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Computers and Concrete Volume 31, Number 5, May 2023 , pages 405-417 DOI: https://doi.org/10.12989/cac.2023.31.5.405 |
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Estimation of reaction forces at the seabed anchor of the submerged floating tunnel using structural pattern recognition |
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Seongi Min, Kiwon Jeong, Yunwoo Lee, Donghwi Jung and Seungjun Kim
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| Abstract | ||
| The submerged floating tunnel (SFT) is tethered by mooring lines anchored to the seabed, therefore, the structural integrity of the anchor should be sensitively managed. Despite their importance, reaction forces cannot be simply measured by attaching sensors or load cells because of the structural and environmental characteristics of the submerged structure. Therefore, we propose an effective method for estimating the reaction forces at the seabed anchor of a submerged floating tunnel using a structural pattern model. First, a structural pattern model is established to use the correlation between tunnel motion and anchor reactions via a deep learning algorithm. Once the pattern model is established, it is directly used to estimate the reaction forces by inputting the tunnel motion data, which can be directly measured inside the tunnel. Because the sequential characteristics of responses in the time domain should be considered, the long short-term memory (LSTM) algorithm is mainly used to recognize structural behavioral patterns. Using hydrodynamics-based simulations, big data on the structural behavior of the SFT under various waves were generated, and the prepared datasets were used to validate the proposed method. The simulation-based validation results clearly show that the proposed method can precisely estimate time-series reactions using only acceleration data. In addition to real-time structural health monitoring, the proposed method can be useful for forensics when an unexpected accident or failure is related to the seabed anchors of the SFT. | ||
| Key Words | ||
| deep learning, forensic; LSTM; pattern recognition; reaction forces; structural health monitoring; submerged floating tunnel | ||
| Address | ||
| Seongi Min, Kiwon Jeong, Donghwi Jung and Seungjun Kim: School of Civil, Environmental and Architectural Engineering, Korea University, Seoul 02841, Korea Yunwoo Lee: School of Civil Engineering, Chungbuk National University, Cheongju 28644, Korea | ||