A new distributed time series evolution prediction model for dam deformation based on constituent elements. (January 2019)

• Record Type: Journal Article
• Authors: Li, Mingchao; Shen, Yang; Ren, Qiubing; Li, Heng
• Is Part Of: Advanced engineering informatics. Volume 39(2019)
• Page Start: 41
• Page End: 52
• Publication Date: 2019-01
• Subjects: Dam deformation prediction -- Distributed modeling -- Time series decomposition -- Constituent elements -- Data cleaning; Computer-aided engineering -- Periodicals; Engineering -- Data processing -- Periodicals; 620.00285
• DOI: 10.1016/j.aei.2018.11.006
• Languages: English
• ISSNs: 1474-0346

Abstract

Highlights: Three constituent elements of deformation series are extracted using improved STL. Distributed modeling method is adopted based on multi-model fusion strategy. Other functions of DDPM include interpolation and anomaly detection. The validity of DDPM is verified by practical engineering application. DDPM is fit for multiple monitoring points due to self-optimization capability. Abstract: The construction of a mathematical model to predict dam deformation can provide an important basis for judging its operating condition. Due to several time-varying factors, such as water level, temperature and aging, the dam prototype monitoring data series shows non-linear and non-stationary features, which increase the difficulty of dam deformation prediction and analysis. For this reason, a novel distributed deformation prediction model (DDPM), which combines transformation ideology with structured methodology, is proposed to improve the reliability of deformation prediction. DDPM starts by considering three constituent elements of dam deformation series using time series decomposition, and a multi-model fusion strategy is adopted. The trend, periodic and remainder components are separately predicted through constructing the optimal fitting, weight window and remainder generation sub-models. The three predicted components are aggregated as the final predicted output based on an underlying data model. The accuracy and validity of DDPM are verified and evaluated by taking a concrete dam in China as an example and comparing prediction performance with well-established models. The simulation results indicate that DDPM can not only extract more potential data features to obtain good deformation prediction effect, it can also reduce the complexity of mathematical modeling. Furthermore, two other functions of DDPM, including missing value handling and anomaly detection, are also discussed, which ultimately realize the integrated configuration of deformation prediction and data cleaning. The new model provides an alternative method for prediction and analysis of dam deformation and other structural behavior.