Xing Tianye, Wang Yidan, Liu Yingxue, Wu Qi, Ma Rong, Shang Xiaoling
College of Basic Medicine, Changchun University of Chinese Medicine, Changchun 130117, China.
Department of Information Engineering, College of Humanities & Information Changchun University of Technology, Changchun 130122, China.
Appl Bionics Biomech. 2022 Aug 18;2022:4757620. doi: 10.1155/2022/4757620. eCollection 2022.
An intelligent health detection model is a new technology developed under an artificial intelligence environment, which is of great significance to the care of the elderly and other people who cannot take care of themselves. This paper comprehensively reviews the structural health monitoring method based on an intelligent algorithm, introduces the application model of neural networks in structural health monitoring in detail, and points out the shortcomings of using neural network technology alone. On the basis of previous work, the genetic algorithm and fuzzy theory were introduced as optimization tools, and a new neural network training algorithm was constructed by combining genetic algorithm, fuzzy theory, and neural network technology for structural health monitoring research. Aimed at the shortcoming of insufficient samples for training neural networks based on experimental data, this paper proposes to use the finite element method to construct a genetic fuzzy RBF neural network after corresponding processing of the first six-order bending modal frequencies of the structure, so as to realize the localization and detection of delamination damage of composite beams. . The experimental results of this paper show that the finite element method proposed in this paper can effectively carry out damage localization and damage assessment; compared with the traditional algorithm, the localization accuracy of this algorithm is improved by 20%, and the damage assessment performance is improved by 10%.
智能健康检测模型是在人工智能环境下发展起来的一项新技术，对老年人及其他无法自理的人群的护理具有重要意义。本文全面综述了基于智能算法的结构健康监测方法，详细介绍了神经网络在结构健康监测中的应用模型，并指出了单独使用神经网络技术的不足之处。在以往工作的基础上，引入遗传算法和模糊理论作为优化工具，将遗传算法、模糊理论与神经网络技术相结合，构建了一种新的神经网络训练算法用于结构健康监测研究。针对基于实验数据训练神经网络样本不足的缺点，本文提出在对结构的前六阶弯曲模态频率进行相应处理后，利用有限元方法构建遗传模糊径向基函数神经网络，以实现复合材料梁分层损伤的定位与检测。本文的实验结果表明，本文提出的有限元方法能够有效地进行损伤定位和损伤评估；与传统算法相比，该算法的定位精度提高了20%，损伤评估性能提高了10%。
