PARAMETRIC DESIGN AND EXPERIMENTAL VALIDATION OF CONJUGATE STRESS SENSORS FOR STRUCTURAL HEALTH MONITORING

Loading...
Thumbnail Image

Files

Publication or External Link

Date

2021

Citation

Abstract

In this dissertation, conjugate stress (CS) sensing is advanced through a parametric evaluation of a surface-mounted design and through experimental validation in monotonic and cyclic tensile tests. The CS sensing concept uses a pair of sensors of significantly different mechanical stiffness for direct query of the instantaneous local stress-strain relationship in the host structure, thus offering measurement of important health indicators such as stiffness (modulus), yield strength, strain hardening, and cyclic hysteresis. In this study, surface-mounted CS sensor designs are parametrically evaluated with finite element modeling, with respect to the sensors’ location, thickness, and modulus and the external loading state. An analytic pin-force model is developed to infer the host structure’s stress-strain state, based on the strain outputs of the CS sensor-pair. Two CS sensor designs are fabricated – one employs resistive foil strain gauges and the second employs fiber optic sensors – and paired with the pin-force model for experimental demonstration of the measurement of: (i) stress-strain history of three different isotropic metal bars (aluminum, copper, and steel) as they experience monotonic tensile loads well into plasticity and (ii) stress-strain hysteresis of a steel bar as it is subject to cyclic tensile fatigue. In the cyclic tests, two machine learning algorithms – anomaly detection and neural net classification – are used in conjunction with the estimated host stiffness from the CS sensor and pin force model to predict the onset of damage in the steel beams.

Notes

Rights