...
首页> 外文期刊>Applied Soft Computing >A two-step method for damage identification in moment frame connections using support vector machine and differential evolution algorithm
【24h】

A two-step method for damage identification in moment frame connections using support vector machine and differential evolution algorithm

机译:使用支持向量机和差分演化算法的时刻框架连接造成两步方法

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

The main aim of this study is to introduce a two-step method for damage identification in moment frame connections using a support vector machine (SVM) and differential evolution algorithm (DEA). In the first step, the potential location of damage in connections is determined through SVM leading to reducing the dimension of the search space. Then, the accurate location and precise amount of damage in connections are determined in the second step via DEA with a high speed. In order to simulate damage in connections, a moment frame is modeled through semi-rigid beam to column connections and the analytical model is used to randomly generate structures with damaged connections as data. Then, SVM is trained and tested using this data, to facilitate natural frequencies are considered as input data and the characteristics of damage in beam to column connections are considered as output data of the network. Now, the possible location of the damage in connections can be determined using the SVM trained. The accurate location and severity of damage are determined by DEA based on the prediction of SVM in the first step. In order to assess the efficiency of the proposed method, two numerical examples are considered with different damage cases and considering noise. A comparative study is also made to judge the performance of the method with that of a work available in the literature. The outcome shows the high efficiency of the proposed method to identify the location and severity of the damage in moment frame connections. (C) 2019 Elsevier B.V. All rights reserved.
机译:本研究的主要目的是引入使用支持向量机(SVM)和差分演进算法(DEA)的时刻框架连接中损坏损坏的两步方法。在第一步中,通过SVM确定连接中损坏的潜在位置,导致降低搜索空间的维度。然后,通过具有高速的DEA在第二步中确定连接的精确位置和精确的损坏量。为了模拟连接中的损坏,一瞬间帧通过半刚性光束模型到列连接,分析模型用于随机生成与数据损坏的连接的结构。然后,使用该数据训练和测试SVM,以促进自然频率被认为是输入数据,并且光束损坏的特性被认为是网络的输出数据。现在,可以使用培训的SVM确定连接中的损坏的可能位置。基于第一步中的SVM预测,DEA的准确位置和严重程度是由DEA确定的。为了评估所提出的方法的效率,用不同的损伤病例考虑两个数值例子,并考虑噪音。还制定了比较研究,以判断文献中可用的方法的方法。结果表明,所提出的方法的高效率识别时刻框架连接损坏的位置和严重程度。 (c)2019年Elsevier B.V.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号