Combining Subsystem and Full System Data with Application to Cold-Formed Steel Shear Wall

Consider a stochastic system composed of multiple subsystems and a full system. This paper studies the identification of parameters for the case where the subsystems are log-normally distributed and the full system is normally distributed. Using the maximum likelihood estimation (MLE) technique, we present the formal convergence results on the MLEs to the true subsystem and full system values. The asymptotic normal distributions for the MLEs are also derived, which are shown to be closely connected with the Fisher information matrix (FIM). The asymptotic distributions are usefully in providing uncertain bounds on the estimates. We apply our method on the coldformed steel (CFS) structural system, where the fasteners are viewed as the subsystems and the shear wall is viewed as a full system. Using the data independently collected from multiple sources, we are able to provide a much better estimation of the shear wall strength when compared with the previous simple one-sample estimation method.

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