Kinematic analysis of structural mechanics based on convolutional neural network

TL;DR

This paper demonstrates that convolutional neural networks can effectively perform kinematic analysis of plane bar structures, achieving high accuracy and revealing structural features, with potential to surpass human experts in complex cases.

Contribution

The study develops a CNN-based method for structural kinematic analysis, including dataset creation, model training, and visualization, showing promising accuracy and insights into feature learning.

Findings

CNN achieved 93.7% accuracy on test set.

Visualization reveals how CNN recognizes structural features.

Pre-trained VGG16 model has inferior generalization compared to self-built model.

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.