Advancing tunnel equipment maintenance through data-driven predictive strategies in underground infrastructure

Urban tunnel infrastructure, crucial for societal well-being, depends on reliable Tunnel Electromechanical Equipment (TEE), including ventilation, drainage, and lighting systems. A key challenge is these systems’ proactive and efficient maintenance, particularly under limited resources. This study i...

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Autores:

Zou, Xiaoping
Zeng , Jie
Yan, Gongxing
Mohammed, Khidhair Jasim
Abbas, Mohamed
Abdullah, Nermeen
Elattar, Samia
Amine Khadimallah, Mohamed
Toghroli, Sana
Escorcia-Gutierrez, José

Tipo de recurso:

Article of investigation

Fecha de publicación:

2024

Institución:

Corporación Universidad de la Costa

Repositorio:

REDICUC - Repositorio CUC

Idioma:

eng

OAI Identifier:

oai:repositorio.cuc.edu.co:11323/13357

Acceso en línea:

https://hdl.handle.net/11323/13357

Palabra clave:

Urban Tunnel Infrastructure
Tunnel Electromechanical Equipment (TEE)
Deep Learning
Tunnel Boring Machine (TBM) Performance
Att-GCN (Attention-based Graph Convolutiona Networks)
Predictive Maintenance

Rights

embargoedAccess

License

Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)