By Saeed Eftekhar Azam
This monograph assesses extensive the applying of recursive Bayesian filters in structural future health tracking. even though the tools and algorithms used listed here are good confirmed within the box of automated keep watch over, their program within the realm of civil engineering has to this point been constrained. The monograph is as a result meant as a reference for structural and civil engineers who desire to behavior study during this box. To this finish, the most notions underlying the households of Kalman and particle filters are scrutinized via motives in the textual content and various numerical examples. the most boundaries to their software in tracking of high-rise structures are mentioned and a therapy according to a synergy of lowered order modeling (based on right orthogonal decomposition) and Bayesian estimation is proposed. The functionality and effectiveness of the proposed set of rules is verified through pseudo-experimental evaluations.
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Example text
Adding this to the three former parameters, one can see how delicate the task of tuning can become. 6 Numerical Results for Dual Estimation of Single Degree 31 Fig. 7 Results of EKF for estimation of parameters of linear-plastic constitutive law Fig. 8 Results of EKF for estimation of parameters of linear-softening constitutive law Fig. 9 Results of SPKF for estimation of parameters of linear-hardening constitutive law designed for nonlinear systems with arbitrary uncertainty associated with them.
To obtain insights into the superior performance of the EK-PF, Figs. 5 report the projections onto the two model parameter axes of the time evolution of the (smoothed) distribution of particles deployed by PF and EK-PF, respectively. 6 Numerical Results for Dual Estimation of Single Degree 27 Fig. 3 Model calibration. Time evolution of estimated model parameters (top row) a and (bottom row) n, at varying initialization values. Results obtained by running: EK-PF (long-dashed blue lines), PF (dashed orange lines) and S-PKF (continuous black lines) efficient in moving the particles toward the region of major interest, with distributions that are not spread over an extensive range of values.
20 show the results of analysis for estimation of k2 . Looking at Figs. 18, they plot the time histories of estimation of the parameter k2 , supposing that the values of k1 and uM are a priori known. Moving from Figs. 6 Numerical Results for Dual Estimation of Single Degree 33 Fig. 13 Results of PF for estimation of parameters of linear-plastic constitutive law Fig. 14 Results of PF for estimation of parameters of linear-softening constitutive law importance in the parameter estimation. In both cases the initial value of the parameter is set to 50 % of the target value.