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Abstract

The Building Dynamic Life Cycle Assessment (BDLCA) is a critical method that aims to identify the environmental impacts of various dynamic variables and mitigate their disruptive effects during the early stages of a building’s lifecycle. Since the dynamic variables take part in a time-sensitive network, their interactions, intensity, and number of variables vary in each time slice. Hence, a holistic approach should consider the entire longest lifespan of a building, from handover to end-of-life, to address the complexity of the problem.

In this research study, collecting, sorting, and storing the dynamic data are enabled by utilizing cutting-edge technologies, providing the industry with the first comprehensive post-assessed repository. The repository contains patterns, which are sets of data in specific time slices of the net value of variables that are accompanied by timestamps and geographical spatial characteristics.

In the suggested framework, Artificial Intelligence (AI) and Machine Learning (ML) techniques are adopted to extract information from dynamic patterns, identify the behaviour of a dynamic variable in a network over time, review prediction equations in the design stage relevant to the variables with corresponding data from real-life, and standardize the storage to apply to assessment applications. 

Four groups are identified who can benefit from the generated information: application developers, facility managers, occupants, and the broader construction industry. The mentioned stakeholders can leverage the information to reduce the environmental impact of the dynamic variables and mitigate the carbon footprint in the AEC industry.

Further research studies were proposed such as implementing the framework in a case study, developing the background dynamic variable repository, integration into Modern Methods of Construction (MMC), integration automatise occupant agreements into the framework, and automatise the information update in the digital twins and BIM model. The research provides a solid theoretical foundation for the development of sustainable construction practices.

Course: Building Information Management - MSc - P2657FTC

Date Deposited: 2023-11-07

URI/permalink: https://library.port.ac.uk/dissert/dis14251.html