The strengthening of steel bridges using CFRP laminates has become a commonly used technique because of its numerous advantages compared to conventional repairs which involve welding or bolting of new steel plates. These structures will experience some sort of irregular cyclic loading during their lifetime and to analyze these complex loading cases, small scale testing is used to evaluate the fatigue performance between the steel substrate, adhesive layer and the CFRP laminate. In this research, double-strap joints (DSJ) were fabricated consisting of two high-modulus CFRP laminates and ASTM A36 steel plates bonded using a two-part epoxy adhesive. Two types of steel surface conditions were considered to evaluate the fatigue behavior under constant force amplitudes. Roughness on the steel substrate was achieved by ½ in (13 mm) diameter pits approximately 1/8 in (3.18 mm) deep to simulate an irregular surface. The results show that the surface condition has marginal influence on the total life of the specimens. To assess the damage accumulation in the DSJ, phenomenological methods like the nonlinear strength wearout Model (NLSW) and stiffness degradation were used. It was found that residual strength and stiffness decreased in a non-linear fashion. A non-linear model was used that agrees well with the experimental results and can be used to predict the residual strength of the specimens under variable amplitude fatigue (VAF). / Master of Science / With an aging and deteriorating infrastructure potentially being subjected to heavier loads than initially designed for, bridge engineers are increasingly looking for innovative, yet cost-effective solutions for repairing and maintaining the existing bridge inventory with sufficient capacity to carry legal loads. One alternative is to enhance the flexural strength of deteriorated steel members using carbon fiber-reinforced polymers (CFRP). This research project will investigate whether the use of CFRP retrofits is feasible for fatigue strengthening. The research will serve as insight to help guide bridge engineers or researchers on when and where the use of CFRP retrofits is suitable for the strengthening of steel bridges.

A dynamically dexterous legged robot platform generates specific acoustic signals during the interaction with the ground and the environment. These acoustic signals are expected to contain rich information that is related to the interaction surface as a function of the position of the legs and the overall contact process mixed with the actuator sounds that initiate the movement. As the robot platform walks or runs in any environment, this convolved acoustic signal created can be processed and analyzed in real time operation and the interaction surface can be identified. Such an utilization of acoustic data can be possible for various indoor and outdoor surfaces and with this can be useful in adjusting gait parameters that play an essential role in dynamic dexterity. In this work, surface type identification is achieved with using the several popular signal processing and pattern classification methods not on the robot platform but off-line. The performances of the selected features and the algorithms are evaluated for the collected data sets and these outputs are compared with the expectations. Depending on the off-line training and experiment results, the applicability of the study to an embedded robot platform as a future application is found quite feasible and the surface type as an input to the robot sensing is expected to improve the mobility of the robot in both indoor and outdoor environment.