DISCOVER aims to develop an autonomous, synchronous, continuous and intelligent identification and data analysis system for materials and products in existing end-of-life built works. The proposed approach will provide key stakeholders, including academia research performers, along with construction industry representatives, with data-driven insights to make deconstruction more efficient, optimise the use of resources, improve the environmental footprints and enhance the circularity of construction and demolition, unlocking the potential of end-of-life built works, which will become material banks. The expected outcomes include an autonomous robotic platform coupled with continuous identification tools to scan built works and provide synchronous quantitative and qualitative data from different materials, including complex and concealed elements. Artificial intelligence algorithms will allow a rapid analysis of the properties and characteristics of components, and feed the automated scan-to-BIM model creation. The multi-dimensional BIM, including selective demolition processes, labour productivity, and technical planning, will become a Digital Twin of the demolition site optimised by social, economic, and environmental multi-criteria assessments. This approach will highly contribute to significantly increasing the supply of traceable and sustainable construction materials and products for enhancing their wider market acceptance, following the waste hierarchy. The social impacts of digital transformation in the construction sector will be considered, and also new professional development tools for the relevant stakeholders will be proposed. The system will be tested in 4 different real demolition sites (Spain, Portugal, Poland and Belgium), offering a complete range of built work typologies and wide geographical coverage to demonstrate the replicability potential of DISCOVER, increasing the project dissemination capacity and awareness among the construction sector.

In the face of a rapidly advancing digital healthcare terrain, the DTRIP4H project emerges as a momentous effort to revolutionize predictive, preventive, personalized, and participatory health paradigms within the EU. Amid significant incidence of chronic conditions and cancer, there is a pressing need for a proactive shift in health strategies. Yet, the full potential of European research infrastructures (RIs) is curtailed by investment deficits, fragmentation, and the intricacies of data management. Digital Twin (DT) technology introduces a new age of precision by enabling sophisticated simulations and analyses of intricate biological processes. In DTRIP4H, we start a new initiative in Europe “decentralized health digital twin ecosystem consisting of RIs”. Using DTs, we aim to resolve critical challenges around data harmonization, equitable access, and stringent privacy safeguards. Incorporating technologies such as federated learning, Generative AI, and Virtual Reality (VR), the project aspires to create a decentralized digital twin environment (DDTE). This will empower both internal and external RI users, such as researchers, innovators, and SMEs, to craft DT applications that address specific scientific challenges, utilizing a blend of real-world and synthetic data in compliance with regulatory frameworks, i.e. GDPR. We will develop 7 innovative proof of concept thematic health-related Use cases fulfilling the needs of scientists, SMEs, and industrial end users, particularly in health topics related to cancer treatment, drug development, human environmental exposome, precision treatment for schizophrenia and personalized medicine through Artificial Intelligence (AI), AR/VR empowered DTs utilizing DDTE, while adhering to FAIR data principles. DTRIP4H adopts a human-centric methodology to elevate research efficacy, narrow the skills gap, and align with the objectives of the European Research Area (ERA) and the Sustainable Development Goals (SDGs) by 2030.

RobetArme will deliver a human-robot collaborative construction system for the automation of shotcrete (i.e. concrete spray casting), which is an emerging technology in construction domain. A multitasking Inspection-Reconnaissance mobile manipulator-IRR (ROB) will fuse the latest Geotechnical models (BIM/CIM) (DTT), high-density visual data and semantic SLAM (CERTH) representations to automate modelling and fast reconstruction (DTU) of the surface to be shotcreted. IRR will facilitate rebar reinforcement through metal additive manufacturing (ANIMA) capitalizing on its precise repair skills (EPFL). The Shotcrete and Finishing mobile manipulator ?SFR (COBOD) will apply dexterous concrete placement through closed-loop visual guidance methods, suitable for turbid conditions (KUL). Concrete mix-design and study on innovative reinforced cementitious materials (TITAN) will contribute to the reduction of materials? and water waste during shotcrete. The SFR robot equipped with universal tool changing (ROB) will also automate the surface finishing step through delicate and human-like robot manipulations, enabled from a safety operation toolkit that includes physical human-robot collaboration and human-aware navigation (CERTH). A Digital Twin (DTT) coupled with simulation tools (SDU), advanced decision making (ICE) and task planning (CERTH) skills will facilitate fast and greener shotcrete automation. RobetArme will be evaluated (DS4) on four diverse construction sites, i.e. tunnels/culverts (BYCN), bridges posttensioned boxes (ARUP), beams & piles of buildings (CEAS) and ground support walls (BYCN), assessing its autonomous shotcreting abilities. RobetArme will substantially increase the repair/maintenance automation, will foster adoption of robots in Construction 4.0 era (EFF), while it will increase construction productivity providing flexibility to the maintenance personnel, contribution to standards (UNI) and better quality to their workplaces (MORE).

