Sensitivity of Collapse Potential of Buildings to Variations in Structural Systems and Structural Parameters

2007 ◽  
Author(s):  
Farzin Zareian ◽  
Helmut Krawinkler
Keyword(s):  
Structural Parameters ◽  
Structural Systems ◽  
Collapse Potential

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.

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.

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.

A Two-stage Parametric Identification of Strong Nonlinear Structural Systems with Incomplete Response Measurements

2016 ◽  
Vol 16 (04) ◽  
pp. 1640022 ◽  
Author(s):  
Lijun Liu ◽  
Ying Lei ◽  
Mingyu He
Keyword(s):  
Kalman Filter ◽  
Unscented Kalman Filter ◽  
Structural Parameters ◽  
Nonlinear Problems ◽  
Parametric Identification ◽  
Structural Systems ◽  
Two Stage ◽  
Incomplete Response ◽  
Nonlinear Structural ◽  
Structural Responses

Compared with the identification of linear structural parameters, it is more difficult to conduct parametric identification of strong nonlinear structural systems, especially when only incomplete structural responses are available. Although the extended Kalman filter (EKF) is useful for structural identification with partial measurements of structural responses and can be extended for the identification of nonlinear structures, EKF approximates nonlinear system through Taylor series expansion and is therefore not effective for the identification of strong nonlinear structural systems. Other approaches such as the unscented Kalman filter (UKF) have been proposed for the identification of strong nonlinear problems. Based on the fact that nonlinearities exist in local areas of structures, a straightforward two-stage identification approach is proposed in this paper for the identification of strong nonlinear structural parameters with incomplete response measurements. In the first stage, the locations of nonlinearities are identified based on the EKF for the identification of the equivalent linear structures. In the second stage, the UKF is utilized to identify the parameters of strong nonlinear structural systems. Therefore, the parametric identification of strong nonlinear structural parameters is simplified by the proposed approach. Several numerical examples with different nonlinear models and locations are used to validate the proposed approach.

Response of earthquake-resistant reinforced-concrete buildings to blast loadingThis article is one of a selection of papers published in the Special Issue on Blast Engineering.

2009 ◽  
Vol 36 (8) ◽  
pp. 1378-1390 ◽  
Author(s):  
Murat Saatcioglu ◽  
Togay Ozbakkaloglu ◽  
Nove Naumoski ◽  
Alan Lloyd
Keyword(s):  
Reinforced Concrete ◽  
Blast Resistance ◽  
Structural Parameters ◽  
Progressive Collapse ◽  
Structural Response ◽  
Charge Weight ◽  
Blast Loads ◽  
Reinforced Concrete Buildings ◽  
Collapse Potential ◽  
Concrete Buildings

Recent bomb attacks on buildings have raised awareness about the vulnerability of structures to blast effects. The resiliency of structures against blast-induced impulsive loads is affected by structural characteristics that are also important for seismic resistance. Deformability and continuity of structural elements, strength, stiffness, and stability of the structural framing system and resistance to progressive collapse are factors that play important roles on the survivability of buildings under both blast and seismic loads. The significance of these structural parameters on blast resistance of reinforced concrete buildings is assessed through structural analysis. Both local element performance and global structural response are considered while also assessing the progressive collapse potential. The buildings under investigation include 10-storey moment resisting frames with or without shear walls. The blast loads selected consist of different charge-weight and standoff distance combinations. The results are presented in terms of ductility and drift demands. They indicate improved performance of seismic-resistant buildings when subjected to blast loads, in terms of local column performance, overall structural response, and progressive collapse potential.

scholarly journals Selecting Optimal Dimensions of Internal Tube in Tube-in-Tube Structural Systems Based on Structural Parameters

2021 ◽  
Vol 5 (3) ◽  
pp. 23-33
Author(s):  
M. Babaei ◽  
Y. Mohammadi ◽  
A. Ghannadiasl ◽  
◽  
◽  
...  
Keyword(s):  
Structural Parameters ◽  
Structural Systems

scholarly journals Identification of time-varying systems with partial acceleration measurements by synthesis of wavelet decomposition and Kalman filter

2020 ◽  
Vol 12 (6) ◽  
pp. 168781402093046
Author(s):  
Siyi Chen ◽  
Jubin Lu ◽  
Ying Lei
Keyword(s):  
Kalman Filter ◽  
Degrees Of Freedom ◽  
Structural Parameters ◽  
Parametric Identification ◽  
Identification Problem ◽  
Structural Systems ◽  
Wavelet Expansion ◽  
Time Varying ◽  
Multiresolution Decomposition ◽  
Original Time

Structural systems often exhibit time-varying dynamic characteristics during their service life due to serve hazards and environmental erosion, so the identification of time-varying structural systems is an important research topic. Among the previous methodologies, wavelet multiresolution analysis for time-varying structural systems has gained increasing attention in the past decades. However, most of the existing wavelet-based identification approaches request the full measurements of structural responses including acceleration, velocity, and displacement responses at all dynamic degrees of freedom. In this article, an improved algorithm is proposed for the identification of time-varying structural parameters using only partial measurements of structural acceleration responses. The proposed algorithm is based on the synthesis of wavelet multiresolution decomposition and the Kalman filter approach. The time-varying structural stiffness and damping parameters are expanded at multi-scale profile by wavelet multiresolution decomposition, so the time-varying parametric identification problem is converted into a time-invariant one. Structural full responses are estimated by Kalman filter using partial observations of structural acceleration responses. The scale coefficients by the wavelet expansion are estimated via the solution of a nonlinear optimization problem of minimizing the errors between estimated and observed accelerations. Finally, the original time-varying parameters can be reconstructed. To demonstrate the efficiency of the proposed algorithm, the identification of several numerical examples with various time-varying scenarios is studied.

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.

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.

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