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Damage Assessment of a Beam Using Artificial Neural Networks and Antiresonant Frequencies

机译：使用人工神经网络和反谐振频率损坏梁的损伤评估

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The main problem of damage assessment is how to ascertain the presence, location and severity of structural damage given the structure's dynamic characteristics. The most successful applications of vibration based damage assessment are model updating methods using global optimization algorithms. Nevertheless, these algorithms are very slow, and the damage assessment process is achieved through a costly and time-consuming inverse process. This is a problem for real-time health monitoring applications. Artificial Neural Networks (ANN) have been recently introduced as an alternative to model updating methods. Once a neural network has been properly trained, it can potentially detect, locate and quantify structural damage in a short period. Hence, it can be used for real-time damage assessment. The primary contribution of this research is the development of a real-time damage assessment algorithm using ANN and antiresonant frequencies. Antiresonant frequencies can be identified easier and more accurately than mode shapes and still provide the same information. An experimental beam with multiple damage scenarios is used to validate the approach.
机译：损害评估的主要问题是如何确定结构损坏的存在，位置和严重程度，因为结构的动态特性。基于振动的损伤评估最成功的应用是使用全局优化算法的模型更新方法。然而，这些算法非常慢，通过昂贵和耗时的逆过程实现损伤评估过程。这是实时健康监控应用的问题。最近已被引入人工神经网络（ANN）作为模型更新方法的替代方案。一旦神经网络被培训，它可能会在短时间内检测，定位和量化结构损坏。因此，它可用于实时伤害评估。本研究的主要贡献是使用ANN和反谐振频率进行实时损伤评估算法。可以比模式形状更容易且更准确地识别反谐振频率，并且仍然提供相同的信息。使用具有多种损坏方案的实验梁用于验证该方法。

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