The number of sensors and the corresponding locations are very important for the information content obtained from the measured data, which is a recognized challenging problem for large-scale structural systems. This paper pays special attention to the sensor placement issues on a large-scale periodically articulated structure representing typical pipelines to extract the most information from measured data for the purpose of model identification. The minimal model parameter estimation uncertainties quantified by the information entropy measure is taken as the optimality criterion for sensors placement. By utilizing the inherent periodicity property of this type of structures together with the Bloch theorem, the computational cost required for dynamic analysis to form the Fisher information entropy (IE) with respect to the entire periodic structure can be dramatically reduced regardless of the number of contained periodic units. In addition, in order to avoid the error of dynamic modeling induced by conventional finite element method based on static shape function, the spectral finite element method, a highly accurate dynamic modeling method, is employed for modeling the periodic unit. Moreover, a novel discrete optimization method is developed, which is very efficient in terms of the number of function evaluations. The proposed methodology is demonstrated by both numerical and laboratory experiments conducted for a bolt-connected periodic I-beam model.