Model updating method for hybrid simulation based on global sensitivity analysis

Yang

Earthquake Engineering and Resilience

et al. 2022

Hybrid simulation is a powerful testing method that combines numerical analysis and physical experiments through on-line interaction. The substructuring technology enables hybrid simulation to evaluate the structural performance with large-scale specimens. However, this technology also increases the complexity of specimen boundary conditions. Due to limited equipment resources of structural laboratories, not all the degrees of freedom of the boundary conditions can be realized during tests, which will induce error to the experimental restoring forces and decrease of accuracy of hybrid simulation. In this case, the hybrid simulation method based on restoring force correction was proposed to reduce this error. In this method, the error in experimental restoring forces were corrected using an auxiliary numerical model of the experimental substructure. The effectiveness of the proposed method was verified through both theoretical analysis and numerical examples. This method was then adopted to evaluate the seismic performance of a spatial reinforced concrete frame with buckling-restrained braces numerically. Results showed that the proposed method can enhance the accuracy of tests compared to conventional hybrid simulation, and keep computational efficiency in the meanwhile.

Section: Hybrid Simulation Methods Characterized By Incomplete Bounda...mentioning

confidence: 99%

Hybrid simulation method with restoring force correction for structural testing characterized by incomplete boundary conditions

Yang

Earthquake Engineering and Resilience

et al. 2022

“…Sudret [ 47 ] analytically derived the PCE-based global sensitivity indices formulae, and Cheng et al [ 48 ] and Wu et al [ 49 ] analytically derived global sensitivity indices formulae using SVR and RBF, respectively. In recent years, the application of global sensitivity analysis in the field of finite element model updating and damage identification for civil engineering structures has received much attention [ 50 , 51 , 52 ]. Surprisingly, global sensitivity analysis techniques to assess the long-term deflection affecting RC beams have not been developed or studied.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, the application of global sensitivity analysis in the field of finite element model updating and damage identification for civil engineering structures has received much attention [50][51][52]. Surprisingly, global sensitivity analysis techniques to assess the long-term deflection affecting RC beams have not been developed or studied.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Global Sensitivity Analysis of Long-Term Deflections in Reinforced Concrete Flexural Structures Using Surrogate Models

Dan

Yue

et al. 2023

Materials

Reinforced concrete (RC) is the result of a combination of steel reinforcing rods (which have high tensile) and concrete (which has high compressive strength). Additionally, the prediction of long-term deformations of RC flexural structures and the magnitude of the influence of the relevant material and geometric parameters are important for evaluating their serviceability and safety throughout their life cycles. Empirical methods for predicting the long-term deformation of RC structures are limited due to the difficulty of considering all the influencing factors. In this study, four popular surrogate models, i.e., polynomial chaos expansion (PCE), support vector regression (SVR), Kriging, and radial basis function (RBF), are used to predict the long-term deformation of RC structures. The surrogate models were developed and evaluated using RC simply supported beam examples, and experimental datasets were collected for comparison with common machine learning models (back propagation neural network (BP), multilayer perceptron (MLP), decision tree (DT) and linear regression (LR)). The models were tested using the statistical metrics R2, RAAE, RMAE, RMSE, VAF, PI, A10−index and U95. The results show that all four proposed models can effectively predict the deformation of RC structures, with PCE and SVR having the best accuracy, followed by the Kriging model and RBF. Moreover, the prediction accuracy of the surrogate model is much lower than that of the empirical method and the machine learning model in terms of the RMSE. Furthermore, a global sensitivity analysis of the material and geometric parameters affecting structural deflection using PCE is proposed. It was found that the geometric parameters are more influential than the material parameters. Additionally, there is a coupling effect between material and geometric parameters that works together to influence the long-term deflection of RC structures.

Earthquake Engineering and Resilience

“…Hybrid simulation treats critical and/or complex components that are difficult to numerically model as experimental substructures, while the rest of the system is often easy to model and analyze as numerical substructures 20,21 . Due to the advantages of hybrid simulation, many studies have focused on numerical substructure and experimental substructure boundary correction, 22 compensation techniques for reducing actuator delay, 23 identification and modification of structural parameters for hybrid simulation model update methods, 24–27 Internet‐integrated geo‐distributed real‐time hybrid simulation platform, 28 force and displacement hybrid control strategies for solving multifreedom strong coupling problems 29 . These have made the hybrid simulation technique widely used in engineering 30,31 to be applied to the assessment of seismic performance of more complex and large engineering structures 32 …”

Section: Introductionmentioning

confidence: 99%

A cross validation‐Voronoi and entropy based adaptive sampling strategy for uncertainty quantification through hybrid simulation

Chen

Hou

et al. 2022

Intensive and complex engineering models drive the need for computational affordability and accuracy. To obtain more accurate prediction, an effective sampling method is proposed and evaluated in this study by integrating CV (cross validation)-Voronoi (CV-V) and entropy (EP) strategies. The new CVV (CV-V) -DEP (Distance and EP) method uses CV-V to divide the sample space into Voronoi units and selects the unit with largest error. The candidate sample point with the largest EP and distance values in the selected unit is then selected. To verify the efficacy of the proposed method, CVV-DEP is first compared with traditional CV-V and CVV-EP (CV-V and EP) strategies for six nonlinear functions. Computational simulations are further conducted using the proposed method for uncertainty quantification through hybrid simulation of two different nonlinear systems. The proposed method is demonstrated to provide great potential for uncertainty quantification of civil engineering infrastructures through hybrid simulation.