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

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

doi:10.1016/j.compgeo.2024.106532

Computers and Geotechnics

2024

DOI: 10.1016/j.compgeo.2024.106532

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

Xiaoping Zou,

Jie Zeng,

Gongxing Yan

et al.

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Segmentation of tunnel water leakage based on a lightweight DeepLabV3+ model

Wang,

Hou,

Chen

et al. 2024

Meas. Sci. Technol.

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The accurate and efficient detection of water leakage with complex backgrounds is crucial for the safety of metro operations. A lightweight segmentation method for metro tunnel water leakage based on transfer learning is proposed. Firstly, this is based on the Deeplabv3+ model and adopts MobileNetv3-Large as the backbone feature extraction network, which significantly reduces the network parameters and improves the detection speed; secondly, it incorporates the efficient channel attention mechanism, which enables the model to adaptively adjust the weights of the channel features and capture the inter-channel relationships in the image, which significantly improves the model’s ability for feature extraction ability; furthermore, for the problem of severe imbalance between positive and negative samples in the dataset, the recognition accuracy of complex samples is increased by optimizing the loss function; finally, the training method of transfer learning is utilized to solve the problem of scarcity of water leakage dataset, and to improve the model’s accuracy and generalization ability. The results show that the model has more significant detection accuracy and segmentation speed advantages than today’s mainstream semantic segmentation model. With strong generalization ability in complex environments (e.g., low illumination and multiple obstructions), the model can be used for intelligent operation and maintenance in metro tunnel projects.

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Segmentation of tunnel water leakage based on a lightweight DeepLabV3+ model

Wang,

Hou,

Chen

et al. 2024

Meas. Sci. Technol.

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

Cited by 1 publication

References 79 publications

Segmentation of tunnel water leakage based on a lightweight DeepLabV3+ model

Segmentation of tunnel water leakage based on a lightweight DeepLabV3+ model

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