Identification of Structural Systems Under Tri-Directional Seismic Excitations Using a Statistically Refined Bouc-Wen Model

2007 ◽  
Author(s):  
Jeng-Wen Lin ◽  
Chong-Shien Tsai ◽  
Wen-Shin Chen
Keyword(s):  
Earthquake Engineering ◽  
Sampling Error ◽  
Structural Parameters ◽  
Statistical Significance ◽  
Structural Systems ◽  
Seismic Excitations ◽  
Estimated Parameters ◽  
Restoring Forces ◽  
System Vibration ◽  
Axial Interaction

This paper presents the identification of structural systems under tri-directional seismic excitations using a statistically refined Bouc-Wen model of tri-axial interaction. Through limited vibration measurements in the National Center for Research on Earthquake Engineering in Taiwan, the Bouc-Wen model has been statistically and repetitively refined using the 95% confidence interval of the estimated structural parameters so as to determine their statistical significance in a multiple regression setting. The effectiveness of the refined model has been shown considering the effects of the sampling error, of the coupled restoring forces in tri-directions, and of the under-over-parameterization of structural systems. Sifted and estimated parameters such as the stiffness, and its corresponding natural frequency, resulting from the methodology developed in this paper are carefully observed for system vibration control.

Health Diagnosis of Structural Systems Using a Repetitive Model Updating Approach

2007 ◽  
Author(s):  
Jeng-Wen Lin ◽  
Chong-Shien Tsai ◽  
Chih-Wei Huang
Keyword(s):  
Confidence Interval ◽  
Model Updating ◽  
Structural Parameters ◽  
Statistical Significance ◽  
Structural Systems ◽  
Least Squares Regression ◽  
Accurate Identification ◽  
Statistical Confidence ◽  
Multivariable Polynomial ◽  
Health Diagnosis

This paper proposes a statistical confidence interval based model updating approach for the health diagnosis of structural systems subjected to seismic excitations. The proposed model updating approach uses the 95% confidence interval of the estimated structural parameters to determine their statistical significance in a least-squares regression setting. When the parameters’ confidence interval covers the “null” value, it is statistically sustainable to truncate such parameters. The remaining parameters will repetitively undergo such parameter sifting process for model updating until all the parameters’ statistical significance cannot be further improved. This newly developed model updating approach is implemented for the developed series models of multivariable polynomial expansions: the linear, the Taylor series, and the power series model, leading to a more accurate identification as well as a more controllable design for system vibration control.

scholarly journals Repetitive Identification of Structural Systems Using a Nonlinear Model Parameter Refinement Approach

2009 ◽  
Vol 16 (3) ◽  
pp. 229-240 ◽  
Author(s):  
Jeng-Wen Lin ◽  
Hung-Jen Chen
Keyword(s):  
Power Series ◽  
Confidence Interval ◽  
Nonlinear Model ◽  
Health Monitoring ◽  
Structural Parameters ◽  
Statistical Significance ◽  
Structural Systems ◽  
Statistical Regression ◽  
Model Refinement ◽  
Model Parameter

This paper proposes a statistical confidence interval based nonlinear model parameter refinement approach for the health monitoring of structural systems subjected to seismic excitations. The developed model refinement approach uses the 95% confidence interval of the estimated structural parameters to determine their statistical significance in a least-squares regression setting. When the parameters' confidence interval covers the zero value, it is statistically sustainable to truncate such parameters. The remaining parameters will repetitively undergo such parameter sifting process for model refinement until all the parameters' statistical significance cannot be further improved. This newly developed model refinement approach is implemented for the series models of multivariable polynomial expansions: the linear, the Taylor series, and the power series model, leading to a more accurate identification as well as a more controllable design for system vibration control. Because the statistical regression based model refinement approach is intrinsically used to process a “batch” of data and obtain an ensemble average estimation such as the structural stiffness, the Kalman filter and one of its extended versions is introduced to the refined power series model for structural health monitoring.

scholarly journals Structural Reliability Sensitivities under Nonstationary Random Vibrations

