In order to diminish the dependency on new natural resources, reduce the overall environmental costs of the industry (extraction of raw material, its transportation and the manufacturing of new construction elements), this project employs an automatic digitization method for post-demolition elements and considers it as a source of high value assets. The subsequent dataset is then transferred to designers and builders to promote the use of valuable secondary-source materials and better inform early design decisions when repurposing construction waste.
The process links the database with a computational design tool that can be integrated into construction software for architects and construction companies. The proposed system both matched the designed components with relevant stored materials by their design requirements, as well as providing suggestions for design changes. The design iterations aim to optimize repurposed material utilization, performance, and cost.
Finally, the research seeks to reduce the environmental impact of the sector by promoting the use of locally sourced, readily available, and reclaimed materials in an automated way. It exposes the potential for constructing big data sets of reusable materials, digitally available, for sharing and organizing material harvesting and facilitating its incorporation in new designs.
Aiming to have of an optimal density of reused material, the design approach features a high degree of flexibility by an adjustment of certain parameters. Is it necessary that the generative design algorithm is able to adapt to the designers intent without losing the dependency on sparse and locally available materials dataset.
Top view of the design, the red spots mark the selected pieces necessary for the construction of that part of the facade.
The second iteration utilizes a real world inventory of wood battens taken from a previous pavilion at IaaC. This post-demolition inventory is then digitized into a relational database by a robotic scanning procedure, the scans are then decimated and classified to be accessed afterwards in the matching process. The system is able to extract information like mass ratio, usage, nail density, warping percentage and surface quality. This parameters are weighted into an average named "Material Health" from which a pre-selection will be done in order to match only the proper quality for the structure. The amount of information extracted on each piece boosts the confidence on the wood battens finally selected for the design.
Material Matching and Optimization
With the goal of increasing the design and construction potentials of upcycle and irregular materials through digital technologies to advance the widespread use of reclaimed materials in structurally reliable assemblies, a system was developed that reviews stored materials in the library and matches them with the design requirements. As each element in the design is compared with the dataset, there are three possible results: First, it may find a direct match within a certain tolerance. Alternatively, a larger piece may need to be cut down to match, and the resulting cutoff added back to the dataset. Finally, the dataset may be unable to provide the necessary element and a new-stock element will need to be procured.
The efficiency of this mapping, along with a resulting structural simulation of the resulting configuration, is used as a guiding fitness value for gradually adjusting and optimizing the design. Finally, for the optimization, a three-dimension genetic algorithm applies small translations to the geometric inputs of the façade and minimizes a total fitness value calculated from the previous analysis. Inputs are weighted to favor efficient reuse first, with the structural results only disqualifying an iteration if the system proves to be very unstable.
Honors and Awards