Deep learning model of concrete dam deformation prediction based on CNN. Issue 1 (October 2020)

• Record Type: Journal Article
• Authors: Xi, Wen; Yang, Jie; Song, Jintao; Qu, Xudong
• Is Part Of: IOP conference series. Volume 580:Issue 1(2020)
• Publication Date: 2020-10
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/580/1/012042
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: The concrete dam deformation prediction model is a key measure to predict the evolution of structural behavior and evaluate the safe service status. This paper uses open-source deep learning framework TensorFlow as the platform and uses the mature convolutional neural network technology in deep learning theory to establish the concrete dam deformation safety prediction model based on a deep learning. The application of engineering examples shows that the residual map, mean square error, and average percentage error are used as the model fitting and prediction accuracy evaluation standards. Compared with the shallow neural network model and the traditional Statistical model, the concrete dam deformation prediction model based on deep learning has higher prediction accuracy and more stable performance, providing a new method for concrete dam deformation monitoring.