<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In order to quickly and accurately detect the instability of foundation piles on concrete Bridges, an improved convolutional neural network based image recognition method for concrete bridge pile foundations was proposed. In order to improve the image quality, the entropy threshold method is used to process the image, and the two-channel convolutional neural network is designed to extract the image features fully. The improved traditional Relu activation function avoids model underfitting. Support vector machine (SVM) was used to replace Softmax classifier to improve computing efficiency. The experiment of bridge pile foundation image recognition shows that the improved convolutional neural network has significantly improved the recognition rate of the real bridge pile foundation. No matter how many degrees the image is rotated, the method in this article always maintains a high recognition rate, and the recognition rate does not fluctuate much, indicating that the algorithm in this article has good robustness to rotation and translation. In summary, the average recognition rate of the 5 groups was 96.26%. The feasibility of this method in identifying bridge pile foundation is proved.</p>
</abstract>

In order to quickly and accurately detect the instability of foundation piles on concrete Bridges, an improved convolutional neural network based image recognition method for concrete bridge pile foundations was proposed. In order to improve the image quality, the entropy threshold method is used to process the image, and the two-channel convolutional neural network is designed to extract the image features fully. The improved traditional Relu activation function avoids model underfitting. Support vector machine (SVM) was used to replace Softmax classifier to improve computing efficiency. The experiment of bridge pile foundation image recognition shows that the improved convolutional neural network has significantly improved the recognition rate of the real bridge pile foundation. No matter how many degrees the image is rotated, the method in this article always maintains a high recognition rate, and the recognition rate does not fluctuate much, indicating that the algorithm in this article has good robustness to rotation and translation. In summary, the average recognition rate of the 5 groups was 96.26%. The feasibility of this method in identifying bridge pile foundation is proved.

Analysis of bridge foundation pile detection based on convolutional neural network model

Applied Mathematics and Nonlinear Sciences

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

In order to quickly and accurately detect the instability of foundation piles on concrete Bridges, an improved convolutional neural network based image...

Analysis of bridge foundation pile detection based on convolutional neural network model

Pubblicato online: 06 giu 2023

Pagine: 2085 - 2094

Ricevuto: 18 lug 2022

Accettato: 29 nov 2022

DOI: https://doi.org/10.2478/amns.2023.1.00313

Parole chiave
bridge, Pile foundation, Testing, Technology, Convolutional neural network

© 2023 Aiping Chen, published by Sciendo

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

Analysis of bridge foundation pile detection based on convolutional neural network model

Pubblicato online: 06 giu 2023

Pagine: 2085 - 2094

Ricevuto: 18 lug 2022

Accettato: 29 nov 2022

DOI: https://doi.org/10.2478/amns.2023.1.00313

Parole chiavebridge, Pile foundation, Testing, Technology, Convolutional neural network

© 2023 Aiping Chen, published by Sciendo

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

Parole chiave
bridge, Pile foundation, Testing, Technology, Convolutional neural network