2013 ◽  
Vol 2013 ◽  
pp. 1-21 ◽  
Author(s):  
Rita Greco ◽  
Francesco Trentadue
Keyword(s):  
Structural Reliability ◽  
Structural Parameters ◽  
Time Integration ◽  
Structural Response ◽  
Integration Scheme ◽  
Structural Systems ◽  
Frame Building ◽  
Time Integration Scheme ◽  
Noise Input ◽  
The Time Domain

Response sensitivity evaluation is an important element in reliability evaluation and design optimization of structural systems. It has been widely studied under static and dynamic forcing conditions with deterministic input data. In this paper, structural response and reliability sensitivities are determined by means of the time domain covariance analysis in both classically and nonclassically damped linear structural systems. A time integration scheme is proposed for covariance sensitivity. A modulated, filtered, white noise input process is adopted to model the stochastic nonstationary loads. The method allows for the evaluation of sensitivity statistics of different quantities of dynamic response with respect to structural parameters. Finally, numerical examples are presented regarding a multistorey shear frame building.

Seismic Behaviour of Composite Panel Composed of Laminated Wood and Bearing Glass - Experimental Investigation

2013 ◽  
Vol 778 ◽  
pp. 698-705 ◽  
Author(s):  
Lidija Krstevska ◽  
Ljubomir Tashkov ◽  
Vlatka Rajčić ◽  
Roko Zarnic
Keyword(s):  
Earthquake Engineering ◽  
Experimental Testing ◽  
Shaking Table ◽  
Composite Panel ◽  
Seismic Excitations ◽  
Seismic Behaviour ◽  
Time Duration ◽  
Earthquake Motion ◽  
Scientific Project ◽  
Real Earthquake

Within the bilateral scientific project between the Institute of Earthquake Engineering and Engineering Seismology - UKIM-IZIIS, St. Cyril and Methodius University, Skopje, Republic of Macedonia and the Civil Engineering Faculty, University of Zagreb, Croatia, experimental testing of full scale composite timber-glass innovative panels was carried out on the seismic shaking table at IZIIS for the purpose of defining their behaviour and stability under real earthquake conditions. The seismic excitations selected for the shake-table testing of the model were four representative accelerograms recorded during the following earthquakes: El Centro, Petrovac, Kobe and Friuli. The idea was to investigate the seismic behavior of the model under several types of earthquakes, considering their different frequency content, peak acceleration and time duration. The performed tests showed clearly the behaviour of the composite panels and the failure mechanism under strong earthquake motion.

Statistical Tests of Type I Error

2021 ◽  
pp. 90-120
Author(s):  
Charles Auerbach
Keyword(s):  
Control Charts ◽  
Sampling Error ◽  
Statistical Tests ◽  
Statistical Significance ◽  
Data Sets ◽  
Chi Square ◽  
Statistical Process ◽  
Incorrect Decision ◽  
Type 1 Error ◽  
Statistical Process Control Charts

This chapter covers tests of statistical significance that can be used to compare data across phases. These are used to determine whether observed outcomes are likely the result of an intervention or, more likely, the result of sampling error or chance. The purpose of a statistical test is to determine how likely it is that the analyst is making an incorrect decision by rejecting the null hypothesis, that there is no difference between compared phases, and accepting the alternative one, that true differences exist. A number of tests of significance are presented in this chapter: statistical process control charts (SPCs), proportion/frequency, chi-square, the conservative dual criteria (CDC), robust conservative dual criteria (RCDC), the t test, and analysis of variance (ANOVA). How and when to use each of these are also discussed, and examples are provided to illustrate each. The method for transforming autocorrelated data and merging data sets is discussed further in the context of utilizing transformed data sets to test of Type 1 error.

scholarly journals Experimental Validation of Optimal Parameter and Uncertainty Estimation for Structural Systems Using a Shuffled Complex Evolution Metropolis Algorithm

