Research on acoustic methods for buried PE pipeline detection based on LSTM neural networks Article Swipe
Yongsheng Qi
,
Xinhua Wang
,
Xuyun Yang
,
Tao Sun
,
Izzat Razzaq
,
Lin F. Yang
,
Yuexin Wang
,
Ghulam Rasool
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ad4dcd
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1361-6501/ad4dcd
As an essential component of urban infrastructure construction, polyethylene (PE) pipelines face the challenging task of underground detection due to the complex and dynamic nature of the subsurface environment, diverse installation paths, and the inherent insulating properties of PE materials. In order to address the non-excavation detection of buried PE pipelines, this paper proposes an acoustic method based on the long short-term memory (LSTM) neural network. The study begins by analyzing the propagation and reflection mechanisms of elastic waves in the pipe-soil coupling system, and a impact excitation source is designed to generate the excitation signal. After establishing the experimental environment and collecting experimental data, a comprehensive analysis is conducted, and the LSTM neural network is employed for data classification to determine the presence of buried PE pipelines. Through neural network training, accurate identification of the PE pipeline’s existence and prediction of its burial depth are achieved, providing an efficient and reliable solution for buried PE pipeline detection. The practical results demonstrate the significant application prospects of the combined acoustic method and LSTM neural network in buried PE pipeline detection. This research contributes a novel solution to the field of non-destructive PE pipeline detection, with both theoretical and practical implications.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1088/1361-6501/ad4dcd
- OA Status
- hybrid
- Cited By
- 4
- References
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- OpenAlex ID
- https://openalex.org/W4398144843
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4398144843Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1361-6501/ad4dcdDigital Object Identifier
- Title
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Research on acoustic methods for buried PE pipeline detection based on LSTM neural networksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-20Full publication date if available
- Authors
-
Yongsheng Qi, Xinhua Wang, Xuyun Yang, Tao Sun, Izzat Razzaq, Lin F. Yang, Yuexin Wang, Ghulam RasoolList of authors in order
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-
https://doi.org/10.1088/1361-6501/ad4dcdPublisher landing page
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YesWhether a free full text is available
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hybridOpen access status per OpenAlex
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https://doi.org/10.1088/1361-6501/ad4dcdDirect OA link when available
- Concepts
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Pipeline (software), Artificial neural network, Computer science, Deep neural networks, Artificial intelligence, Speech recognition, Pattern recognition (psychology), Programming languageTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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33Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.landing_page_url | https://doi.org/10.1088/1361-6501/ad4dcd |
| publication_date | 2024-05-20 |
| publication_year | 2024 |
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| corresponding_author_ids | https://openalex.org/A5003702556 |
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| corresponding_institution_ids | https://openalex.org/I37796252 |
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| sustainable_development_goals[0].display_name | Sustainable cities and communities |
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