## 1 Introduction  
Nowadays, parameter estimation with high accuracy and practicality is essential in civil engineering when assessing the performance of structural systems; thus, a contractor, engineer, or assessor will use it when evaluating such issues as reliability predictions, structural control, and structural health monitoring. Parameter estimation takes aim at searching for the model parameter values that has the best fit to measurement data. Therefore, it provides a reliable model to describe the current state of the structure [1]. Structural parameter identification is based on least squares or maximum likelihood criterion generally [2; 3], which requires solving a nonlinear and nonconvex optimization problem. The error surface in this estimation problem is a hypersurface in the multidimensional parameter space, thereby the hypersurface potential is highly multimodal possessing local optimum. This may lead to the search algorithms converging to local optimum or being trapped in suboptimal regions. To surmount these problems, global optimization algorithms are recommended [4]. Recently, global heuristic computational intelligence methods have been found to be powerful when local methods have not proven effective. Global heuristic optimization methods are an active field of research with more models and strategies proposed every year. Some examples are genetic algorithms (GA) [5; 6], differential evolution [7; 8], particle swarm optimization (PSO) [9; 10], the Big Bang-Big Crunch (BB-BC) algorithm [11], evolution strategy (ES) [12], simulated annealing [13], the Shuffled Complex Evolution (SCE-UA), and some variations of the SCE-UA model [14; 15]. While some global optimization methods focus on finding the best set of parameters, a realistic assessment of parametric uncertainty in structural systems has not been given enough attention. The identification of parameters involves uncertainties that are caused by mathematical model simplification, errors in the measured data, and insufficient bandwidth. Consequently, finding a single optimal set in the parameter space is generally impossible. The identified parameter set should be represented with a confidence range. Classical deterministic approaches quite often involve minimization of the least-squares norm, which compares the system measured response to model predictions with some kind of regularization. However, neither errors in the measured data nor uncertainties in model predictions are rigorously accounted for [16]. Although these global optimization methods are extensively used and are robust and efficient for parameter estimation, finding out a unique \"optimal\" parameter set remains a huge challenge, which has a significantly better performance compared with other feasible parameter sets [17]. Realistic quantification of parameter uncertainty greatly influences structural analysis results and determines the final decision of structural design, assessment, and control strategies. Recently proposed methods for the estimating of parametric uncertainty in structural models, including the Markov chain Monte Carlo (MCMC) algorithms [18; 19] and bootstrap techniques [20; 21; 22], have been successfully applied to update structural models and estimate their parameter uncertainties. MCMC algorithms have received increasing attentions for sampling from a posterior probability distribution of structural parameters of interests [23; 24; 25]. However, MCMC algorithms require a prior probability sampling distribution determining explorative capabilities and convergence rate [17]. For complex structures, it is impossible to rigorously incorporate any relevant information, including model and measurement uncertainties. Prior knowledge about the high probability region located in the parameter space is usually insufficient. Therefore, the sampling driven by transitions of Markov chain is used to adjust the proposal distribution to the posterior target distribution, which improves the efficiency of MCMC algorithms. [PERSON] et al.