Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references (leaves 148-162). Also available in electronic version.

Thesis (M.S.) in Civil and Environmental Engineering--University of Maine, 2002. / Includes vita. Includes bibliographical references (leaves 102-105).

Thesis (Ph. D.)--West Virginia University, 2006. / Title from document title page. Document formatted into pages; contains xvii, 167 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 154-159).

No description available.

No description available.

No description available.

No description available.

No description available.

Corrosion of steel reinforcements is the leading causes of malfunction or even failures of reinforced concrete (RC) structures nationwide and worldwide for many decades. This arises up to substantial economic burden on repairs and rehabilitations to maintain and extend their service life of those RC public projects. The inherent natures of glass fiber-reinforced polymers (GFRP) bars, from their superior corrosion resistance to high strength-to-weight ratio, have promoted their acceptance as a viable alternative for steel reinforcement in civil infrastructures. Comprehensive understanding of the bond between GFRP bars and concrete, in particular under in-service conditions or extremely severe events, enables scientists and engineers to provide their proper design, assessment and long-term predictions, and ultimately to implement them toward the corrosion-free concrete products. This research aims to develop a holistic framework through an experimental, analytical and numerical study to gain deep understanding of the bond mechanism, behavior, and its long-term durability under harsh environments. The bond behavior and failure modes of GFRP bar to concrete are investigated through the accelerated aging tests with various environmental conditions, including alkaline and/or saline solutions, freezing-thawing cycles. The damage evolution of the bond is formulated from Damage Mechanics, while detailed procedures using the Arrhenius law and time shift factor approach are developed to predict the long-term bond degradation over time. Besides, the machine learning techniques of the artificial neural network integrated with the genetic algorithm are used for bond strength prediction and anchorage reliability assessment. Clearly, test data allow further calibration and verification of the analytical models and the finite element simulation. Bond damage evolution using the secant modulus of the bond-slip curves could effectively evaluate the interface degradation against slip and further identify critical factors that affect the bond design and assessment under the limit states. Long-term prediction reveals that the moisture content and elevated temperature could impact the material degradation of GFRP bars, thereby affecting their service life. In addition, the new attempt of the Data-to-Information concept using the machine learning techniques could yield valuable insight into the bond strength prediction and anchorage reliability analysis for their applications in RC structures. / ND NASA EPCoR (FAR0023941) / ND NSF EPSCoR (FAR0022364) / US DOT (FAR0025913)

Delamination is currently a largely undetectable form of damage in composite laminate materials. This thesis will develop a method to more easily detect delamination damage within composite materials. Using finite element analysis modeling and lab testing, a new method from interpreting the results obtained from existing structural health monitoring techniques is developed. Lamb waves were introduced and recorded through an actuator and sensors made of piezoelectric material. The data was then analyzed through a novel data reduction method using the Fast Fourier Transform (FFT). Using the data from FFT, the idea of covariance of energy change was developed. By comparing the covariance of energy change in beams with differing delamination size, thickness and depth, correlations were able to be developed. With these correlations, the severity and of damage was able to be detected.