scholarly journals State-of-the-art review on Bayesian inference in structural system identification and damage assessment

2018 ◽  
Vol 22 (6) ◽  
pp. 1329-1351 ◽  
Author(s):  
Yong Huang ◽  
Changsong Shao ◽  
Biao Wu ◽  
James L. Beck ◽  
Hui Li
Keyword(s):  
Bayesian Inference ◽  
System Identification ◽  
Inverse Problems ◽  
Damage Assessment ◽  
Structural Engineering ◽  
State Of The Art ◽  
Model Parameters ◽  
Civil Infrastructure ◽  
Modeling Uncertainties ◽  
Structural System Identification

Bayesian inference provides a powerful approach to system identification and damage assessment for structures. The application of Bayesian method is motivated by the fact that inverse problems in structural engineering, including structural health monitoring, are typically ill-conditioned and ill-posed when using noisy incomplete data because of various sources of modeling uncertainties. One should not just search for a single “optimal” value for the vector of model parameters but rather attempt to describe the whole family of plausible model parameters based on measured data using a Bayesian probabilistic framework. In this article, the fundamental principles of Bayesian analysis and computation are summarized; then a review is given of recent state-of-the-art practices of Bayesian inference in system identification and damage assessment for civil infrastructure. Discussions of the benefits and deficiencies of these approaches, as well as potentially useful avenues for future studies, are also provided. Our focus is on meeting challenges that arise from system identification and damage assessment for the civil infrastructure but our presented theories also have a considerably broader applicability for inverse problems in science and technology.

Error analysis of structural system identification by observability method

2017 ◽  
Vol 109 (7) ◽  
pp. 3254-3261
Author(s):  
Jun LEI ◽  
Dong XU ◽  
José Antonio Lozano-Galant ◽  
María Nogal ◽  
José Turmo
Keyword(s):  
System Identification ◽  
Error Analysis ◽  
Structural System ◽  
Structural System Identification

scholarly journals Comparing two sequential Monte Carlo samplers for exact and approximate Bayesian inference on biological models

2017 ◽  
Vol 14 (134) ◽  
pp. 20170340 ◽  
Author(s):  
Aidan C. Daly ◽  
Jonathan Cooper ◽  
David J. Gavaghan ◽  
Chris Holmes
Keyword(s):  
Bayesian Inference ◽  
Bayesian Methods ◽  
Sequential Monte Carlo ◽  
Model Parameters ◽  
Biological Models ◽  
Exact Inference ◽  
Modelling Studies ◽  
Approximate Bayesian ◽  
Approximate Bayesian Inference ◽  
Abc Methods

Bayesian methods are advantageous for biological modelling studies due to their ability to quantify and characterize posterior variability in model parameters. When Bayesian methods cannot be applied, due either to non-determinism in the model or limitations on system observability, approximate Bayesian computation (ABC) methods can be used to similar effect, despite producing inflated estimates of the true posterior variance. Owing to generally differing application domains, there are few studies comparing Bayesian and ABC methods, and thus there is little understanding of the properties and magnitude of this uncertainty inflation. To address this problem, we present two popular strategies for ABC sampling that we have adapted to perform exact Bayesian inference, and compare them on several model problems. We find that one sampler was impractical for exact inference due to its sensitivity to a key normalizing constant, and additionally highlight sensitivities of both samplers to various algorithmic parameters and model conditions. We conclude with a study of the O'Hara–Rudy cardiac action potential model to quantify the uncertainty amplification resulting from employing ABC using a set of clinically relevant biomarkers. We hope that this work serves to guide the implementation and comparative assessment of Bayesian and ABC sampling techniques in biological models.

scholarly journals Structural Health Monitoring of 2D Plane Structures

2021 ◽  
Vol 11 (5) ◽  
pp. 2000
Author(s):  
Behnam Mobaraki ◽  
Haiying Ma ◽  
Jose Antonio Lozano Galant ◽  
Jose Turmo
Keyword(s):  
Mechanical Properties ◽  
Health Monitoring ◽  
Mathematical Analysis ◽  
Stiffness Matrix ◽  
State Of The Art ◽  
System Of Equations ◽  
Matrix System ◽  
Structural System ◽  
Detailed Mathematical Analysis ◽  
Structural System Identification