The problem addressed by the OPTIMINER project is multi-faceted and significant in the European context. Europe's mining industry is grappling with the pressing need to efficiently and sustainably recover CRMs from increasingly complex and low-grade ores. OPTIMINER offers a comprehensive solution to Europe's mining challenges, encompassing five Key Exploitable Results (KERs). REMINER focuses on advanced technologies for efficient CRM recovery. These include smart ore sorters, membrane-based hydrometallurgical processes, bioleaching, and phytomining. They will comprise a marketplace of technologies that will be offered to the extractive industry through an innovative AI-enabled CRM Recovery Selector. DIGIMINER offers a digital platform for smart monitoring and control. It is enhanced with a Decision Support System and an NLP-based digital assistant, the Virtual Miner. Moreover, Digital Twins of the recovery pilot plants will be created to optimise productivity and provide with simulation capacities. ECOMINER delivers services enhancing sustainability and resilience. It boosts resource management and efficiency by designing a toolkit to optimise energy, water usage and waste valorisation, along with strategies for toxicity management and LCA. Financial sustainability is tackled by using an AI-enabled Market Observatory system to provide knowledge support and resilience to the end user. DEMOMINER showcases pilot lines for CRM recovery across different materials and geographical locations. Specifically, we are proposing 6 use cases in Spain, Greece, Poland, Finland and Chile, while focusing on CRM recovery, namely magnesium, tungsten, REE (esp. Neodymium), Copper, Cobalt and Coking Coal. GLOBEMINER promotes increased awareness and market uptake, highlighting the strategic EU-Chile strategic cooperation. Together, these components form an integrated approach to revolutionize CRM recovery, ensuring environmental sustainability and operational efficiency.

Ashvin aims at enabling the European construction industry to significantly improve its productivity, while reducing cost and ensuring absolutely safe work conditions, by providing a proposal for a European wide digital twin standard, an open source digital twin platform integrating IoT and image technologies, and a set of tools and demonstrated procedures to apply the platform and the standard proven to guarantee specified productivity, cost, and safety improvements. The envisioned platform will provide a digital representation of the construction product at hand and allow to collect real-time digital data before, during, and after production of the product to continuously monitor changes in the environment and within the production process. Based on the platform, Ashvin will develop and demonstrate applications that use the digital twin data. These applications will allow it to fully leverage the potential of the IoT based digital twin platform to reach the expected impacts (better scheduling forecast by 20%; better allocation of resources and optimization of equipment usage; reduced number of accidents; reduction of construction projects). The Ashvin solutions will overcome worker protection and privacy issues that come with the tracking of construction activities, provide means to fuse video data and sensor data, integrate geo-monitoring data, provide multi-physics simulation methods for digital representing the behavior of a product (not only its shape), provide evidence based engineering methods to design for productivity and safety, provide 4D simulation and visualization methods of construction processes, and develop a lean planning process supported by real-time data. All innovations will be demonstrated on 10 real-world construction projects across Europe. The Ashvin consortium combines strong R&I players from 11 EU member states with strong expertise in construction and engineering management, digital twin technology, IoT, and data security / privacy.