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交通导致振动 1

信号平稳化 1

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非平稳环境振动下桥梁的阻尼识别

Sunjoong Kim, Ho-Kyung Kim

《工程（英文）》 2017年第3卷第6期页码 839-844 doi: 10.1016/j.eng.2017.11.002

摘要：

本研究着重于使用非平稳的环境振动数据来识别桥梁的阻尼比。我们通常使用基于输入信号的静态白噪声假设的运行模态分析（OMA）来识别使用中的桥梁的阻尼比。然而，大多数桥梁在使用时通常会受到非平稳激励，而违反这种基本假设会导致阻尼识别的不确定性。为了处理非平稳性，根据测量的响应来计算幅度调制函数，以消除由非平稳输入引起的整体趋势。采用自然激励技术（NExT）-特征系统实现算法（ERA）估算平稳过程中的阻尼比。为了提高基于OMA 的阻尼估计的准确性，在提取的平稳过程和非平稳数据之间进行比较分析，以评估消除非平稳性的效果。在信号平稳化后，第一竖向模态的阻尼比的平均值和标准偏差会减小。

关键词：阻尼运行模态分析交通导致振动非平稳信号平稳化调幅大桥斜拉悬索

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Trend detection and stochastic simulation prediction of streamflow at Yingluoxia hydrological station, Heihe River Basin, China

Chenglong ZHANG,Mo LI,Ping GUO

《农业科学与工程前沿（英文）》 2017年第4卷第1期页码 81-96 doi: 10.15302/J-FASE-2016112

摘要： Investigating long-term variation and prediction of streamflow are critical to regional water resource management and planning. Under the continuous influence of climate change and human activity, the trends of hydrologic time series are nonstationary, and consequently the established methods for hydrological frequency analysis are no longer applicable. Five methods, including the linear regression, nonlinear regression, change point analysis, wavelet analysis and Hilbert-Huang transformation, were first selected to detect and identify the deterministic and stochastic components of streamflow. The results indicated there was a significant long-term increasing trend. To test the applicability of these five methods, a comprehensive weighted index was then used to assess their performance. This index showed that the linear regression was the best method. Secondly, using the normality test for stochastic components separated by the linear regression method, a normal distribution requirement was satisfied. Next, the Monte Carlo stochastic simulation technique was used to simulate these stochastic components with normal distribution, and thus a new ensemble hydrological time series was obtained by combining the corresponding deterministic components. Finally, according to these outcomes, the streamflow at different frequencies in 2020 was predicted.

关键词： Monte Carlo nonstationary trend detection streamflow prediction decomposition and ensemble Yingluoxia