Multi-objective sensor placement optimization for structural response estimation under spatially varying dynamic loading of bridges

Abstract

Although a lot of different types of sensors are available in the market only a limited number of sensors can be installed on a structure. Proper placement of these sensors plays a vital role in effectively achieving the objectives of a monitoring system. Sensor placement becomes especially critical in the case of bridges where the applied loading keeps on changing its location. A sensor layout that provides good quality structural response estimates for a given applied loading may not yield acceptable results for a different loading arrangement. Further, usually different types of sensors are installed on a structure e.g. strain gauges and accelerometers. These sensors measure different physical quantities having different units and orders of magnitude thus cannot be easily incorporated in a sensor placement optimization (SPO) process. So, this research work proposes a multi-objective sensor placement optimization approach that can effectively deal with different types of sensor measurements and spatially varying loading such as in the case of bridges. The proposed method employs an augmented Kalman filter (AKF) for structural response estimation and a multi-objective genetic algorithm for SPO. The AKF can effectively estimate structural response using a few heterogeneous noisy measurements while incorporating the modeling error. The effectiveness of the proposed method is demonstrated using a numerical example of a 3D truss bridge structure. The results show that the proposed multi-objective optimization method yields a sensor arrangement that remains effective against spatially varying dynamic loading.

General Tikhonov regularization‐based load estimation of bridges considering the computer vision‐extracted prior information

“…In the beam and plate examples, the number and location of the sensors are selected randomly without optimization. The optimal sensor layout has been studied by several researchers 39,40 . One general principle is that the number of sensors must be greater than that of the unknown forces.…”

“…The optimal sensor layout has been studied by several researchers. 39,40 One general principle is that the number of sensors must be greater than that of the unknown forces. When measurement noise exists, more sensors can help to reduce the overfitting effect.…”