Smart Structures and Systems

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Operation load estimation of chain-like structures using fiber optic strain sensors

  • Derkevorkian, Armen (Jet Propulsion Lab., California Inst. of Technology) ;
  • Pena, Francisco (NASA Armstrong Flight Research Center) ;
  • Masri, Sami F. (Department of Civil Engineering, Viterbi School of Engineering, Univ. of Southern California) ;
  • Richards, W. Lance (NASA Langley Research Center)
  • Received : 2017.02.17
  • Accepted : 2017.06.26
  • Published : 2017.09.25
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Abstract

The recent advancements in sensing technologies allow us to record measurements from target structures at multiple locations and with relatively high spatial resolution. Such measurements can be used to develop data-driven methodologies for condition assessment, control, and health monitoring of target structures. One of the state-of-the-art technologies, Fiber Optic Strain Sensors (FOSS), is developed at NASA Armstrong Flight Research Center, and is based on Fiber Bragg Grating (FBG) sensors. These strain sensors are accurate, lightweight, and can provide almost continuous strain-field measurements along the length of the fiber. The strain measurements can then be used for real-time shape-sensing and operational load-estimation of complex structural systems. While several works have demonstrated the successful implementation of FOSS on large-scale real-life aerospace structures (i.e., airplane wings), there is paucity of studies in the literature that have investigated the potential of extending the application of FOSS into civil structures (e.g., tall buildings, bridges, etc.). This work assesses the feasibility of using FOSS to predict operational loads (e.g., wind loads) on chain-like structures. A thorough investigation is performed using analytical, computational, and experimental models of a 4-story steel building test specimen, developed at the University of Southern California. This study provides guidelines on the implementation of the FOSS technology on building-like structures, addresses the associated technical challenges, and suggests potential modifications to a load-estimation algorithm, to achieve a robust methodology for predicting operational loads using strain-field measurements.

Keywords

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