Time-varying identification model for dam behavior considering structural reinforcement
Time-varying identification model for dam behavior considering structural reinforcement
复制标题
考虑结构加固的大坝行为时变识别模型
DOI：
10.1016/j.strusafe.2015.07.002
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发表时间：
2015-11
影响因子：
5.8
通讯作者：
Yang Meng
中科院分区：
文献类型：
--
作者：
Su Huaizhi;Wen Zhiping;Sun Xiaoran;Yang Meng
大坝工作性态识别和评价常采用结构反应与其影响因素之间的数学关系模型。在荷载、材料性质变化、结构加固等作用下，大坝工作性态表现出不确定性的变化特征。根据加固前后大坝性态的原型观测值、客观和主观不确定性信息，将支持向量回归（SVR）方法与贝叶斯方法相结合，建立了加固后大坝性态的时变识别模型。首先，建立了大坝性态识别的静态支持向量回归模型。其次，采用贝叶斯方法对静态辨识模型的计算结果进行动态调整。提出了一种确定大坝工作性态客观和主观不确定性的贝叶斯先验分布和似然函数的方法。强调大坝工作性态最新信息的重要性，提出了一种真实的实时更新贝叶斯参数的算法，以反映结构加固后大坝工作性态的特征变化。最后，以某实际大坝结构加固后的位移性状为例进行了分析。比较了经典统计模型、静态SVR模型和时变模型的辨识能力。结果表明，所提出的时变模型能提供更准确的拟合和预测结果，更适合于病险坝加固效果的评价。
Mathematical relationship model between structural response and its influence factors is often used to identify and assess dam behavior. Under the action of loads, changing material property, structural reinforcement and so on, dam behavior expresses the uncertain variation characteristics. According to the prototypical observations, objective and subjective uncertain information on dam behavior before and after structural reinforcement, support vector regression (SVR) method is combined with Bayesian approach to build the time-varying identification model for dam behavior after structural reinforcement. Firstly, a static SVR model identifying dam behavior is established. Secondly, Bayesian approach is adopted to adjust dynamically the calculated results of static identification model. A method determining the Bayesian prior distribution and likelihood function is developed to describe the objective and subjective uncertainty on dam behavior. Emphasizing the importance of recent information on dam behavior, an algorithm updating in real time the Bayesian parameters is proposed to reflect the characteristic change of dam behavior after structural reinforcement. Lastly, the displacement behavior of one actual dam undergoing structural reinforcements is taken as an example. The identification capabilities of classical statistical model, static SVR model and time-varying model are compared. It is indicated that the proposed time-varying model can provide more accurate fitted and forecasted results, and is more suitable to be used to evaluate the reinforcement effect of dangerous dam.
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影响因子：
5.5
作者：
Ardito, R.;Maier, G.;Massalongo, G.
通讯作者：
Massalongo, G.
影响因子：
5.8
作者：
W. Yan;L. Katafygiotis
通讯作者：
W. Yan;L. Katafygiotis
DOI：
10.1016/j.engappai.2011.09.020
发表时间：
2012-04
期刊：
Eng. Appl. Artif. Intell.
影响因子：
--
作者：
Chang Xu;D. Yue;Chengfa Deng
通讯作者：
Chang Xu;D. Yue;Chengfa Deng
影响因子：
5.4
作者：
Leung, SH;So, CF
通讯作者：
So, CF
DOI：
10.1111/j.1467-8667.2007.00499.x
发表时间：
2007-08
期刊：
Computer‐Aided Civil and Infrastructure Engineering
影响因子：
--
作者：
H. Su;Zhong Wu;Z. Wen
通讯作者：
H. Su;Zhong Wu;Z. Wen