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A New Damage Detection Method: Big Bang-Big Crunch (BB-BC) Algorithm

机译:一种新的损坏检测方法:大爆炸-大咬嚼(BB-BC)算法

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The present paper aims to explore damage assessment methodology based on the changes in dynamic parameters properties of vibration of a structural system. The finite-element model is used to apply at an element level. Reduction of the element stiffness is considered for structural damage. A procedure for locating and quantifying damaged areas of the structure based on the innovative Big Bang-Big Crunch (BB-BC) optimization method is developed for continuous variable optimization. For verifying the method a number of damage scenarios for simulated structures have been considered. For the purpose of damage location and severity assessment the approach is applied in three examples by using complete and incomplete modal data. The effect of noise on the accuracy of the results is investigated in some cases. A great unbraced frame with a lot of damaged element is considered to prove the ability of proposed method. More over BB-BC optimization method in damage detection is compared with particle swarm optimizer with passive congregation (PSOPC) algorithm. This work shows that BB-BC optimization method is a feasible methodology to detect damage location and severity while introducing numerous advantages compared to referred method.
机译:本文旨在探索基于结构系统振动动态参数特性变化的损伤评估方法。有限元模型用于在元素级别应用。考虑到单元刚度的降低对于结构损坏。针对连续变量优化,开发了一种基于创新的大爆炸-大挤压(BB-BC)优化方法来定位和量化结构受损区域的程序。为了验证该方法,已经考虑了许多模拟结构的损坏情况。为了进行损坏位置和严重性评估,在三个示例中使用完整和不完整的模态数据来应用该方法。在某些情况下,研究了噪声对结果准确性的影响。考虑到一个大的无支撑框架,其中包含很多损坏的元素,以证明所提出方法的能力。将更多的BB-BC优化方法与采用被动会聚(PSOPC)算法的粒子群优化器进行了比较。这项工作表明,BB-BC优化方法是一种可行的方法,可以检测损伤的位置和严重程度,同时与参考方法相比具有许多优势。

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