【摘要】 主要研究温度和风速两个环境因素对桥梁结构模态频率和阻尼比的影响,并建立环境因素与模态参数之间关系的数学模型。由于风速和温度的相关性较强,提出通过非线性主成分分析将相关的温度、风速转化为两个不相关的温度和风速主成分,从而将问题转化为研究温度和风速主成分对模态参数的影响;然后,采用非线性主成分分析对模态频率和阻尼比进行预处理,预处理后的数据隐含了模态频率和阻尼比与环境主成分之间的关系;其次,建立模态频率和阻尼比与环境主成分之间的神经网络模型;最后,对某一斜拉桥健康监测系统得到的模态频率、阻尼比和测试温度与风速进行分析。结果表明,环境因素对结构模态频率影响较大,神经网络模型可以较好地模拟环境主成分与模态参数之间的关系;对大部分模态的阻尼比,其与环境主成分的相关性不高。更多还原

【Abstract】 The dependence of natural frequencies, damping ratios on temperature and wind velocity are investigated. Because temperature and wind velocity measurement data may be closely correlated, the Nonlinear Principal Component Analysis (NLPCA) provides a powerful mathematical tool to transform the correlated variables (temperature and wind velocity) into a set of independent ones, the so called environmental principal components ( PC), namely the temperature PC and the wind velocity PC. The investigation of temperature and wind velocity effects on modal parameters can then be transformed into the study of influences of environmental principal components. The NLPCAis also employed as a signal pre-processing tool to distinguish predominant environmental effects on structural modal parameters from other environmental factors. The pre-processed data by NLPCA includes implicitly the relationship between modal parameters and environmental PCs. The Artificial Neural Network (ANN) technique is employed to model the relationship between the pre-processed modal parameters and the environmental PCs. Numerical studies are carried out using the in-situ monitoring data of the natural frequencies, damping ratios, temperatures and wind velocity of a cable-stayed bridge. The results indicate that environmental factors dramatically impact the natural frequencies and the ANN technique is capable of modeling the relationship between the modal frequency and the environmental PCs. For some modes, the effects of environmental factors variations on modal damping ratios are observed, while the damping ratios are also affected by other factors besides temperature and wind velocity.更多还原