A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM
Joint Authors
Qu, Xudong
Yang, Jie
Chang, Meng
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-10-31
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Deformation is a comprehensive reflection of the structural state of a concrete dam, and research on prediction models for concrete dam deformation provides the basis for safety monitoring and early warning strategies.
This paper focuses on practical problems such as multicollinearity among factors; the subjectivity of factor selection; robustness, externality, generalization, and integrity deficiencies; and the unsoundness of evaluation systems for prediction models.
Based on rough set (RS) theory and a long short-term memory (LSTM) network, single-point and multipoint concrete dam deformation prediction models for health monitoring based on RS-LSTM are studied.
Moreover, a new prediction model evaluation system is proposed, and the model accuracy, robustness, externality, and generalization are defined as quantitative evaluation indexes.
An engineering project shows that the concrete dam deformation prediction models based on RS-LSTM can quantitatively obtain the representative factors that affect dam deformation and the importance of each factor relative to the effect.
The accuracy evaluation index (AVI), robustness evaluation index (RVI), externality evaluation index (EVI), and generalization evaluation index (GVI) of the model are superior to the evaluation indexes of existing shallow neural network models and statistical models according to the new evaluation system, which can estimate the comprehensive performance of prediction models.
The prediction model for concrete dam deformation based on RS-LSTM optimizes the factors that influence the model, quantitatively determines the importance of each factor, and provides high-performance, synchronous, and dynamic predictions for concrete dam behaviours; therefore, the model has strong engineering practicality.
American Psychological Association (APA)
Qu, Xudong& Yang, Jie& Chang, Meng. 2019. A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM. Journal of Sensors،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1187662
Modern Language Association (MLA)
Qu, Xudong…[et al.]. A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM. Journal of Sensors No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1187662
American Medical Association (AMA)
Qu, Xudong& Yang, Jie& Chang, Meng. A Deep Learning Model for Concrete Dam Deformation Prediction Based on RS-LSTM. Journal of Sensors. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1187662
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1187662