Virtual modelling technique for geometric-material nonlinear dynamics of structures. (January 2023)

• Record Type: Journal Article
• Authors: Feng, Yuan; Wang, Qihan; Chen, Xiaojun; Wu, Di; Gao, Wei
• Is Part Of: Structural safety. Volume 100(2023)
• Publication Date: 2023-01
• Subjects: Virtual modelling -- Geometric nonlinearity -- Material nonlinearity -- Machine learning -- Dynamic analysis -- Uncertainty quantification; Structural stability -- Periodicals; Safety factor in engineering -- Periodicals; Reliability (Engineering) -- Periodicals; Constructions -- Stabilité -- Périodiques; Coefficient de sécurité en ingénierie -- Périodiques; Fiabilité -- Périodiques; 620.86
• DOI: 10.1016/j.strusafe.2022.102284
• Languages: English
• ISSNs: 0167-4730

Abstract

Highlights: Virtual modelling aided dynamic geometric-material nonlinear analysis is freshly developed. The novel surrogate model with T -spline kernel is used for a time-domain nonlinear estimation. Potential dynamic nonlinear responses can be predicted against the future forecasted information. The safety level of the nonlinear system can be efficiently quantified to the end-users. Abstract: This paper presents a virtual modelling technique for dynamic safety assessment of practical structures undergoing geometric and material blended nonlinearities. The variational inputs of systematic properties are treated within the 3D dynamic geometric-elastoplastic analyses. To circumvent numerical challenges in solving the coupled nonlinear problems, a freshly developed dynamic virtual modelling (DVM) technique is employed to determine the inherent relationship between the variational input data and the nonlinear structural response by using a new clustering based extended support vector regression (C-XSVR) algorithm with a novel T -spline polynomial kernel function. The virtual modelling models can be constructed at each time step within the Newmark's time integration procedure, which then can be used to predict deflection, force, and stress of the concerned structure at different periods. The DVM is capable of visibly forecasting potential large deformation nonlinear behaviours in an efficient manner, based on the explicit relationship functions. To demonstrate the accuracy and effectiveness of the proposed framework, nonlinear behaviours of two practical applications under future forecasted working conditions are predicted and validated in the numerical investigations.