Abstract:
Traditional civil engineering practices are undergoing transformation due to advancements in modern technology. This thesis investigates the potential of three emerging technologies—Blockchain, Simulations, and Machine Learning—in the field. The project employs simulations to test various scenarios in a virtual environment, encompassing mining production scheduling, urban scenario analysis, and GIS network analysis. Beyond software-based approaches, the project extends its scope by developing a physical machine capable of performing GIS simulations, overcoming technical and language barriers.
Moreover, the project enhances existing GIS workflows by training and comparing a novel machine-learning model to efficiently identify building footprints from aerial imagery. This innovative approach significantly reduces digitization time from hours or days to mere minutes.
Additionally, the project explores the application of Blockchain technology in the context of sharing survey data. The study investigates the feasibility of using Blockchain for secure data sharing from the Ethereum network by creating a web portal and leveraging Python and smart contracts.
Overall, this thesis delves into the transformative potential of Blockchain, Simulations, and Machine Learning in civil engineering, offering insights and advancements in software and hardware capabilities as well as improved workflows.
