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AI-based recommender system for circular construction

Concular GmbH focuses on circular construction and supports other construction companies and the real estate industry in building more sustainably. Together with its partner Circular Structural Design from Berlin, matching algorithms are being developed within the AI pilot project with the Green-AI Hub Mittelstand in order to consider reusable components when planning new buildings.

Circular construction made in Berlin

The Berlin-based companies Circular Structural Design and Concular are both active in the field of circular construction. They help companies in the construction and property sector act more sustainably. Circular construction refers to a sustainable construction process that aims to conserve resources by reusing components. These range from movable elements such as tables and windows to load-bearing structures such as walls, ceilings, and beams. The reusable components are offered by Concular in its online store and via a plugin in the Autodesk Revit planning software. An AI-supported recommendation system is designed to help integrate existing components into new building designs, taking their load-bearing capacity into account. The entire building is included in the analysis so that holistic recommendations can be made.

Challenge: Simplifying and speeding up the component search

For newly manufactured components, companies can decide for themselves how they should be designed and can specify the dimensions to the centimetre. This is not possible for reused components. These differ depending on the donor building and must be found by civil engineers to suit new construction projects. In addition, construction engineers must be guided by the available supply and search for suitable components on platforms. To optimise the process of conserving resources through reuse, the search for components must be simple and fast. The process is already simplified by a plugin developed by Concular for the widely used Autodesk Revit planning software. For even faster processing, the plugin must be able to provide users with the best possible support in filtering the most suitable components and provide automated recommendations for the building as a whole.

The AI pilot project with the Green-AI Hub Mittelstand aims to implement an AI-supported recommendation system. It will integrate existing components into new building designs, taking their load-bearing capacity into account.

Optimisation of the recommendation system for searching for suitable components using AI

Artificial intelligence helps Circular Structural Design and Concular optimally filter their range of reusable components for structural engineers so that the most suitable components are suggested. In addition, suggestions are made when considering the building as a whole in order to maximise the number of reused components and thus the resource savings for the entire building. The process is rounded off by the integrated calculation of the capacity of load-bearing components and the inclusion of possible tolerances and adaptation options for components.

Reuse of components in the end-of-life phase

The AI-based recommendation system for components will complement the plugin already developed in the planning software by improving value creation at the end of life and increasing the reuse of components. Thanks to reuse, the components do not have to be disposed of or recycled at the end of their life cycle, but can be used directly in new buildings. Movable and easily dismantled components can be reused with minimal effort. Load-bearing components are only dismantled as far as necessary to enable transport.

Saving resources through AI-based recommender system

The AI-supported solution noticeably improves the suggestions of reusable components for users of the plugin, potentially increasing the utilisation of the tool. The integration of tolerances and customisation options also makes it possible to suggest components to users that can be installed with minimal adjustments. Based on this, the overall building analysis will provide users with recommendations for the entire building so that potential resource savings can be fully utilised.