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.
Jin Yan, Xiaosong Du, Austin Downey, Alessandro Cancelli, Simon Laflamme, Leifur Leifsson, An Chen, and 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 (Presented at SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring: March 09, 2018; Published: 27 March 2018); https://doi.org/10.1117/12.2296711.
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