Civil Engineering Structural Damage Identification by Integrating Benchmark Numerical Model and PCNN Network

Strengthening the real-time and accurate identification of civil structures is of great significance for ensuring the safety and service life of civil engineering projects. Therefore, in order to reduce the incidence of civil structural accidents in buildings and improve the safety, availability, and integrity of building structures, the study plans to adopt deep learning methods. In this study, the parallel Convolutional neural network covering one-dimensional and two-dimensional features is combined with the Benchmark numerical model to identify structural damage. This network structure can effectively utilize two parallel branches to extract response features at different scales and time domains, ensuring the coverage of damage feature recognition content to a certain extent. And the Benchmark numerical model can effectively improve the visualization of identification in the simulation of civil structures. By testing the fusion algorithm model, the results show that the network structure can effectively extract damage signal features, and its minimum classification loss value can approach 0.01; The maximum damage indicators on connecting beams, frame beams, and shear walls reached 0.472, 0.117, and 0.055, far higher than other comparative algorithms. The fusion algorithm has a recognition accuracy of over 85% for structural joint damage, showing good damage recognition performance. This fusion algorithm can effectively provide reference value and significance for the development of structural inspection and related risk prevention plans in civil engineering projects, and also provide new ideas and possibilities for relevant researchers to study the field of civil engineering.

Automated summary

Strengthening the real-time and accurate identification of civil structures is crucial for ensuring the safety of buildings. This study combines deep learning methods with a benchmark numerical model to identify structural damage using a parallel convolutional neural network. The results demonstrate that this network structure can effectively extract damage signal features and achieve high recognition accuracy.