A multi-objective optimization approach to optimal sensor placement of irregular LSF structures

Document Type : Applicable

Authors

1 Ph.D. Student, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

2 Ph.D. student, Department of Electrical, Electronic, and Information Engineering, University of Bologna, Bologna, Italy.

3 Associate professor, Department of Civil Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

4 Graduate Student, Department of Architecture, Qazvin Branch, Islamic Azad University, Qazvin, Iran.

Abstract

In recent years, lightweight steel framed (LSF) structures are designed to resist fire, earthquakes, and storm events. This system has entered the field of construction due to advantages of light members. Based on these advantages, such a system is also used for buildings with special importance. Structural health monitoring (SHM) implements a damage detection and characterization strategy for engineering structures.  In the present study, a multi-objective numerical method for optimal sensor placement based on the combination of Modal Assurance Criteria (MAC) and maximum stress has been proposed. Genetic Algorithm (GA) was employed to determine the location of sensors on the structure based on the structural dynamic response of the LSF system. To show the efficiency of the proposed method, a very large irregular museum building, which was built by using LSF system, has been considered. To improve the proposed method, dominant frequencies are identified and the number of sensors is decreased using the Fourier Transform (FT) of the ground motions time history. Results show that the proposed method can provide the optimal sensor locations and remarkably reduce the number of required sensors and also improve their optimum location.

Keywords

References

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Numerical Methods in Civil Engineering
Volume 5, Issue 3
March 2021
Pages 13-22

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History

  • Receive Date: 22 November 2020
  • Revise Date: 02 January 2021
  • Accept Date: 05 February 2021

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