Probabilistic updating of building models using incomplete modal data
Probabilistic updating of building models using incomplete modal data
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DOI：
10.1016/j.ymssp.2015.12.024
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发表时间：
2016-06-15
影响因子：
8.4
通讯作者：
Bueyuekoeztuerk, Oral
中科院分区：
文献类型：
--
作者：
Sun, Hao;Bueyuekoeztuerk, Oral
本文研究了利用不完全模态数据进行贝叶斯模型更新的一种新的概率策略。在更新过程中不需要实测模态量与预测模态量之间的直接模态匹配，通过模型约简实现。提出了一种自适应随机步长马尔可夫链蒙特卡罗技术，用于提取模型参数不确定性量化的样本。采用迭代改进约简系统技术对预测误差进行更新，并计算采样过程中的似然函数。由于模态量用于模型更新，因此首先进行模态识别，通过结构系统的加速度测量提取固有频率和模态振型。最后通过数值算例和实验算例验证了该算法的有效性：一个10层楼的综合数据和一个8层楼的振动台试验数据。结果表明，该算法对建筑物概率模型更新中参数不确定性的量化具有较好的鲁棒性和有效性。(C) 2016 Elsevier Ltd.版权所有。
This paper investigates a new probabilistic strategy for Bayesian model updating using incomplete modal data. Direct mode matching between the measured and the predicted modal quantities is not required in the updating process, which is realized through model reduction. A Markov chain Monte Carlo technique with adaptive random-walk steps is proposed to draw the samples for model parameter uncertainty quantification. The iterated improved reduced system technique is employed to update the prediction error as well as to calculate the likelihood function in the sampling process. Since modal quantities are used in the model updating, modal identification is first carried out to extract the natural frequencies and mode shapes through the acceleration measurements of the structural system. The proposed algorithm is finally validated by both numerical and experimental examples: a 10-storey building with synthetic data and a 8-storey building with shaking table test data. Results illustrate that the proposed algorithm is effective and robust for parameter uncertainty quantification in probabilistic model updating of buildings. (C) 2016 Elsevier Ltd. All rights reserved.