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

<p>The development of the transportation industry can effectively accelerate the speed of economic development, in which bridges occupy an important position in transportation. The safety of the bridge design and construction process is a key part of bridge construction, and relying on human resources to investigate safety hazards greatly affects efficiency. In this paper, we combine deep learning technology and the BIM model to explore the synergistic effect of both on the quality management of the bridge construction phase and analyze the measured data. The results show that the application of the BIM model can improve efficiency by 35% compared with the traditional 2D CAD drawings, and the accuracy of data analysis can be improved by 12.51% and 14.26% for DNN and DBN models based on deep learning, respectively. The addition of the GSO algorithm leads to a further 19.19% improvement in the training accuracy of the coupled model. Finally, the optimization model was used to analyze the load factors and force majeure factors that affect the safety of the bridge, and to find the structural factors that affect the safety of the bridge design, which guides to ensure the quality of the bridge during the construction process.</p>
</abstract>

The development of the transportation industry can effectively accelerate the speed of economic development, in which bridges occupy an important position in transportation. The safety of the bridge design and construction process is a key part of bridge construction, and relying on human resources to investigate safety hazards greatly affects efficiency. In this paper, we combine deep learning technology and the BIM model to explore the synergistic effect of both on the quality management of the bridge construction phase and analyze the measured data. The results show that the application of the BIM model can improve efficiency by 35% compared with the traditional 2D CAD drawings, and the accuracy of data analysis can be improved by 12.51% and 14.26% for DNN and DBN models based on deep learning, respectively. The addition of the GSO algorithm leads to a further 19.19% improvement in the training accuracy of the coupled model. Finally, the optimization model was used to analyze the load factors and force majeure factors that affect the safety of the bridge, and to find the structural factors that affect the safety of the bridge design, which guides to ensure the quality of the bridge during the construction process.

Study on the application of deep learning technology and BIM model in the quality management of bridge design and construction stage

Applied Mathematics and Nonlinear Sciences

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

The development of the transportation industry can effectively accelerate the speed of economic development, in which bridges occupy an important position in...

Study on the application of deep learning technology and BIM model in the quality management of bridge design and construction stage

Online veröffentlicht: 30. Juni 2023

Seitenbereich: -

Eingereicht: 12. Aug. 2022

Akzeptiert: 09. Jan. 2023

DOI: https://doi.org/10.2478/amns.2023.1.00478

Schlüsselwörter
BIM mode.l, CATIA modeling, Deep learning, Bridge construction, Quality and safety

© 2023 Rui Huang et al., published by Sciendo

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

Study on the application of deep learning technology and BIM model in the quality management of bridge design and construction stage

Online veröffentlicht: 30. Juni 2023

Seitenbereich: -

Eingereicht: 12. Aug. 2022

Akzeptiert: 09. Jan. 2023

DOI: https://doi.org/10.2478/amns.2023.1.00478

SchlüsselwörterBIM mode.l, CATIA modeling, Deep learning, Bridge construction, Quality and safety

© 2023 Rui Huang et al., published by Sciendo

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

Schlüsselwörter
BIM mode.l, CATIA modeling, Deep learning, Bridge construction, Quality and safety