ADDITIVE TO PREDICTIVE MANUFACTURING FOR MULTISTOREY CONSTRUCTION USING LEARNING BY PRINTING AND NETWORKED ROBOTICSbroad
AM2PM · Horizon Europe grant · 2024-10-01–2028-09-30
EC contribution
Total cost
Beneficiaries
About the data
Source: CORDIS (official EU open data), Horizon Europe. Framework HORIZON · call HORIZON-EIC-2023-PATHFINDERCHALLENGES-01 · scheme HORIZON-EIC · topic HORIZON-EIC-2023-PATHFINDERCHALLENGES-01-02. CORDIS record →
Objective
The negative environmental impact of construction, effects of global climate change, global housing shortage, and rising labor costs due to a shortage of skilled workers are driving the construction industry to question traditional construction methods and push the limits of innovation. In this quest, additive manufacturing (AM) technology (in particular 3D concrete printing (3DCP)) is gaining ground in the construction industry to respond to the urgent demand for acceleration and digitization. The method of automating construction by reading digital models of buildings is undoubtedly key for the construction industry to achieve environmental sustainability, cost reduction, and realize complex shaped designs. 3DCP is thus well suited to adopting digital technologies and automating construction.The vision of AM2PM is to transform the traditional construction site for multistorey buildings into a digital and sustainable site of the future with a 50% reduction in material use and embodied carbon. This is in line with the EU green deal, New European Bauhaus and EU Digital Strategy to mitigate climate change and reduce carbon emissions.AM2PM will introduce a paradigm shift in the application of AM in construction through a comprehensive system-of-systems approach that intertwines a) multi-agent human-robotic systems that work collaboratively on site in a well-coordinated and safe manner, b) computational design of components considering buildability and environmental efficiency, c) design of the requisite sustainable cementitious materials using granular recycled and locally available material, d) AI models to predict and control the manufacturing process and e) the digital twin construction information backbone that enables cohesive operation of the design and production system as a whole. The result would be transformational, achieving a 50% reduction in material use, would reduce embodied CO2 by up to 29 million tons and potentially save >€11 billion annually
Beneficiaries (7)
| Organisation | Country | Role | EC contribution | SME |
|---|---|---|---|---|
| TECHNION - ISRAEL INSTITUTE OF TECHNOLOGY | IL | coordinator | €1,507,016 | |
| DANMARKS TEKNISKE UNIVERSITET | DK | participant | €581,489 | |
| TECHNISCHE UNIVERSITEIT EINDHOVEN | NL | participant | €520,296 | |
| TECHNISCHE UNIVERSITAET MUENCHEN | DE | participant | €491,188 | |
| FENIX TNT SRO | CZ | participant | €257,250 | Yes |
| WASP SRL | IT | participant | €248,750 | |
| TECHNION RESEARCH AND DEVELOPMENT FOUNDATION LTD | IL | thirdParty | €0 |
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