DRLSTM: A dual-stage deep learning approach driven by raw monitoring data for dam displacement prediction. (January 2022)

• Record Type: Journal Article
• Authors: Li, Mingchao; Li, Minghao; Ren, Qiubing; Li, Heng; Song, Lingguang
• Is Part Of: Advanced engineering informatics. Volume 51(2022)
• Publication Date: 2022-01
• Subjects: Dam displacement prediction -- Raw monitoring data -- Deep learning -- Residual convolutional neural network -- Long and short-term memory units; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2021.101510
• Languages: English
• ISSNs: 1474-0346

Abstract

Abstract: Dam displacement is an important indicator of the overall dam health status. Numerical prediction of such displacement based on real-world monitoring data is a common practice for dam safety assessment. However, the existing methods are mainly based on statistical models or shallow machine learning models. Although they can capture the timing of the dam displacement sequence, it is difficult to characterize the complex coupling relationship between displacement and multiple influencing factors (e.g., water level, temperature, and time). In addition, input factors of most dam displacement prediction models are artificially constructed based on modelers' personal experience, which lead to a loss of valuable information, thus prediction power, provided by the full set of raw monitoring data. To address these problems, this paper proposes a novel dual-stage deep learning approach based on one-Dimensional Residual network and Long Short-Term Memory (LSTM) unit, referred to herein as the DRLSTM model. In the first stage, the raw monitoring sequence is processed and spliced with convolution to form a combined sequence. After the timing information is extracted, the convolution direction is switched to learn the complex relationship between displacement and its influencing factors. LSTM is used to extract this relationship to obtain Stage I prediction. The second stage takes the difference between the actual measurement and the Stage I prediction as inputs, and LSTM extracts the stochastic features of the monitoring system to obtain Stage II prediction. The sum of two stage predictions forms the final prediction. The DRLSTM model only requires raw monitoring data of water level and temperature to accurately predict displacement. Through a real-world comparative study against four commonly used shallow learning models and three deep learning models, the root mean square error and mean absolute error of our proposed method are the smallest, being 0.198 mm and 0.149 mm respectively, while the correlation coefficient is the largest at 0.962. It is concluded that the DRLSTM model performance well for evaluating dam health status.