Thesis (M.S.)--Brigham Young University. Dept. of Civil and Environmental Engineering, 2005. / Includes bibliographical references (p. 163-165).

Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2005. / Includes bibliographical references (leaves 293-308). Also available in electronic version.

Thesis (M. Phil.)--University of Hong Kong, 2005. / Title proper from title frame. Also available in printed format.

Thesis (M.E.)--University of Canterbury, 2006. / Typescript (photocopy). "August 2006." Includes bibliographical references. Also available via the World Wide Web.

Thesis (Ph. D.)--University of Texas at Austin, 2006. / Vita. Includes bibliographical references.

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)

No description available.

A study was performed to compare the linear static analysis and concrete design capabilities of two structural analysis and design programs - MicasPlus and STAAD III. Four structures were considered including a four span continuous beam, a two story 3-D frame, a seven story 3-D frame, and a fourteen story 3-D frame. The study compared the accuracy of the programs as well as their functionality. In the evaluation of the accuracy of analysis results, factors such as support reactions, nodal displacements, and element end forces were compared. The evaluation of the concrete design capabilities was based on comparing the reinforcement recommended by the two programs for beams and columns. For beam design, steel reinforcing bar selections for positive moment, negative moment, and shear reinforcing were compared. For column design, the cross sectional area of steel chosen by each program was compared. Factors considered in the evaluation of functionality included: user interface, ease of use, ease of learning, quality of output, documentation, flexibility, and analysis and design capabilities. / Master of Science

Call number: LD2668 .T4 1960 C45

published_or_final_version / Civil Engineering / Master / Master of Philosophy