Next Article in Journal
A Granularity-Based Intelligent Tutoring System for Zooarchaeology
Next Article in Special Issue
Damage Detection Using Modal Rotational Mode Shapes Obtained with a Uniform Rate CSLDV Measurement
Previous Article in Journal
Saturation Based Nonlinear FOPD Motion Control Algorithm Design for Autonomous Underwater Vehicle
Previous Article in Special Issue
Gradient Descent-Based Optimization Method of a Four-Bar Mechanism Using Fully Cartesian Coordinates
Open AccessArticle

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

1
Department of Disaster Mitigation for Structures, Tongji University, Shanghai 200092, China
2
State Key Laboratory of Disaster Reduction in Civil Engineering, Tongji University, Shanghai 200092, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(22), 4959; https://doi.org/10.3390/app9224959
Received: 6 September 2019 / Revised: 30 October 2019 / Accepted: 11 November 2019 / Published: 18 November 2019
(This article belongs to the Special Issue Vibration-Based Structural Health Monitoring)
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. View Full-Text
Keywords: parameter identification; uncertainty estimation; Markov chain Monte Carlo; shuffled complex evolution metropolis algorithm; optimization algorithm parameter identification; uncertainty estimation; Markov chain Monte Carlo; shuffled complex evolution metropolis algorithm; optimization algorithm
Show Figures

Figure 1

MDPI and ACS Style

Tang, H.; Guo, X.; Xie, L.; Xue, S. Experimental Validation of Optimal Parameter and Uncertainty Estimation for Structural Systems Using a Shuffled Complex Evolution Metropolis Algorithm. Appl. Sci. 2019, 9, 4959.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop