ADDOPTML · ADDitively Manufactured OPTimized Structures by means of Machine Learning

ADDitively Manufactured OPTimized Structures by means of Machine Learning

The construction industry, while economically vital, is one of the most resource- and energy-intensive sectors in Europe. It is responsible for over 40% of total energy consumption and contributes to 25–30% of all waste generated in the EU, making it one of the most environmentally burdensome industries. Despite its size and importance, the sector remains heavily reliant on traditional, labor-intensive processes, which are ill-suited to meet the urgent demands for decarbonization, circularity, and digitalization. At the same time, Additive Manufacturing (AM)—a technology that has already transformed sectors like aerospace and automotive—remains significantly underutilized in construction. This is largely due to the highly fragmented, interdisciplinary, and bespoke nature of building design and production workflows. These workflows, often involving custom-made large-scale structures, resist standardization and automation, hindering the widespread adoption of AM and other advanced digital technologies. Europe’s ambition to become the first climate-neutral continent by 2050, as outlined in the European Green Deal, necessitates deep structural changes in how we build. The construction sector must drastically reduce its environmental footprint, optimize material use, and shift toward circular and resilient building practices. Innovations that enhance resource efficiency, structural adaptability, and sustainability will be instrumental in reaching these climate goals. Furthermore, the world is witnessing a growing frequency of natural disasters and humanitarian crises, often requiring fast, efficient, and adaptable construction solutions for shelter and infrastructure. Conventional construction methods are often too slow, costly, or logistically constrained to respond adequately. AM offers the potential to deliver rapidly deployable, customizable structures, designed and fabricated on demand, which can play a transformative role in post-disaster reconstruction and emergency housing, especially in remote or resource-limited settings. The overarching goal of ADDOPTML is to establish a next-generation, intelligent manufacturing paradigm for the construction industry, centered on AM and powered by machine learning, topology optimization, and generative design. This paradigm aims to accelerate innovation in the design and fabrication of high-performance, adaptable, and environmentally responsible structures. 1. To develop a comprehensive library of data-driven constitutive models for structural materials, enabling accurate and efficient simulation of AM behavior. 2. To create a high-fidelity yet computationally efficient topology optimization framework for structural components manufactured via AM. 3. To deliver a fully automated, generative design pipeline for AM structures, integrating performance-driven design and fabrication constraints. 4. To demonstrate the application of AM in rapidly deployable steel and concrete structures, tailored for post-disaster and emergency sheltering solutions. 5. To define design and fabrication protocols for AM components for space applications, extending the project’s impact beyond Earth-bound construction.

Data: CORDIS, © European Union