A robust prediction model for displacement of concrete dams subjected to irregular water‐level fluctuations. (6th May 2021)

• Record Type: Journal Article
• Authors: Ren, Qiubing; Li, Mingchao; Li, Heng; Song, Lingguang; Si, Wen; Liu, Han
• Other Names: Wan‐Wendner Roman guestEditor.; Bolander John guestEditor.; Bažant Zdeněk P. guestEditor.
• Is Part Of: Computer-aided civil and infrastructure engineering. Volume 36:Number 5(2021)
• Page Start: 577
• Page End: 601
• Publication Date: 2021-05-06
• Subjects: Civil engineering -- Data processing -- Periodicals; Computer-aided engineering -- Periodicals; 624.0285
• DOI: 10.1111/mice.12654
• Languages: English
• ISSNs: 1093-9687

Abstract

Abstract: Monitoring and predicting the displacement of concrete dams is one of the most crucial considerations for ensuring their long‐term safe operation. Most existing models are designed for dams subjected to common structural and environmental conditions, with little attention paid to atypical operational conditions, such as structural strengthening or sudden changes in the external environment. The motivation for this work is to develop a reliable prediction model for displacement behavior of concrete dams subjected to irregular upstream water‐level fluctuations. In our study, a reconstruction method for unevenly sampled time series is presented to analyze such data. Then, an improved model, factor weighted support vector regression (FWSVR), which differentiates various factors and their effects through a weighting matrix, is theoretically derived. The weights are determined by the RReliefF algorithm, and together with FWSVR form the final RReliefF‐based FWSVR (RFWSVR) model. In particular, the hyperparameters involved in the above modeling strategy are optimized by the Grey Wolf Optimizer. Eventually, the prediction robustness of the developed model was verified on the data from four representative monitoring points of a real‐world dam, where its accuracy was compared to classical dam behavior modeling methods, FWSVR models using other weighting methods, and an ensemble learning algorithm. Comparative evaluation of the performance of the different methods was conducted with the help of recognized statistical indices. The evaluation results show that the overall performance of the proposed RFWSVR model is optimal for the displacement prediction at the selected points when the dam case is subjected to irregular water‐level changes. This novel modeling approach may be generalized for modeling the evolution behavior of other civil or hydraulic structures.