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<p>Structural health monitoring is a research hotspot in engineering, and structural damage identification is one of the key problems in structural health monitoring research. This paper proposes a study on structural damage identification and localization based on artificial neural networks, and for the problem that the learning convergence speed of the BP neural network is too slow, a genetic algorithm is used to optimize the update of weights and thresholds during training and learning. In the finite element simulation, the structure's primary and secondary damage are taken as input nodes, and the optimized GA-BP neural network is used for training and identification. For the localization recognition of the primary damage of the structure, the maximum recognition relative errors of both the BP neural network and the GA-BP neural network did not exceed 5%, but the latter's accuracy was 2.63% higher than that of the former. For the localization recognition of secondary damage, the GA-BP neural network can effectively recognize 90% of the samples. The artificial neural network-based structural damage recognition localization has high recognition efficiency and accuracy, which is conducive to improving the robustness of the structural damage recognition system and is of significant help to real-time structural health monitoring.</p>
</abstract>

Structural health monitoring is a research hotspot in engineering, and structural damage identification is one of the key problems in structural health monitoring research. This paper proposes a study on structural damage identification and localization based on artificial neural networks, and for the problem that the learning convergence speed of the BP neural network is too slow, a genetic algorithm is used to optimize the update of weights and thresholds during training and learning. In the finite element simulation, the structure's primary and secondary damage are taken as input nodes, and the optimized GA-BP neural network is used for training and identification. For the localization recognition of the primary damage of the structure, the maximum recognition relative errors of both the BP neural network and the GA-BP neural network did not exceed 5%, but the latter's accuracy was 2.63% higher than that of the former. For the localization recognition of secondary damage, the GA-BP neural network can effectively recognize 90% of the samples. The artificial neural network-based structural damage recognition localization has high recognition efficiency and accuracy, which is conducive to improving the robustness of the structural damage recognition system and is of significant help to real-time structural health monitoring.

Research on structural damage identification and localization based on artificial neural network

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Structural health monitoring is a research hotspot in engineering, and structural damage identification is one of the key problems in structural health...

Research on structural damage identification and localization based on artificial neural network

Publicado en línea: 09 jun 2023

Páginas: -

Recibido: 01 sept 2022

Aceptado: 13 dic 2022

DOI: https://doi.org/10.2478/amns.2023.1.00375

Palabras clave
BP neural network, Genetic algorithm, Structural damage identification, Accuracy, Finite element simulation

© 2023 Yuhang Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Research on structural damage identification and localization based on artificial neural network

Publicado en línea: 09 jun 2023

Páginas: -

Recibido: 01 sept 2022

Aceptado: 13 dic 2022

DOI: https://doi.org/10.2478/amns.2023.1.00375

Palabras claveBP neural network, Genetic algorithm, Structural damage identification, Accuracy, Finite element simulation

© 2023 Yuhang Liu, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Palabras clave
BP neural network, Genetic algorithm, Structural damage identification, Accuracy, Finite element simulation