Application of in-test model updating to earthquake structural assessment

Hazem H. Elanwar, Amr S. Elnashai

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


Analytical methods are frequently utilized for structural assessment due to their simplicity and cost-effectiveness. However, modeling of material inelasticity and geometric nonlinearity under reversed inelastic deformations is still very challenging and its accuracy is difficult to quantify. On the other hand, realistic experimental assessment is costly, time-consuming, and impractical for large or spatially extended structures. Hybrid simulation has been developed as an approach that combines the realism of experimental techniques with the economy of analytical tools. In hybrid simulation, the structural is divided into several modules such that the critical components are tested in the laboratory, while the rest of the structure is simulated numerically. The equations of motion solved in the computer enable the integration of the analytical and experimental components at each time increment. The objective of this article is to apply a newly developed identification and model updating scheme to acquire the material constitutive relationship from the physically tested specimen during the analysis to two complex hybrid simulation case studies. The identification scheme is developed and verified in a companion article, while the two experiments presented in this article are selected such that they address different structural engineering applications. First, a beam-column steel connection with heat treated beam section is analyzed. Afterwards, the response of a multi-bay concrete bridge is investigated. The results of these two examples demonstrate the effectiveness of model updating to improve the numerical model response as compared to the conventional hybrid simulation approaches.

Original languageEnglish (US)
Pages (from-to)62-79
Number of pages18
JournalJournal of Earthquake Engineering
Issue number1
StatePublished - Jan 2 2016

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality


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