Hybrid simulation has been effectively utilized to assess structural response subjected to intense dynamic loads. The process comprises dividing the structure into experimental and numerical modules. The experimental modules represent the critical components responses, which cannot be idealized reliably through analytical approaches. The responses of the different modules are combined through a stepwise integration scheme. In conventional hybrid simulations, the number of experimental components is restricted by the capacity of the test facility; usually 1-3 components, and the numerical simulation does not benefit from the information acquired from the tested component during the analysis. In this article, a framework is proposed to identify the material constitutive relationship from the tested component(s) and to update the corresponding numerical parts that share close characteristics with the physical tests. Optimization tools and neural networks are presented as alternatives for the identification procedure; the framework is however extendable and scalable. The communication protocol between the different structural components is also discussed within the proposed framework. Several analytical examples are presented to prove the feasibility of the presented framework, while experiments are used to verify the process in a companion article.
All Science Journal Classification (ASJC) codes
- Civil and Structural Engineering
- Building and Construction
- Geotechnical Engineering and Engineering Geology