DOAJ Open Access 2023

Discovery and Discrimination of Bridge Engineering Safety Issues by BIM Virtual Scene Combined with Robotic Mapping

Guilin Gong Shaowu Zeng Jingjing Gao Qingliang Zhang Xinli Wang

Abstrak

All along, safety accidents in the field of bridge construction have continued to arise, costing society's development financial and material resources and taking away people’s personal safety, turning into an urgent need for safety managers in the field to solve the subject research. Researchers look forward to continued innovative exploration to find new ways of working safely and using them in the construction industry. On this basis, BIM virtual scenes are introduced to create a universal scene specification dataset in the field of bridge construction and a dataset of unsafe potential hazard characteristics in the field of bridge construction. Nanobots are artificial intelligence systems built to simulate biological nanomachine constructions in life activities and important biological events in life processes. In this paper, BIM virtual scenarios incorporating intelligent nanomaterial robot mapping technology are migrated to bridge engineering safety management. According to the collected construction scene images and BIM (building information model) virtual scenes, build the construction scene dataset, use intelligent robots to obtain construction scenes, and load them into the trained construction on-the-spot neural network modeling to complete the bridge construction scene identification safety discrimination to select more comprehensive and more stringent management solutions as safety management personnel management methods on-the-spot.

Penulis (5)

G

Guilin Gong

S

Shaowu Zeng

J

Jingjing Gao

Q

Qingliang Zhang

X

Xinli Wang

Format Sitasi

Gong, G., Zeng, S., Gao, J., Zhang, Q., Wang, X. (2023). Discovery and Discrimination of Bridge Engineering Safety Issues by BIM Virtual Scene Combined with Robotic Mapping. https://doi.org/10.1155/2023/3028505

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Informasi Jurnal
Tahun Terbit
2023
Sumber Database
DOAJ
DOI
10.1155/2023/3028505
Akses
Open Access ✓