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基于卷积神经网络的结构力学机动分析
首发时间：2024-05-13
摘要：尝试利用卷积神经网络实现对平面杆系结构的机动分析。通过3dsmax动画软件、opencv模块，自建几何不变体系和几何可变体系的图像数据集。基于tensorflow及keras深度学习平台框架，构建和训练卷积神经网络模型。模型在训练集、验证集和测试集上均达到了100%的精度。在额外的测试集上的精度为93.7%，这表明卷积神经网络能够学习并掌握结构力学机动分析的相关知识。未来可通过数据集的多样性来提高模型的泛化能力，对于复杂结构其具有超越人类专家的潜力。卷积神经网络在结构力学机动分析领域具有一定的实用价值。利用可视化技术，揭示了卷积神经网络是如何学习和识别结构特征的。利用预训练的vgg16模型进行特征提取和微调，发现泛化能力不及作者自建的模型。
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kinematic analysis of structural mechanics based on convolutional neural network
abstract：attempt to use convolutional neural network to achieve kinematic analysis of plane bar structure. through 3dsmax animation software and opencv module, self-build image dataset of geometrically stable system and geometrically unstable system. we construct and train convolutional neural network model based on the tensorflow and keras deep learning platform framework. the model achieves 100% accuracy on the training set, validation set, and test set. the accuracy on the additional test set is 93.7%, indicating that convolutional neural network can learn and master the relevant knowledge of kinematic analysis of structural mechanics. in the future, the generalization ability of the model can be improved through the diversity of dataset, which has the potential to surpass human experts for complex structures. convolutional neural networks have certain practical value in the field of kinematic analysis of structural mechanics. using visualization technology, we reveal how convolutional neural network learns and recognizes structural features. using pre-trained vgg16 model for feature extraction and fine-tuning, we found that the generalization ability is inferior to the self-built model.
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