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 network has 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.
Abstract (translated)
尝试使用卷积神经网络来实现平面梁结构的运动分析。通过3dsMax动画软件和OpenCV模块，基于TensorFlow和Keras深度学习平台框架构建和训练几何稳定系统和高不稳定性系统图像数据集。该模型在训练集、验证集和测试集上的准确度均为100%。在附加测试集上的准确度为93.7%，表明卷积神经网络可以学习和掌握相关结构力学运动分析的知识。通过数据集的多样性来提高模型的泛化能力，该模型在复杂结构上的表现有可能超过人类专家。在结构力学运动分析领域，卷积神经网络具有一定的实际价值。通过可视化技术，我们揭示了卷积神经网络学习和识别结构特征的过程。使用预训练的VGG16模型进行特征提取和微调，我们发现自建模型的泛化能力要强于预训练模型。
URL
https://arxiv.org/abs/2405.02807
