27 March 2018 Surrogate model for condition assessment of structures using a dense sensor network
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Condition assessment of civil infrastructures is difficult due to technical and economic constraints associated with the scaling of sensing solutions. When scaled appropriately, a large sensor network will collect a vast amount of rich data that is difficult to directly link to the existing condition of the structure along with its remaining useful life. This paper presents a methodology to construct a surrogate model enabling diagnostic of structural components equipped with a dense sensor network collecting strain data. The surrogate model, developed as a matrix of discrete stiffness elements, is used to fuse spatial strain data into useful model parameters. Here, strain data is collected from a sensor network that consists of a novel sensing skin fabricated from large area electronics. The surrogate model is constructed by updating the stiffness matrix to minimize the difference between the model’s response and measured data, yielding a 2D map of stiffness reduction parameters. The proposed method is numerically validated on a plate equipped with 40 large area strain sensors. Results demonstrate the suitability of the proposed surrogate model for the condition assessment of structures using a dense sensor network.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jin Yan, Jin Yan, Xiaosong Du, Xiaosong Du, Austin Downey, Austin Downey, Alessandro Cancelli, Alessandro Cancelli, Simon Laflamme, Simon Laflamme, Leifur Leifsson, Leifur Leifsson, An Chen, An Chen, Filippo Ubertini, Filippo Ubertini, "Surrogate model for condition assessment of structures using a dense sensor network", Proc. SPIE 10598, Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems 2018, 105983F (27 March 2018); doi: 10.1117/12.2296711; https://doi.org/10.1117/12.2296711

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