Enhancing water pressure sensing in challenging environments: A strain gage technology integrated with deep learning approach
The manuscript introduces a novel approach to design and construct a pore water pressure sensor utilizing strain gage technology integrated with deep learning principles. This sensor type is specifically tailored for measuring pressure at the vertex of pile bases in structures with substantial load-...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-02-01
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| Series: | Measurement + Control |
| Online Access: | https://doi.org/10.1177/00202940241256802 |
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| _version_ | 1841527674883801088 |
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| author | Thanh Q Nguyen Vu Ba Tu Duong N Nguyen |
| author_facet | Thanh Q Nguyen Vu Ba Tu Duong N Nguyen |
| author_sort | Thanh Q Nguyen |
| collection | DOAJ |
| description | The manuscript introduces a novel approach to design and construct a pore water pressure sensor utilizing strain gage technology integrated with deep learning principles. This sensor type is specifically tailored for measuring pressure at the vertex of pile bases in structures with substantial load-bearing capacity. While existing pressure sensors employing strain gage technology are available, this research addresses a unique measurement model suited for deep-water environments characterized by high corrosiveness and heavy loads. Consequently, the manuscript proposes design innovations aimed at optimizing the sensor’s form and dimensions to accommodate these demanding conditions. Computational simulations are conducted to perform relevant calculations, with results validated through rigorous analysis and experimentation against real-world datasets. Moreover, the study incorporates a pioneering deep learning-based data acquisition model to enhance output values, a feature currently underutilized in sensor technology. The findings demonstrate the viability of the proposed water pressure sensor model in various challenging working environments. This research underscores the potential for proactive manufacturing of sensors in diverse configurations, emphasizing adaptability and efficiency. |
| format | Article |
| id | doaj-art-5e80c7ec6a9341a9aa859e5c49547732 |
| institution | Kabale University |
| issn | 0020-2940 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Measurement + Control |
| spelling | doaj-art-5e80c7ec6a9341a9aa859e5c495477322025-01-15T09:04:20ZengSAGE PublishingMeasurement + Control0020-29402025-02-015810.1177/00202940241256802Enhancing water pressure sensing in challenging environments: A strain gage technology integrated with deep learning approachThanh Q Nguyen0Vu Ba Tu1Duong N Nguyen2Department of Railway-Metro Engineering, Ho Chi Minh City University of Transport, Ho Chi Minh City, VietnamHo Chi Minh City Department of Transport, Road Traffic Management Center, Ho Chi Minh City, VietnamFaculty of Engineering and Technology, Thu Dau Mot University, Binh Duong Province, VietnamThe manuscript introduces a novel approach to design and construct a pore water pressure sensor utilizing strain gage technology integrated with deep learning principles. This sensor type is specifically tailored for measuring pressure at the vertex of pile bases in structures with substantial load-bearing capacity. While existing pressure sensors employing strain gage technology are available, this research addresses a unique measurement model suited for deep-water environments characterized by high corrosiveness and heavy loads. Consequently, the manuscript proposes design innovations aimed at optimizing the sensor’s form and dimensions to accommodate these demanding conditions. Computational simulations are conducted to perform relevant calculations, with results validated through rigorous analysis and experimentation against real-world datasets. Moreover, the study incorporates a pioneering deep learning-based data acquisition model to enhance output values, a feature currently underutilized in sensor technology. The findings demonstrate the viability of the proposed water pressure sensor model in various challenging working environments. This research underscores the potential for proactive manufacturing of sensors in diverse configurations, emphasizing adaptability and efficiency.https://doi.org/10.1177/00202940241256802 |
| spellingShingle | Thanh Q Nguyen Vu Ba Tu Duong N Nguyen Enhancing water pressure sensing in challenging environments: A strain gage technology integrated with deep learning approach Measurement + Control |
| title | Enhancing water pressure sensing in challenging environments: A strain gage technology integrated with deep learning approach |
| title_full | Enhancing water pressure sensing in challenging environments: A strain gage technology integrated with deep learning approach |
| title_fullStr | Enhancing water pressure sensing in challenging environments: A strain gage technology integrated with deep learning approach |
| title_full_unstemmed | Enhancing water pressure sensing in challenging environments: A strain gage technology integrated with deep learning approach |
| title_short | Enhancing water pressure sensing in challenging environments: A strain gage technology integrated with deep learning approach |
| title_sort | enhancing water pressure sensing in challenging environments a strain gage technology integrated with deep learning approach |
| url | https://doi.org/10.1177/00202940241256802 |
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