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Smart Structures and Systems Volume 33, Number 5, May 2024 , pages 325-332 DOI: https://doi.org/10.12989/sss.2024.33.5.325 |
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Carbonation depth prediction of concrete bridges based on long short-term memory |
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Youn Sang Cho, Man Sung Kang, Hyun Jun Jung and Yun-Kyu An
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| Abstract | ||
| This study proposes a novel long short-term memory (LSTM)-based approach for predicting carbonation depth, with the aim of enhancing the durability evaluation of concrete structures. Conventional carbonation depth prediction relies on statistical methodologies using carbonation influencing factors and in-situ carbonation depth data. However, applying in-situ data for predictive modeling faces challenges due to the lack of time-series data. To address this limitation, an LSTM-based carbonation depth prediction technique is proposed. First, training data are generated through random sampling from the distribution of carbonation velocity coefficients, which are calculated from in-situ carbonation depth data. Subsequently, a Bayesian theorem is applied to tailor the training data for each target bridge, which are depending on surrounding environmental conditions. Ultimately, the LSTM model predicts the time-dependent carbonation depth data for the target bridge. To examine the feasibility of this technique, a carbonation depth dataset from 3,960 in-situ bridges was used for training, and untrained timeseries data from the Miho River bridge in the Republic of Korea were used for experimental validation. The results of the experimental validation demonstrate a significant reduction in prediction error from 8.19% to 1.75% compared with the conventional statistical method. Furthermore, the LSTM prediction result can be enhanced by sequentially updating the LSTM model using actual time-series measurement data. | ||
| Key Words | ||
| bridge; concrete carbonation; deep learning; long short-term memory; prediction; time-series update | ||
| Address | ||
| (1) Youn Sang Cho, Man Sung Kang, Yun-Kyu An: Department of Architectural Engineering, Sejong University 209 Neungdong-ro, Gwangjin-gu, Seoul, Republic of Korea; (2) Hyun Jun Jung: Korea Authority of Land & Infrastructure Safety (KALIS), Jinju 52856, Republic of Korea. | ||