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Externally bonding of fibre-reinforced polymer (FRP) strips or sheets has become a popular strengthening method for reinforced concrete structures over the last two decades. For most such strengthened concrete beams and slabs, the failure is at or near the FRP-concrete interface due to FRP debonding. The objective of this thesis is to develop a deeper understanding of the debonding behaviour of the FRP-concrete interface through mesoscale finite element simulation. Central to the investigation is the use of the concrete damaged plasticity (CDP) model for modelling the concrete. The FRP is treated as an elastic material. The numerical simulation is focused on the single shear test of FRP-concrete bonded joints. This problem is known to be highly nonlinear and has many difficulties in achieving a converged solution using the standard static loading procedures. A dynamic loading procedure is applied in this research and various parameters such as time step, loading rate etc. are investigated. In particular, the effect of the damping ratio is investigated in depth and an appropriate selection is recommended for solving such problems. It has been identified that the concrete damage model can have a significant effect on the numerical predictions in the present problem. Various concrete empirical damage models are assessed using cyclic test data and simulation of the single shear test of the FRP-concrete bonded joint and it is proposed that the Birtel and Mark’s (2006) model is the most appropriate one for use in the present problem. Subsequently, the effects of other aspects of the concrete behaviour on the FRP-concrete bond behaviour are investigated. These include the tensile fracture energy, compression strain energy and different concrete compression stress-strain models. These leads to the conclusion that the CEBFIP1990 model is the most appropriate one for the problem. An important issue for recognition is that the actual behaviour of the FRP-concrete bonded joints is three dimensional (3D), but most numerical simulations have treated the problem as two dimensional (2D) which has a number of imitations. True 3D simulation is however very expensive computationally and impractical. This study proposes a simple procedure for modelling the joint in 2D with the 3D behaviour properly considered. Numerical results show that the proposed method can successfully overcome the limitations of the traditional 2D simulation method. The above established FE model is then applied to simulate a large number of test specimens. The bond stress-slip relationship is extracted from the mesoscale FE simulation results. An alternative model is proposed based on these results which is shown to be advantageous compared with existing models. This new model provides the basis for further investigation of debonding failures in FRP strengthened concrete structures in the future.

The current study examines the effect of two methods of vocabulary instruction on writing: reading-based vocabulary instruction and writing-based vocabulary instruction. The two methods were compared on their impact on Taiwanese college students' writing quality, number of target words used, receptive vocabulary knowledge and productive vocabulary knowledge. Participants were 84 first-year ) technological college students, who were taught 75 target words over five weeks, receiving either writing-based or reading-based instruction. Reading-based instruction, learning vocabulary from a receptive approach, consisted of receptive learning activities including copying words, translation activities from L2 to L l , choosing and matching activities. Writing-based instruction, learning vocabulary from a productive approach, consisted of learning vocabulary through word pairs, translation activities from Ll to L2, sentence writing and story writing activities. Outcomes were measured on task of writing quality on pre and post picture-based story writing which was scored using two kinds of writing rubrics, number of target words used in post picture-based story writing tasks, receptive vocabulary knowledge as assessed by using a multiple-choice test and productive vocabulary knowledge assessed by using a sentence completion test. Students' pre and post writing attitude and perception toward the two learning methods after treatment were also compared. The findings suggest that the writing-based instruction may be a more effective method than reading-based instruction of improving writing quality and developing productive vocabulary knowledge. Students who received the writing-based instruction achieved higher scores on tests of writing quality and sentence completion, while no differences were found between groups in receptive vocabulary knowledge and writing attitude. The main implication is that learning vocabulary from a writing-based approach for writing may improve writing quality better than a reading-based approach.

The human brain activity is a popular and important topic in the medical science field and academic studies. In recent years, scientists have been applying various statistical methods to analyze human brain activity. Correlation between brain regions is the most common and fundamental method used to perform this task. However, correlation describes only a two-way relationship. This work explores a new approach by analyzing multi-way relationships. Due to computational complexities, we concentrate on three-way relationships. In particular, we compare conventional two-way correlations and three-way regression models. Data transformed and processed from 3,280 MRI scans of the human brain are used in modeling and analysis. The results of this research show qualified three-way relationships which have a significant advantage relative to their corresponding two-way relationships. The algorithm proposed in this paper can potentially outperform the conventional two-way correlations in exploring the activity of human brain regions.

This thesis research attempts to answer the question of “how companies can better compete in the market with their sustainable innovations?” using cases in the cosmetic industry. Key factors that contribute to the successful diffusion of sustainable innovations, as well as obstacles that need to be overcome for a sustainable cosmetic company to thrive in an already saturated cosmetic product industry are explored. Strategies that help cosmetic companies better integrate sustainability into the core of their business strategies and managerial operations are suggested. Based on literature review, three main contributing factors: customer education, strategic alliance, and company image building are identified and illustrated in details. A survey and statistical analysis are performed to test the impact of customer education on customers’ decision in choosing sustainable cosmetic products and to study the importance of fourteen factors that contribute to a sustainable cosmetic company’s image building.

The construction industry faces challenges that include high workplace injuries and fatalities, stagnant productivity, and skill shortage. Automation and Robotics in Construction (ARC) has been proposed in the literature as a potential solution that makes machinery easier to collaborate with, facilitates better decision-making, or enables autonomous behavior. However, there are two primary technical challenges in ARC: 1) unstructured and featureless environments; and 2) differences between the as-designed and the as-built. It is therefore impossible to directly replicate conventional automation methods adopted in industries such as manufacturing on construction sites. In particular, two fundamental problems, pose estimation and scene understanding, must be addressed to realize the full potential of ARC. This dissertation proposes a pose estimation and scene understanding framework that addresses the identified research gaps by exploiting cameras, markers, and planar structures to mitigate the identified technical challenges. A fast plane extraction algorithm is developed for efficient modeling and understanding of built environments. A marker registration algorithm is designed for robust, accurate, cost-efficient, and rapidly reconfigurable pose estimation in unstructured and featureless environments. Camera marker networks are then established for unified and systematic design, estimation, and uncertainty analysis in larger scale applications. The proposed algorithms' efficiency has been validated through comprehensive experiments. Specifically, the speed, accuracy and robustness of the fast plane extraction and the marker registration have been demonstrated to be superior to existing state-of-the-art algorithms. These algorithms have also been implemented in two groups of ARC applications to demonstrate the proposed framework's effectiveness, wherein the applications themselves have significant social and economic value. The first group is related to in-situ robotic machinery, including an autonomous manipulator for assembling digital architecture designs on construction sites to help improve productivity and quality; and an intelligent guidance and monitoring system for articulated machinery such as excavators to help improve safety. The second group emphasizes human-machine interaction to make ARC more effective, including a mobile Building Information Modeling and way-finding platform with discrete location recognition to increase indoor facility management efficiency; and a 3D scanning and modeling solution for rapid and cost-efficient dimension checking and concise as-built modeling.