This paper presents the application of the observability technique for the structural system identification of 2D models. Unlike previous applications of this method, unknown variables appear both in the numerator and the denominator of the stiffness matrix system, making the problem non-linear and impossible to solve. To fill this gap, new changes in variables are proposed to linearize the system of equations. In addition, to illustrate the application of the proposed procedure into the observability method, a detailed mathematical analysis is presented. Finally, to validate the applicability of the method, the mechanical properties of a state-of-the-art plate are numerically determined.

scholarly journals Bayesian Inference of Species Trees using Diffusion Models

2020 ◽  
Vol 70 (1) ◽  
pp. 145-161 ◽  
Author(s):  
Marnus Stoltz ◽  
Boris Baeumer ◽  
Remco Bouckaert ◽  
Colin Fox ◽  
Gordon Hiscott ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Numerical Algorithms ◽  
Diffusion Models ◽  
Model Parameters ◽  
Data Sets ◽  
Species Trees ◽  
Computationally Efficient ◽  
Data Set ◽  
Snp Data ◽  
Binary Markers

Abstract We describe a new and computationally efficient Bayesian methodology for inferring species trees and demographics from unlinked binary markers. Likelihood calculations are carried out using diffusion models of allele frequency dynamics combined with novel numerical algorithms. The diffusion approach allows for analysis of data sets containing hundreds or thousands of individuals. The method, which we call Snapper, has been implemented as part of the BEAST2 package. We conducted simulation experiments to assess numerical error, computational requirements, and accuracy recovering known model parameters. A reanalysis of soybean SNP data demonstrates that the models implemented in Snapp and Snapper can be difficult to distinguish in practice, a characteristic which we tested with further simulations. We demonstrate the scale of analysis possible using a SNP data set sampled from 399 fresh water turtles in 41 populations. [Bayesian inference; diffusion models; multi-species coalescent; SNP data; species trees; spectral methods.]

Procedures of Structural System Identification Applied to Automotive Structures

1986 ◽  
Author(s):  
A. A. Butkunas ◽  
J. V. Dorenbaum ◽  
D. L. Flanigan ◽  
M. Riesner ◽  
P. Surulinarayanasami ◽  
...  
Keyword(s):  
System Identification ◽  
Structural System ◽  
Automotive Structures ◽  
Structural System Identification

scholarly journals Modeling Northern Hemisphere Summer Heat Extreme Changes and Their Uncertainties Using a Physics Ensemble of Climate Sensitivity Experiments

2006 ◽  
Vol 19 (17) ◽  
pp. 4418-4435 ◽  
Author(s):  
Robin T. Clark ◽  
Simon J. Brown ◽  
James M. Murphy
Keyword(s):  
Structural Changes ◽  
Heat Waves ◽  
Full Range ◽  
Daily Maximum ◽  
Model Parameters ◽  
Mean Values ◽  
Summer Heat ◽  
Modeling Uncertainties ◽  
Physics Ensemble ◽  
Projected Changes

Abstract Changes in extreme daily temperature events are examined using a perturbed physics ensemble of global model simulations under present-day and doubled CO2 climates where ensemble members differ in their representation of various physical processes. Modeling uncertainties are quantified by varying poorly constrained model parameters that control atmospheric processes and feedbacks and analyzing the ensemble spread of simulated changes. In general, uncertainty is up to 50% of projected changes in extreme heat events of the type that occur only once per year. Large changes are seen in distributions of daily maximum temperatures for June, July, and August with significant shifts to warmer conditions. Changes in extremely hot days are shown to be significantly larger than changes in mean values in some regions. The intensity, duration, and frequency of summer heat waves are expected to be substantially greater over all continents. The largest changes are found over Europe, North and South America, and East Asia. Reductions in soil moisture, number of wet days, and nocturnal cooling are identified as significant factors responsible for the changes. Although uncertainty associated with the magnitude of expected changes is large in places, it does not bring into question the sign or nature of the projected changes. Even with the most conservative simulations, hot extreme events are still expected to substantially increase in intensity, duration, and frequency. This ensemble, however, does not represent the full range of uncertainty associated with future projections; for example, the effects of multiple parameter perturbations are neglected, as are the effects of structural changes to the basic nature of the parameterization schemes in the model.

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