2019 ◽  
Vol 9 (22) ◽  
pp. 4959 ◽  
Author(s):  
Hesheng Tang ◽  
Xueyuan Guo ◽  
Liyu Xie ◽  
Songtao Xue
Keyword(s):  
Optimal Parameter ◽  
Structural Parameters ◽  
Metropolis Algorithm ◽  
Shaking Table ◽  
Parameter Distribution ◽  
Physical Parameters ◽  
Structural Systems ◽  
Model Errors ◽  
Nonlinear Structural ◽  
Shuffled Complex Evolution

The uncertainty in parameter estimation arises from structural systems’ input and output measured errors and from structural model errors. An experimental verification of the shuffled complex evolution metropolis algorithm (SCEM-UA) for identifying the optimal parameters of structural systems and estimating their uncertainty is presented. First, the estimation framework is theoretically developed. The SCEM-UA algorithm is employed to search through feasible parameters’ space and to infer the posterior distribution of the parameters automatically. The resulting posterior parameter distribution then provides the most likely estimation of parameter sets that produces the best model performance. The algorithm is subsequently validated through both numerical simulation and shaking table experiment for estimating the parameters of structural systems considering the uncertainty of available information. Finally, the proposed algorithm is extended to identify the uncertain physical parameters of a nonlinear structural system with a particle mass tuned damper (PTMD). The results demonstrate that the proposed algorithm can effectively estimate parameters with uncertainty for nonlinear structural systems, and it has a stronger anti-noise capability. Notably, the SCEM-UA method not only shows better global optimization capability compared with other heuristic optimization methods, but it also has the ability to simultaneously estimate the uncertainties associated with the posterior distributions of the structural parameters within a single optimization run.

Identification of model-free structural nonlinear restoring forces using partial measurements of structural responses

2016 ◽  
Vol 20 (1) ◽  
pp. 69-80 ◽  
Author(s):  
Y Lei ◽  
SJ Luo ◽  
MY He
Keyword(s):  
Power Series ◽  
Structural Parameters ◽  
Structural System ◽  
Restoring Force ◽  
Model Free ◽  
Nonlinear Structural ◽  
Structural Nonlinearities ◽  
Restoring Forces ◽  
Structural Responses ◽  
Nonlinear Behaviors

Identification of nonlinear structural system is an important but challenging task for structural health monitoring. Due to the complexities of structural nonlinearities, it is hard to establish proper mathematical models for some structural nonlinear behaviors. Moreover, only partial structural responses can be measured in practice; it is essential to conduct identification of nonlinear structural systems using only partial measurements of structural responses. To cope with these issues, an algorithm is proposed in this article for the identification of some model-free structural nonlinear restoring forces using only partial measurements of structural responses. First, an equivalent linear structural system is introduced for the identification of the locations of structural nonlinearities. Then, a model-free structural nonlinear restoring force is approximated by a power series polynomial. The unknown coefficients of the power series polynomials together with other structural parameters are identified by the extended Kalman filter so that the characteristics of the behaviors of the model-free of nonlinear restoring forces can be identified. Some numerical examples including the identification of two nonlinear multi-story shear frames and a planar nonlinear truss with different structural nonlinear restoring forces are used to validate the proposed algorithm.

Optimization Dressing Method of Amplitude Transformer Based on Finite Element

2013 ◽  
Vol 278-280 ◽  
pp. 315-318
Author(s):  
Ming Li Zhao ◽  
Bo Zhao ◽  
Yu Qing Wang
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Resonant Frequency ◽  
Structural Parameters ◽  
Low Cost ◽  
Dressing Method ◽  
Finite Element Software ◽  
System Vibration ◽  
Vibration Performance ◽  
Element Method

The node position of amplitude transformer was determined by the finite element method, and the flange was designed at the nod position for conveniently installation. By the finite element software, the amplitude transformer with flange was optimized and dressed, and its structural parameters were determined. During the actual manufacturing process, it was used impedance analyzer to test its vibration performance, the testing results show that this system vibration performance is good, its resonant frequency is 34.771kHz, anti-resonant frequency is 35.008kHz. The above-mentioned results are very much coincided with the system natural frequency of 34.893kHz which is drew by finite element method. Compared to the traditional dressing this method has many advantages such as convenience, green, environmental protection, low cost and others.

