• Earthquakes and Structures
• /
• v.5 no.1
• /
• pp.111-127
• /
• 2013

This paper describes effects of infill walls on behavior of RC frame with low strength, including numerical modeling, modal testing and finite-element model updating. For this purpose full scaled, one bay and one story RC frame is produced and tested for plane and brick in-filled conditions. Ambient-vibration testis applied to identify dynamic characteristics under natural excitations. Enhanced Frequency Domain Decomposition and Stochastic Subspace Identification methods are used to obtain experimental dynamic characteristics. A numerical modal analysis is performed on the developed two-dimensional finite element model of the frames using SAP2000 software to provide numerical frequencies and mode shapes. Dynamic characteristics obtained by numerical and experimental are compared with each other and finite element model of the frames are updated by changing some uncertain modeling parameters such as material properties and boundary conditions to reduce the differences between the results. At the end of the study, maximum differences in the natural frequencies are reduced on average from 34% to 9% and a good agreement is found between numerical and experimental dynamic characteristics after finite-element model updating. In addition, it is seen material properties are more effective parameters in the finite element model updating of plane frame. However, for brick in-filled frame changes in boundary conditions determine the model updating process.

• Smart Structures and Systems
• /
• v.29 no.2
• /
• pp.321-336
• /
• 2022

Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the low-dimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.

• Structural Engineering and Mechanics
• /
• v.32 no.5
• /
• pp.667-683
• /
• 2009

FE model-based dynamic analysis has been widely used to predict the dynamic characteristics of civil structures. In a physical point of view, an FE model is unavoidably different from the actual structure as being formulated based on extremely idealized engineering drawings and design data. The conventional model updating methods such as direct method and sensitivity-based parameter estimation are not flexible for model updating of complex and large structures. Thus, it is needed to develop a model updating method applicable to complex structures without restriction. The main objective of this paper is to present the model updating method based on the hybrid genetic algorithm (HGA) by combining the genetic algorithm as global optimization method and modified Nelder-Mead's Simplex method as local optimization method. This FE model updating method using HGA does not need the derivation of derivative function related to parameters and without application of complicated inverse analysis methods. In order to allow its application on diversified and complex structures, a commercial FEA tool is adopted to exploit previously developed element library and analysis algorithms. Moreover, an output-level objective function making use of measurement and analytical results is also presented to update simultaneously the stiffness and mass of the analysis model. The numerical examples demonstrated that the proposed method based on HGA is effective for the updating of the FE model of bridge structures.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.34 no.1
• /
• pp.17-27
• /
• 2014

Numerical model became one of most important tools for identifying the state of an existing structure in accordance with development of numerical analysis techniques. A numerical model should be updated based on the measured responses from the existing structure to accurately use the model for identifying the state of the bridge and executing numerical experiments. In this study, a new model updating method based on repetition method without a differential function is introduced and applicability for high speed railway bridge is verified with dynamic stability analysis. A fine measurement based on measurement points roaming method was executed with an wireless measurement system for precise dynamic characteristic analysis. The natural frequencies and mode shapes were estimated by correlation analysis and a mode decomposition technique. An initial numerical model was constructed based on design drawings and the model have been updated in accordance with the introduced model updating method. The results from numerical experiment and field test have been compared for verifying the applicability of the model updating method. And the dynamic stability analysis has been executed to verify the usability of the updated numerical model and the model updating method. It seems that the model updating method can be used for various bridges after evaluation of applicability for other type bridges in further studies.

• Journal of KIBIM
• /
• v.8 no.4
• /
• pp.34-40
• /
• 2018

As the number of aged bridges increases, the development of efficient bridge maintenance techniques is becoming more important. Particularly, there have been many studies on digital-twin models of bridges for maintenance and SHM (Structure Health Monitering). However, in order to use the digital-twin model for maintenance of the bridge, the model updating process that matches the structural response between the real bridge and the digital-twin bridge model must be done. This study presents a model updating method that adjusts bridge's stiffness and boundary condition with genetic algorithm (GA) using static displacements and verified proposed updating method through field test on PSC girder bridge. This study also proposes a conceptual idea to construct an efficient bridge maintenance system by applying the updated numerical analysis model to the digital-twin model.

