This paper investigates the application of one-dimensional convolutional neural networks (1D CNNs) for damage detection in standardized structural components, addressing the limitations of existing methods that typically focus on individual structures. To assess damage detection capabilities across populations, particularly in situations where no prior damage data from the structures are available, a 1D CNN is trained and validated on simulated damage data and then tested using experimental data from a population of beams. The data consists of vibration responses from five aluminum beams with damage introduced by rectangular notches at different locations. A finite element model that incorporates beam-to-beam variability through random coefficients is employed to generate the training data. To optimize feature extraction across the population, the number and size of kernels in the convolutional layers are fine-tuned using Particle Swarm Optimization (PSO). The method’s robustness is evaluated by comparing its damage detection accuracy with additional deep learning models and conventional machine learning approaches that rely on manual feature extraction. The proposed method achieves near-perfect accuracy—up to 100% in training and 99.6% in validation using simulated data, outperforming the other models in accuracy and computational efficiency. However, when applied to the experimental population, its accuracy drops, particularly in identifying the precise damage state. Nevertheless, it successfully distinguishes between damaged and undamaged samples with an accuracy of 76.3%. These results extend the applicability of 1D CNNs for detecting damage across experimental populations of similar structural components, requiring only a single CNN to identify the presence of damage across the population.

中文翻译：

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使用 PSO 优化的卷积神经网络对一小群名义相等的光束进行结构损伤检测


本文研究了一维卷积神经网络 （1D CNN） 在标准化结构组件中损伤检测的应用，解决了通常关注单个结构的现有方法的局限性。为了评估跨种群的损伤检测能力，特别是在没有来自结构的先前损伤数据的情况下，一维 CNN 在模拟损伤数据上进行训练和验证，然后使用来自梁群的实验数据进行测试。该数据包括来自五根铝梁的振动响应，这些铝梁的损伤是由不同位置的矩形槽口引入的。采用通过随机系数结合梁到梁可变性的有限元模型来生成训练数据。为了优化整个群体的特征提取，使用粒子群优化 （PSO） 对卷积层中内核的数量和大小进行了微调。通过将其损伤检测精度与其他深度学习模型和依赖于手动特征提取的传统机器学习方法进行比较，来评估该方法的稳健性。所提出的方法实现了近乎完美的准确率——在训练中高达 100%，在使用模拟数据进行验证时高达 99.6%，在准确率和计算效率方面优于其他模型。然而，当应用于实验群体时，其准确性会下降，尤其是在识别精确的损伤状态时。尽管如此，它还是成功地区分了受损和未受损的样品，准确率为 76.3%。 这些结果扩展了 1D CNN 在检测相似结构成分的实验群体中的损伤的适用性，只需要一个 CNN 即可识别整个群体中存在的损伤。