Optimal Sensor Placement using Genetic Algorithm and Hybrid Crossover Operator

Document Type : Research Article


1 Associate Professor, Amirkabir University of Technology, Tehran, Iran

2 Ph.D. Student, Amirkabir University of Technology, Tehran, Iran


In this study, optimal sensor placement (OSP) which plays a key role in the health monitoring of large-scale structures, is investigated using the genetic algorithm (GA). The OSP is among permutation problems that it's challenging to define the crossover operator in this kind of problem. In this study, a new hybrid crossover operator is proposed to find the optimal location for sensors and two different strategies are investigated for selecting members to form the next generation population. Also, the two-structure coding method has been used instead of the typical binary coding method to create the chromosomes of the population members. The objective function and fitness is defined based on the modal assurance criterion (MAC) matrix that is calculated with identified mode shapes and analytical mode shapes. The efficiency of the proposed method was investigated on a high-rise structure. The results show that the mode shapes identified by the optimal placement obtained from the proposed method are identical to the analytical mode shapes of the finite element model. Also, the comparison between the sensor locations obtained by conventional operators and the proposed operator shows that the proposed hybrid crossover operator outperforms other operators in terms of accuracy and convergence speed.


Main Subjects

[1] Y. Tan, L. Zhang, Computational methodologies for optimal sensor placement in structural health monitoring: A review, Structural Health Monitoring, 19(4) (2020) 1287-1308.
[2] W.A. Maul, G. Kopasakis, L.M. Santi, T.S. Sowers, A. Chicatelli, Sensor selection and optimization for health assessment of aerospace systems, Journal of Aerospace Computing, Information, and Communication, 5(1) (2008) 16-34.
[3] D.C. Kammer, Sensor placement for on-orbit modal identification and correlation of large space structures, Journal of Guidance, Control, and Dynamics, 14(2) (1991) 251-259.
[4] L. Yao, W.A. Sethares, D.C. Kammer, Sensor placement for on-orbit modal identification via a genetic algorithm, AIAA journal, 31(10) (1993) 1922-1928.
[5] K. Worden, A. Burrows, Optimal sensor placement for fault detection, Engineering structures, 23(8) (2001) 885-901.
[6] H. Wei-ping, L. Juan, L. Hua-jun, Optimal sensor placement based on genetic algorithms, Engineering Mechanics, 22(1) (2005) 113-117.
[7] H. Gao, J.L. Rose, Sensor placement optimization in structural health monitoring using genetic and evolutionary algorithms, in: Smart Structures and Materials 2006: Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, SPIE, 2006, pp. 310-321.
[8] W. Liu, W.-c. Gao, Y. Sun, M.-j. Xu, Optimal sensor placement for spatial lattice structure based on genetic algorithms, Journal of Sound and Vibration, 317(1-2) (2008) 175-189.
[9] T.H. Yi, H.N. Li, M. Gu, Optimal sensor placement for structural health monitoring based on multiple optimization strategies, The Structural Design of Tall and Special Buildings, 20(7) (2011) 881-900.
[10] V. Nieminen, J. Sopanen, Optimal sensor placement of triaxial accelerometers for modal expansion, Mechanical Systems and Signal Processing, 184 (2023) 109581.
[11] T.-H. Yi, H.-N. Li, M. Gu, Optimal sensor placement for health monitoring of high-rise structure based on genetic algorithm, Mathematical Problems in Engineering, 2011 (2011).
[12] Y. Ni, Y. Xia, W. Lin, W. Chen, J. Ko, SHM benchmark for high-rise structures: a reduced-order finite element model and field measurement data, Smart Structures and Systems, 10(4-5) (2012) 411-426.
[13] ANSYS Mechanical User's Guide., in, ANSYS, Inc., 2021.