Application of In-Test Model Updating to Earthquake Structural Assessment

Abstract

Analytical methods are frequently utilized for structural assessment due to their simplicity and costeffectiveness. 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.

Parameter identification for on‐line model updating in hybrid simulations using a gradient‐based method

“…They can feature the desirable simulation of the observed kinematic and isotropic hardening phenomena. [14][15][16][17][18][19][20] In hybrid simulation, also called hybrid testing, [21][22][23][24][25][26][27][28][29] the target structure can be partitioned into multiple substructures. When constitutive models such as the Bouc-Wen model and the 2-surface model are adopted in nonlinear structural response simulations, the lack of relations between the control parameters and the mechanical properties may result in difficulties in finding the parameter values.…”

“…Conventional model fitting is done using the trial and error method. [14][15][16][17][18][19][20] 2.2 | Analytical model for the BRB In order to improve the efficiency of model fitting, various techniques for parameter identification were developed in the past years.…”

“…To investigate the seismic behavior of a structure with distributed plasticity developed on BRBs with hybrid simulation, several physical substructures corresponding to the BRBs are required. From the literature, [14][15][16][17][18][19][20] it should be noted that there is a key assumption in model updating. Thus, the benefit of the hybrid simulation may diminish in such applications.…”

Transfer function‐based Bayesian damage detection under seismic excitation

“…Artificial neural networks could be utilized for nonlinear model updating, but in some cases, only consideration of dynamic responses for model updating may lead to incorrect parameter predictions, and both static and dynamic responses should be used together in model updating . Moreover, modeling of material inelasticity and geometric nonlinearity under reversed inelastic deformations is still challenging, and its accuracy is difficult to quantify . It is shown that the uncertainty due to nonlinear effects is hardly comprehensible in comparison with other measurement uncertainties in dynamic experiments .…”

Hybrid testing with model updating on steel panel damper substructures using a multi‐axial testing system

“…However, if only one physical specimen is constructed and tested while all other relevant elements are numerically simulated, the advantages of HS would be significantly diminished due to the inaccurate modeling of the not‐well‐understood substructures with numerical approach. To address this issue, the development of the online model updating (OMU) technique has gained increasing attention in recent years . One effective approach for OMU is adopted in a series of hybrid tests presented in this paper.…”