• Structural Engineering and Mechanics
• /
• v.62 no.3
• /
• pp.303-310
• /
• 2017

The model updating problems, which are to find the optimal approximation to the discrete quadratic model obtained by the finite element method, are critically important to the vibration analysis. In this paper, the structured model updating problem is considered, where the coefficient matrices are required to be symmetric and positive semidefinite, represent the interconnectivity of elements in the physical configuration and minimize the dynamics equations, and furthermore, due to the physical feasibility, the physical parameters should be positive. To the best of our knowledge, the model updating problem involving all these constraints has not been proposed in the existed literature. In this paper, based on the semidefinite programming technique, we design a general-purpose numerical algorithm for solving the structured model updating problems with incomplete measured data and present some numerical results to demonstrate the effectiveness of our method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.11a
• /
• pp.446-451
• /
• 2007

An Improved finite element model updating method to address the numerical difficulty associated with ill-conditioning and rank-deficiency. These difficulties frequently occur in model updating problems, when the identification of a larger number of physical parameters is attempted than that warranted by the information content of the experimental data. Based on the standard Bounded Variables Least-squares (BVLS) method, which incorporates the usual upper/lower-bound constraints, the proposed method is equipped with new constraints based on the correlation coefficients between the sensitivity vectors of updating parameters. The effectiveness of the proposed method is investigated through the numerical simulation of a simple framed structure by comparing the results of the proposed method with those obtained via pure BVLS and the regularization method. The comparison indicated that the proposed method and the regularization method yield approximate solutions with similar accuracy.

• Structural Engineering and Mechanics
• /
• v.70 no.3
• /
• pp.339-350
• /
• 2019

This research focuses on finite element model updating and damage assessment of structures at element level based on global nondestructive test results. For this purpose, an optimization system is generated to minimize the structural dynamic parameters discrepancies between numerical and experimental models. Objective functions are selected based on the square of Euclidean norm error of vibration frequencies and modal assurance criterion of mode shapes. In order to update the finite element model and detect local damages within the structural members, modern optimization techniques is implemented according to the evolutionary algorithms to meet the global optimized solution. Using a simulated numerical example, application of genetic algorithm (GA), particle swarm (PSO) and artificial bee colony (ABC) algorithms are investigated in FE model updating and damage detection problems to consider their accuracy and convergence characteristics. Then, a hybrid multi stage optimization method is presented merging advantages of PSO and ABC methods in finding damage location and extent. The efficiency of the methods have been examined using two simulated numerical examples, a laboratory dynamic test and a high-rise building field ambient vibration test results. The implemented evolutionary updating methods show successful results in accuracy and speed considering the incomplete and noisy experimental measured data.

• Earthquakes and Structures
• /
• v.22 no.1
• /
• pp.65-82
• /
• 2022

This paper presents a structural performance assessment of a historical masonry clock tower both using numerical and experimental process. The numerical assessment includes developing of finite element model with considering different types of soil-structure interaction systems, identifying the numerical dynamic characteristics, finite element model updating procedure, nonlinear time-history analysis and evaluation of seismic performance level. The experimental study involves determining experimental dynamic characteristics using operational modal analysis test method. Through the numerical and experimental processes, the current structural behavior of the masonry clock tower was evaluated. The first five experimental natural frequencies were obtained within 1.479-9.991 Hz. Maximum difference between numerical and experimental natural frequencies, obtained as 20.26%, was reduced to 4.90% by means of the use of updating procedure. According to the results of the nonlinear time-history analysis, maximum displacement was calculated as 0.213 m. The maximum and minimum principal stresses were calculated as 0.20 MPa and 1.40 MPa. In terms of displacement control, the clock tower showed only controlled damage level during the applied earthquake record.

• Smart Structures and Systems
• /
• v.26 no.1
• /
• pp.63-75
• /
• 2020

Substructure pseudo-dynamic hybrid simulation (SPDHS) combining the advantages of physical experiments and numerical simulation has become an important testing method for evaluating the dynamic responses of structures. Various parameter identification methods have been proposed for online model updating. However, if there is large model gap between the assumed numerical models and the real models, the parameter identification methods will cause large prediction errors. This study presents an ANN (artificial neural network) method based on forgetting factor. During the SPDHS of model updating, a dynamic sample window is formed in each loading step with forgetting factor to keep balance between the new samples and historical ones. The effectiveness and anti-noise ability of this method are evaluated by numerical analysis of a six-story frame structure with BRBs (Buckling Restrained Brace). One BRB is simulated in OpenFresco as the experimental substructure, while the rest is modeled in MATLAB. The results show that ANN is able to present more hysteresis behaviors that do not exist in the initial assumed numerical models. It is demonstrated that the proposed method has good adaptability and prediction accuracy of restoring force even under different loading histories.