验,能够在不中断交通的情况下更好地指导阻尼器的养护管理,保障大跨度斜拉桥的正常运营.
To scientifically monitor and predict the performance of large-scale tower-beam dampers during operation, the longitudinal vibration of the cable-stayed bridge main beam in natural environment was made equivalent to the problem of the single degree of freedom ( SDOF) system under random vibration in this study, and the power spectrum under white noises was deduced. Several acceleration gauges were mounted on the bridge and dampers and field tests were performed three times in three years repetitively. Discrete wavelet transform ( DWT) was applied to extract the low frequency vibration data of the main beam. With the measured acceleration power spectrum curve fitted to the formula in SDOF system by the least square method, the damping ratio of the tower- beam damper was obtained approximately. Results showed that after the simplification of the cable-stayed bridge, the system damping ratio of longitudinal vibration of the main beam can be precisely identified. Compared with traditional low-pass filters, the DWT method performs better in conserving the low frequency component. The attenuation of the system damping ratio indicates that the tower-beam damper installed at Sutong Bridge degenerates in a slow process. The field monitor of the tower-beam damper should be carried out regularly, which is beneficial for maintenance guidance and the management of dampers without interrupting traffic, which guarantees the normal operation of long-span cable-stayed bridges.
作者
马如进
葛纯熙
胡晓红
MA Rujin;GE Chunxi;HU Xiaohong(Department of Bridge Engineering,Tongji University,Shanghai 200092,China)
出处
《哈尔滨工业大学学报》
EI
CAS
CSCD
北大核心
2019年第3期73-79,共7页
Journal of Harbin Institute of Technology
基金
国家自然科学基金(51678437)
贵州省科技重大专项([2016]3013)