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Application of artificial intelligence techniques in life cycle assessment of structures: A literature review
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Insinööritieteiden korkeakoulu |
Bachelor's thesis
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ENG3082
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en
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23+ 11
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Abstract
This thesis explores the potential of enhancing life cycle assessment in structural engineering through the application of artificial intelligence methodologies, with the aim of advancing sustainable design and decision-making. It begins by analyzing current LCA practices and their limitations, highlighting areas where AI can address key deficiencies. The study investigates how various AI techniques—including natural language processing, supervised and reinforcement learning, hybrid approaches, and generative models—can improve scenario analysis, data processing, and predictive accuracy. Furthermore, it examines the integration of AI with Building Information Modeling and digital twin technologies, emphasizing their ability to enable more dynamic and comprehensive sustainability assessments. Critical challenges such as data availability, standardization, and the trade-off between interpretability and model performance are also addressed. The thesis presents relevant tools and frameworks for AI-driven LCA and outlines future directions, including real-time monitoring and the use of the Internet of Things. The findings suggest that AI holds significant promise for advancing LCA methodologies and contributing to more sustainable architectural and structural design practices.