Thesis (Ph.D.)--University of Western Australia, 2007.

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A building which grows from a proposed artistic epicentre of the University of Pretoria becomes a sanctuary for all those concerned with the disciplines of art and architecture. By occupying the vacant site at the historical southern entrance of the university, the proposed centre for visual arts becomes a vehicle which allows the main campus to spill over its boundaries and fuse itself onto the existing separated South Campus. It is the aim of this dissertation to explore the ability of spaces inside this building typology to assist in inspiring and educating each of its occupants. The journey through the building becomes the narrator of spatial experiences whilst creating opportunities for artistic expression by providing facilities for the exhibition of art. By inspiring young minds with a symphony of experiences these spaces create the foundation for developing great artists. / Dissertation (MArch(Prof))--University of Pretoria, 2009. / Architecture / unrestricted

Diploma thesis is focused on design of road bridge bearing structure over a D1 highway. Structure is continuous slab with 4 fields beared by local supports. It is prestressed in longitudinal direction. Internal forces are calculated in Scia Engineer software. Reference is made according to valid standarts.

The section of highway over the 400 South roadway in Orem, Utah is made up of two separate three span bridges. The bridges were originally constructed in 1960 and were expanded in 2004 to accommodate for one extra lane per bridge. During the fall of 2012 both bridges were scheduled for demolition and four girders were salvaged from the southernmost span of the 2004 expansion. These girders were transported to the Structural Materials And Systems Health Lab (SMASH Lab) where a series of tests were performed to determine the prestressing losses, flexural, shear, and shear-flexure capacity of the girders. The results of these tests were compared to the American Association of State Highway and Transportation Officials Load Resistance Factored Design (AASHTO LRFD) Bridge Design Specifications and an ANSYS Finite Element model. For all test results the AASHTO Bridge Design was conservative for each test setup and was able to predict the type of failure that occurred. The finite element model was developed for the four test conditions and calibrated so as to accurately represent test data. The calibrations were compared to actual tested material properties to determine the difference between the theoretical model and the girders. (120 pages)

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Accurately predicting the deck condition rating of a bridge is crucial for effective maintenance and repair planning. Despite significant research efforts to develop deterioration models, a nationwide model has not been developed. This study aims to identify an appropriate machine learning (ML) algorithm that can accurately predict the deck condition ratings of the nation's bridges. To achieve this, the study collected big bridge data (BBD), which includes NBI, traffic, climate, and hazard data gathered using geospatial information science (GIS) and remote sensing techniques. Two sets of data were collected: a BBD for a single year of 2020 and a historical BBD covering a five-year period from 2016 to 2020. Three ML algorithms, including random forest, eXtreme Gradient Boosting (XGBoost), and Artificial Neural Network (ANN), were trained using 319,404 and 1,246,261 bridge decks in the BBD and the historical BBD, respectively. Results showed that the use of historical BBD significantly improved the performance of the models compared to BBD. Additionally, random forest and XGBoost, trained using the historical BBD, demonstrated higher overall accuracies and average F1 scores than the ANN model. Specifically, the random forest and XGBoost models achieved overall accuracies of 83.4% and 79.4%, respectively, and average F1 scores of 79.7% and 77.5%, respectively, while the ANN model achieved an overall accuracy of 58.8% and an average F1 score of 46.1%. The permutation-based variable importance revealed that the hazard data related to earthquakes did not significantly contribute to model development. In conclusion, tree-based ensemble learning algorithms, such as random forest and XGBoost, trained using updated historical bridge data, including NBI, traffic, and climate data, provide a useful tool for accurately predicting the deck condition ratings of bridges in the United States, allowing infrastructure managers to efficiently schedule inspections and allocate maintenance resources.

The integration of Artificial Intelligence (AI) in collaborative tasks has gained momentum, with particular implications for critical infrastructure maintenance. This study examines the assurance goals of AI—security, explainability, and trustworthiness—within Virtual Reality (VR) environments for bridge maintenance. Adopting a within-subjects design approach, this research leverages VR environments to simulate real-world bridge maintenance scenarios and gauge user interactions with AI tools. With the industry transitioning from paper-based to digital bridge maintenance, this investigation underscores the imperative roles of security and trust in adopting AI-assisted methodologies. Recent advancements in AI assurance within critical infrastructure highlight its monumental role in ensuring safe, explainable, and trustworthy AI-driven solutions. / Master of Science / In today's rapidly advancing world, the traditional methods of inspecting and maintaining our bridges are being revolutionized by digital technology and artificial intelligence (AI). This study delves into the emerging role of AI in bridge maintenance, a field historically reliant on manual inspection. With the implementation of AI, we aim to enhance the efficiency and accuracy of assessments, ensuring that our bridges remain safe and functional. Our research employs virtual reality (VR) to create a realistic setting for examining how users interact with AI during bridge inspections. This immersive approach allows us to observe the decision-making process in a controlled environment that closely mimics real-life scenarios. By doing so, we can understand the potential benefits and challenges of incorporating AI into maintenance routines. One of the critical challenges we face is the balance of trust in AI. Too little trust could undermine the effectiveness of AI assistance, while too much could lead to overreliance and potential biases. Furthermore, the use of digital systems introduces the risk of cyber threats, which could compromise the security and reliability of the inspection data. Our research also investigates the impact of AI-generated explanations on users' decisions. In essence, we explore whether providing rationale behind AI's recommendations helps users make better judgments during inspections. The ultimate objective is to develop AI tools that are not only advanced but also understandable and reliable for those who use them, even if they do not have a deep background in technology. As we integrate AI into bridge inspections, it's vital to ensure that such systems are protected against cyber threats and that they function as reliable companions to human inspectors. This study seeks to pave the way for AI to become a trusted ally in maintaining the safety and integrity of our infrastructure.

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