Developments and advances in experimental technologies providing useful data make it possible to identify civil engineering structures and to obtain a more reliable model characterizing the existing condition for decision making. Analytical models such as Finite Element (FE) models, which are calibrated using structural health monitoring (SHM) data, better represent the existing structures' behavior under different loading conditions. However, the SHM data should include sufficient information about the structural parameters to be identified. In this study, a novel methodology is proposed in order to determine the optimum sensor configuration which provides adequate data for structural identification (St-Id). The success of the St-Id is investigated in a comparative fashion by comparing the model parameters calibrated using different sensor configurations. Uncertainties both in the mathematical model and the experimental data are taken into account using the fuzzy number concept. A so-called inverse fuzzy arithmetic technique is used to quantify the uncertainties in the updated parameters. The proximity of linkage values, which are the product of cluster analysis, is used to determine the optimal sensor configuration. The optimal sensor configuration is then verified by using the relative amount of uncertainty in the updating parameters resulting from the inverse propagation of the uncertainties. A hybrid evolutionary optimization algorithm is also proposed in order to efficiently minimize an objective function that consists of differences between the fuzzy experimental measurements and the analytical data. Genetic Algorithms (GA) and Harmony Search (HS) algorithm are combined to enhance the efficiency and the robustness of the optimization process. An analytical benchmark bridge structure developed for bridge health monitoring studies is used as the test structure to verify the proposed methodologies. Three different cases including the undamaged and the damage cases are considered, It has been shown that there is no significant difference between the St-Id results obtained by using a dense sensor configuration and the optimum configuration obtained by the proposed method in terms of accuracy. (C) 2013 Elsevier B.V. All rights reserved.