Time series estimation based on deep Learning for structural dynamic nonlinear prediction

This paper explores state-of-the-art deep learning techniques to analyse and predict structural dynamic nonlinear behaviours in civil engineering applications. In this paper, three methods, namely the piecewise linear least squares (PLLS) method, fully connected neural network (FCNN) method, and long short-term memory neural network (LSTMNN) method, are implemented and compared for structural dynamic response application under the condition of periodic, impact and seismic load. These methods are based on auto-regression model and time series estimation model, and still work when the structure is excited using immeasurable inputs. The dynamic response of a six-story steel frame analysed using the finite element method is used to validate these methods. Experimental results reveal that the PLLS and FCNN methods based on auto-regression model performs less well than the LSTMNN method based on time series estimation model, and it has a large the prediction peak mean square error. In addition, PLLS method is sensitive to noise, but FCNN and LSTMNN method based on deep learning are highly robust and anti-noise performance. These reveal the feasibility of the application of deep learning method in structural behaviours analysis in civil engineering.

Automated summary

This paper explores the use of deep learning techniques to analyze and predict structural dynamic nonlinear behaviors in civil engineering applications. Different methods are compared, with the LSTMNN method based on time series estimation model showing superior performance and robust anti-noise capabilities. The study demonstrates the feasibility of applying deep learning methods in analyzing structural behaviors in civil engineering.