Optimal Sensor Placement for Modal Identification of Bridge Systems Considering Number of Sensing Nodes

• Published in: Journal of bridge engineering 2014-06, Vol.19 (6)
• Main Authors: Chang, Minwoo, Pakzad, Shamim N
• Format: Article
• Language: English
• Subjects: Technical Papers
• ISSN: 1084-0702; 1943-5592
• DOI: 10.1061/(ASCE)BE.1943-5592.0000594

AbstractA series of optimal sensor placement (OSP) techniques is discussed in this paper. A framework for deciding the optimum number and location of sensors is proposed, to establish an effective structural health monitoring (SHM) system. The vibration response from an optimized sensor network reduces the installation and operational cost, simplifies the computational processes for a SHM system, and ensures an accurate estimation of monitoring parameters. In particular, the proposed framework focuses on the determination of the number of sensors and their proper locations to estimate modal properties of bridge systems. The relative importance of sensing locations in terms of signal strength was obtained by applying several OSP techniques, which include effective influence (EI), EI-driving point residue (EI-DPR), and kinetic energy (KE) methods. Additionally, the modified variance (MV) method, based on the principal component analysis (PCA), was developed with the assumption of independence in modal ordinates at each sensing location. Modal assurance criterion (MAC) between the target and interpolated mode shapes from an optimal sensor set was utilized as an effective measure to determine the number of sensors. The proposed framework is verified by three examples: (1) a numerically simulated simply supported beam, (2) finite-element (FE) model of the Northampton Street Bridge (NSB), and (3) modal information identified using a set of wireless sensor data from the Golden Gate Bridge (GGB). These three examples demonstrate the application and efficiency of the proposed framework to optimize the number of sensors and verify the performance of the MV method compared to the EI, EI-DPR, and KE methods.