scholarly journals Determination of Peak Impact Force for Buildings Exposed to Structural Pounding during Earthquakes

Geosciences ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 18 ◽  
Author(s):  
Seyed Mohammad Khatami ◽  
Hosein Naderpour ◽  
Rui Carneiro Barros ◽  
Anna Jakubczyk-Gałczyńska ◽  
Robert Jankowski
Keyword(s):  
Ground Motion ◽  
Parametric Study ◽  
Structural Damage ◽  
Impact Force ◽  
Structural Parameters ◽  
Seismic Excitations ◽  
Ground Acceleration ◽  
Structural Pounding ◽  
Peak Impact Force

Structural pounding between adjacent, insufficiently separated buildings, or bridge segments, has been repeatedly observed during seismic excitations. Such earthquake-induced collisions may cause severe structural damage or even lead to the collapse of colliding structures. The aim of the present paper was to show the results of the study focused on determination of peak impact forces during collisions between buildings exposed to different seismic excitations. A set of different ground motion records, with various peak ground acceleration (PGA) values and frequency contents, were considered. First, pounding-involved numerical analysis was conducted for the basic parameters of colliding buildings. Then, the parametric study was carried out for different structural natural periods, structural damping ratios, gap sizes between buildings and coefficients of restitution. The results of the analysis conducted for the basic structural parameters indicate that the largest response of the analysed buildings was observed for the Duzce earthquake. The parametric study showed that the pounding-involved structural response depended substantially on all parameters considered in the analysis, and the largest response was observed for different ground motions. The results of the study presented in this paper indicate that the value of the peak impact force expected during the time of the earthquake does not depend on the PGA value of ground motion, but rather on the frequency contents of excitation and pounding scenario. It is therefore recommended that the peak impact force for buildings exposed to structural pounding during earthquakes should be determined individually for the specific structural configuration taking into account the design ground motion.

scholarly journals An ANN-Based Approach for Prediction of Sufficient Seismic Gap between Adjacent Buildings Prone to Earthquake-Induced Pounding

2020 ◽  
Vol 10 (10) ◽  
pp. 3591
Author(s):  
Seyed Mohammad Khatami ◽  
Hosein Naderpour ◽  
Seyed Mohammad Nazem Razavi ◽  
Rui Carneiro Barros ◽  
Barbara Sołtysik ◽  
...  
Keyword(s):  
Parametric Analysis ◽  
Structural Parameters ◽  
Seismic Gap ◽  
Minimal Distance ◽  
Lumped Mass ◽  
Seismic Excitations ◽  
Ground Acceleration ◽  
Earthquake Records ◽  
Adjacent Buildings ◽  
Structural Arrangements

Earthquake-induced structural pounding may cause major damages to structures, and therefore it should be prevented. This study is focused on using an artificial neural network (ANN) method to determine the sufficient seismic gap in order to avoid collisions between two adjacent buildings during seismic excitations. Six lumped mass models of structures with a different number of stories (from one to six) have been considered in the study. The earthquake characteristics and the parameters of buildings have been defined as inputs in the ANN analysis. The required seismic gap preventing pounding has been firstly determined for specified structural arrangements and earthquake records. In order to validate the method for other structural parameters, the study has been further extended for buildings with different values of height, mass, and stiffness of each story. Finally, the parametric analysis has been conducted for various earthquakes scaled to different values of the peak ground acceleration (PGA). The results of the verification and validation analyses indicate that the determined seismic gaps are large enough to prevent structural collisions, and they are just appropriate for all different structural arrangements, seismic excitations, and structural parameters. The results of the parametric analysis show that the increase in the PGA of earthquake records leads to a substantial, nearly uniform, increase in the required seismic gap between structures. The above conclusions clearly indicate that the ANN method can be successfully used to determine the minimal distance between two adjacent buildings preventing their collisions during different seismic excitations.

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