Multi-Type Sensor Placement for Multi-Scale Response Reconstruction

Abstract

Although various types of sensors are now available to monitor a structure, measurements are usually obtained in only a few locations numbering less than the total degrees of freedom (DOFs) of the structure. The lack of structural responses of the structure at all its critical areas may hamper the effectiveness of structural monitoring. Therefore, multi-scale response reconstruction at those key structural locations where sensors are not available is essential to fully achieving the structural monitoring objectives. This paper addresses a problem of placing multi-type sensors in a structure with the objective of best reconstruction of structural responses. The types of sensors include accelerometers, displacement and strain measurement sensors, all of which are widely used in civil engineering structures. The number and locations of the three types of sensors are determined aiming to best possibly reconstruct the strain, displacement and acceleration responses, in which the Kalman filter algorithm is employed. By minimizing the overall reconstruction error variance at the locations of interest and maintaining reconstruction errors to a desired target level, the initial set of candidate sensor locations is reduced to a smaller set. The key multi-scale structural responses are reconstructed from the fusion of limited multi-type sensor data information. A simply-supported overhanging steel beam is investigated as a sample case in numerical and experimental study to investigate the effectiveness and accuracy of the presented approach. The good response reconstruction results clearly demonstrate the effectiveness of the proposed optimal placement method for multi-type sensors.