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This project explores the feasibility of achieving a circular economy for building materials in urban environments by developing a system for recycling existing building materials based on different demolition and reuse methods, measured under parameters of monetary cost, embodied carbon, machinery requirements, or transportation. Responding to excessive global resource extraction, Material Locale provides a framework for a systematic approach that could potentially yield a new vernacular, displayed through both materiality and assembly logic. A case study site in Seven Sisters, North London, is chosen to test this notion of a hyper-local material and language system.
What if our cities were understood as resources in their own right, composing a new kind of material repository, a hyper-local material library.
This process combines 3D scanning, photogrammetry, and historical document research to construct virtual architectural models. Machine learning image recognition techniques were then used to identify material types.
After collecting architectural data, different demolition methods will be chosen based on the existing material availability and the specific material requirements for the new construction.
Machine learning is employed for material identification and quantity assessment. Target recognition is used to determine material types, while semantic segmentation is used to estimate the proportion of materials within the building.
Based on the pre-inspection before demolition, the materials inside the building are selectively removed and catalogued.
The data of the demolished materials is uploaded to the online material library, providing the user with a visualisation of the material information.
The building is first treated as a box with different resolutions and then materialised and functionalised. According to the different sizes and functionality of the materials, the building is finally assembled with reversible disassembly design.
This process deconstructs how a closed space can be formed by defining the modules of the different dimensions of the voxel, and how the voxel is connected. The voxel is thus divided into different functions and different dimensions.
Based on the difference in the integrity of the building materials obtained from the different demolition methods, the building elements are divided into three different sizes to incorporate different sizes of demolished materials.
Material assembly follows the principle of design for disassembly, using metal connectors that can be disassembled and reinstalled repeatedly.
In the process of assembling reused materials, uniform-sized cut pieces or a variety of different-sized pieces can be used.
Buildings can be constructed by selecting material modules based on different hashtags selected by users.
Land is divided into a 1m x 1m grid and categorised into different basic functions. These functions are combined to create complete 3m x 3m spaces. The space is then materialised by connecting grid units using a wave function collapse algorithm.
The system's adjacency mechanism delineates the specific functions of the community site when residents make design choices.
Layout optimisation of the community is based on factors such as plot ratio, solar radiation, community green space ratio, and community public spaces.
During the process of material recycling and reuse, additional carbon costs will be recorded, and the final carbon savings will be calculated by combining the carbon costs saved from material extraction and processing.
The system provides the user with a range of building assembly modules with corresponding assembly guidance, and the embodied carbon of the materials will be used as a reference for the purchaser's carbon credit.