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

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.