改进粒子群优化的LSSVM结构损伤评定
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    摘要:

    为改善结构动力损伤的识别效果,提出了刚度变化指标构架下改进粒子群算法优化的最小二乘支持向量机的结构损伤评估方法。首先,通过由试验技术修正的有限元模型来计算刚度变化指标(stiffness variation index,简称SVI),并进行损伤定位;然后,在SVI基础上,利用改进粒子群算法优化最小二乘支持向量机的超参数,建立结构损伤评估优化模型,计算损伤大小。将该方法用于起重机主梁的损伤评定,研究结果表明,该方法具有较高的精度和效率,能准确地判断结构的实际性态,是一种有效的评估手段。

    Abstract:

    In order to improve the recognition effect of structure damage, an effective damage evaluation method, based on stiffness variation index (SVI) and least squares support vector machine optimized by improved particle swarm optimization algorithm, is proposed. The method adopts a finite element model modified by modal testing to calculate SVI. Firstly, the SVI data are investigated to detect damage locations. Secondly, these SVI data serve as the feature vectors to be input to least squares support vector machine for calculating the hyperparameters and optimization model of the structure damage evaluation is established so as to predict the size of damage. Finally, a case study on a crane girder damage evaluation demonstrates that the method can accurately determine the actual conditions of the girder structure, and it enhances identification accuracy and convergence rate. It is an effective health evaluation method

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  • 在线发布日期: 2012-07-19
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