Optimal Sensor Placement for the Structure Health Monitoring of Bridge Structures Using Genetic Algorithm

Document Type : Original Article

Authors

1 Assistant Professor, Civil Engineering Department, Payam-e-Noor University, Tehran, Iran

2 MSc in Structural Engineering, Department of Civil Engineering, Payam-e-Noor University, Tehran, Iran

Abstract

One of the challenges in the health monitoring of bridge structures is the "data" extraction from the structure. This is done by sensors in the structure. The layout and use of the fewest possible number of sensors, so as to provide the most needed data on structural status, has always been of interest. There are shortcomings in the methods used to determine the location of sensors in bridges, such as the use of one optimization indicator, determination of the number of sensors experimentally, high environmental noise, and a long calculation time. In order to overcome these shortcomings, a new MSE-MGA (Modal Strain Energy-Modified Genetic Algorithm) method is proposed in this study. In this method, two modal strain energy indices and modal contribution coefficient are used to reduce the noise effect of vehicles passing through. All the appropriate locations of the sensors are selected by these indices, and then the optimal number of sensors and their location are determined by using the genetic algorithm. The results show that increasing the number of sensors from a given optimal value has no effect on increasing the required data. Also, the simultaneous use of two optimization indices has resulted in the elimination of a large number of inappropriate points for sensor placement, resulting in a significantly reduced computational time. To investigate the performance and practical application of this method, a model of a steel bridge is modeled and the optimal number of sensors and their layout are determined.

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References

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Journal of Structural and Construction Engineering
Volume 8, Issue 6 - Serial Number 44
September 2021
Pages 5-23

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History

  • Receive Date: 16 September 2019
  • Revise Date: 26 April 2020
  • Accept Date: 02 May 2020

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