Improved Hybrid Model for Predicting Concrete Crack Openings Based on Chaos Theory

Abstract

Conventional statistical models provide inaccurate predictions of concrete crack openings because they do not consider the nonlinear temperature response and the residual characteristics of concrete. To address this problem, this study introduces a nonlinear temperature factor and develops an improved statistical model of crack openings. The chaotic characteristics of residual time series of the improved statistical model are analyzed based on chaos theory and phase-space reconstruction theory. These theories are integrated with back-propagation (BP) artificial neural networks and genetic algorithms (GAs) to establish a GA-BP neural network model for predicting residuals. Finally, a hybrid model is developed for predicting the concrete crack opening behavior. The predictions of the conventional statistical model, the statistical model considering nonlinear temperature component, and the hybrid model are compared using the case study on the crack openings of a regulating sluice. The results show that the proposed hybrid model in this study for predicting concrete crack openings is significantly more accurate than the conventional statistical model and the statistical model considering nonlinear temperature component.

GAN-FDSR: GAN-Based Fault Detection and System Reconfiguration Method

“…A reasonable selection of the delay time and embedding dimension then allows reconstruction of the phase space with satisfactory adaptability, thereby producing the internal nonlinear mapping. By exploiting the properties of the Lyapunov exponent, this approach can recognize and make short-term predictions of chaotic time series [ 33 , 34 ].…”