| Autor: |
Zhao Shixiao, Shi Wei, Zhang Lei, Zhang Yan, Zhang Hailiang |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2444-8656 |
| DOI: |
10.2478/amns-2024-1092 |
| Popis: |
This study utilizes digital technology to monitor water pressure changes within basement flashings, facilitating targeted drainage operations. By implementing an active water discharge method, drainage holes are strategically placed around or beneath basements to decompress and reduce water levels, thereby alleviating hydrostatic pressure on underground structures. This approach helps prevent basement uplift. The paper addresses challenges such as high energy consumption and ample data storage in long-term monitoring, introducing an acceleration trigger module and a data correction algorithm based on an enhanced BP neural network. A WOA-BP neural network model was developed using historical data to monitor water pressure efficiently. Our findings indicate that at 0.076MPa, flashing connections begin to fail, progressing to shear damage at 0.085MPa. Consequently, to preserve basement integrity, it is crucial to activate complete drainage when monitored pressure exceeds 0.045MPa to maintain pressures below the critical threshold of 0.076MPa. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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