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.
Highlights
Cracks are a common structural defect in hydraulic concrete. e operational behavior of macroscopic cracks in hydraulic concrete structures is directly related to the safety of the structure and is an important indicator of structural stability
E nonlinear method predicts the residual, which is generated by a phase-space reconstruction, with the result being a hybrid model based on a conventional statistical model [10, 11]. e results show that hybrid models based on residual prediction are more accurate than conventional models
The conventional statistical model for concrete crack opening is improved by considering the nonlinear temperature factor
Summary
Cracks are a common structural defect in hydraulic concrete. e operational behavior of macroscopic cracks in hydraulic concrete structures is directly related to the safety of the structure and is an important indicator of structural stability. Mathematical models based on conventional statistical models of concrete deformations are widely used in engineering monitoring and parameter inversion [1] Such models usually include temperature component, hydraulic component, and aging component. E nonlinear method predicts the residual, which is generated by a phase-space reconstruction, with the result being a hybrid model based on a conventional statistical model [10, 11]. Crack openings involve an irrecoverable part caused by nonlinear factors, suggesting that conventional statistical models cannot predict the deformation behavior as a function of water pressure and temperature. The improved statistical model is combined with the GA-BP neural network model using chaos theory and phasespace reconstruction theory, to develop a hybrid model for predicting concrete crack openings. A detailed case study on the crack openings of a regulating sluice is presented to illustrate the application of the hybrid model
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
