Adapting Cognitive Digital Twins for the Production of Sustainable Modular Houses
Research Project, 2022 – 2025

Construction industry has been regulated recently in Sweden for the purpose of implementing measures for increasing sustainability in the construction projects and also contributing to the circular economy. Swedish towns are struggling with providing sustainable housing that is also affordable. The digital transformation in construction industry could aid that goal, as it provides promising new tools for maintaining sustainability in the production of affordable houses. Within such a digital transformation, Cognitive Digital Twins (CDTs) are introduced as the next level of Semantic Digital Twins towards Construction 4.0, where efficient and sustainable processes are simulated and optimized. CDTs facilitate self-learning from previous project cases and applying new knowledge on the existing data, models, methods in combination with advanced decision support enabling the resolving of previously unknown situations in an efficient way.
The aim of this research is thus twofold: (i) Developing a CDT platform prototype (COGNITIO N) for the production of timber sustainable modular houses in order to implement them for the
analysis, self-learning and optimization of production with a limited human intervention and under a variety of economic and sustainability criteria, and (ii) Offering resource-aware, energy-efficient, low-emission, and low-cost production methods. The benefits to be derived from the adaptation of COGNITION can be better real-time decisions, through the interplay of cognition,
analytics and optimization during the production process. The aimed COGNITION platform to be adapted to the shop-floor production will reach at TRL 5: Large scale prototype to be tested in an intended environment (testbed).


Dimosthenis Kifokeris (contact)

Chalmers, Architecture and Civil Engineering, Building Design


Halmstad University

Halmstad, Sweden

School of engineering Jönköping university

Jönköping, Sweden



Project ID: 2022-01714
Funding Chalmers participation during 2022–2025

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