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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >A Machine Learning Approach to Model Interdependencies between Dynamic Response and Crack Propagation
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A Machine Learning Approach to Model Interdependencies between Dynamic Response and Crack Propagation

机译：一种机器学习方法来模拟动态响应与裂纹传播之间的相互依赖性

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摘要

Accurate damage detection in engineering structures is a critical part of structural health monitoring. A variety of non-destructive inspection methods has been employed to detect the presence and severity of the damage. In this research, machine learning (ML) algorithms are used to assess the dynamic response of the system. It can predict the damage severity, damage location, and fundamental behaviour of the system. Fatigue damage data of aluminium and ABS under coupled mechanical loads at different temperatures are used to train the model. The model shows that natural frequency and temperature appear to be the most important predictive features for aluminium. It appears to be dominated by natural frequency and tip amplitude for ABS. The results also show that the position of the crack along the specimen appears to be of little importance for either material, allowing simultaneous prediction of location and damage severity.
机译：工程结构中精确的损坏检测是结构健康监测的关键部分。已经采用各种非破坏性检查方法来检测损害的存在和严重程度。在本研究中，机器学习（ML）算法用于评估系统的动态响应。它可以预测系统的伤害严重程度，损害位置和基本行为。在不同温度下耦合机械负载下的铝和ABS的疲劳损伤数据用于培训模型。该模型表明，固有频率和温度似乎是铝的最重要的预测功能。它似乎由ABS的固有频率和尖端幅度占主导地位。结果还表明，裂纹沿着样本的位置似乎对任一材料具有很小的重要性，允许同时预测地点和损伤严重程度。

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