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篇名 |
Research on Recurrent Neural Network Based Crack Opening Prediction of Concrete Dam
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並列篇名 | Research on Recurrent Neural Network Based Crack Opening Prediction of Concrete Dam |
作者 | Jin Wang、Yongsong Zou、Peng Lei、R. Simon Sherratt、Lei Wang |
英文摘要 | The concrete dam can prevent flooding events and generate a vast amount of electricity, and it is a critical national infrastructure. However, it is easy to get cracked, and cracks usually pose significant potential threats to the safety of the concrete dam. Many researchers have done much research on dam crack protection and explored various rules to protect the concrete dam from cracks. However, the complex and irregular distribution of cracks make this task a very challenging research issue. In this paper, the feature importance of crack influencing factors is firstly analyzed. Then, the Recurrent Neural Network (RNN) is introduced for dam crack modeling. Next, the crack width of the Longyangxia Dam is modeled and tested by using the Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM). Finally, experimental results show that our proposed RNN-based method can effectively predict the crack change of the concrete dam. |
起訖頁 | 1161-1170 |
關鍵詞 | Concrete dam、Crack opening prediction、LSTM、Recurrent Neural Network |
刊名 | 網際網路技術學刊 |
期數 | 202007 (21:4期) |
出版單位 | 台灣學術網路管理委員會 |
DOI |
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