Projects
Current Projects
Aquila: AI-Powered Digital Twin for Construction Sites
(1st Sept 2024 – 1st Sept 2025)
This project aims to develop Aquila, an AI-powered Digital Twin platform that transforms productivity and sustainability on construction sites. In collaboration with world-leading organizations like BIM Academy, XBIM, Gray Fox Consulting, Costain Group Plc, and Northumbria University, Aquila tackles key industry challenges such as low equipment utilization and resource management inefficiencies. By leveraging real-time data, AI-driven analytics, and 4D visualizations, Aquila establishes bi-directional control loops between the construction site and the project control office, enhancing decision-making for equipment usage, safety monitoring, and scheduling. This leads to significant reductions in costs, delays, and carbon emissions. Aquila’s core AI functionalities include real-time monitoring, predictive analytics, and AI-assisted decision-making to optimize equipment use and work programs. The platform aligns with the UK's Industrial Decarbonization Strategy and the government’s Transforming Infrastructure Performance roadmap, supporting both economic growth and environmental targets. Key milestones in the project include selecting and testing AI methods for productivity and safety monitoring, deploying cloud computing infrastructure, and integrating IoT, mobile applications, and 4D dashboard visualizations. These elements will create a comprehensive, data-driven solution tailored to the needs of the construction sector.
Wilson (Horizon Europe): Distributed data modelling and Federated Digital Twinning for lifecycle data-driven sustainable operation and management of buildings and districts
(1st May 2024 – 30th April 2028)
The WILSON consortium is formed of 16 partners across 10 countries. The main objective of WILSON is to enable more efficient and sustainable data use in the built environment by leveraging cutting-edge technologies and innovative management practices.
To do so, WILSON presents a holistic, extensible, and decentralised approach to data management, agnostic to and interoperable with existing proprietary Building Management System (BMS) and Digital Twin (DT) systems. This is integrated with a set of tools for both energy and non-energy applications, aiming to increase the availability of performance indicators relevant to the built environment as well as supporting the creation of new services and sustainable financing schemes. To ensure integration and market uptake across the data lifecycle, WILSON will be demonstrated in 4 different countries and mixed typologies, showing its adaptability and performance in real-case scenarios. Newcastle University is the Research and Development partner leading the implementation of WILSON use cases (decentralised data mesh architecture and predictive maintenance) in the Newcastle Helix innovation district, and developing the building and district digital twins.
Project website: https://wilson-project.eu/
RINNO (Horizon 2020): Building a Low Carbon, Climate Resilient Future: Secure, Clean and Efficient Energy
(1st June 2020 - 31st August 2025)
RINNO is a Horizon 2020 project that aims to deliver a set of processes that when working together give a system, repository, marketplace, and enabling workflow process for managing deep renovation projects. The ultimate objective of RINNO is to dramatically accelerate the rate of deep renovation in the EU by reducing the time, effort and cost of deep renovation while improving energy performance and stakeholder satisfaction. This concept has at its foundation, a set of cost-attractive, environmentally friendly, multi-functional and easily applicable building-related innovations, grouped into: - plug-n-play, modular building envelope solutions; - RES, hybrid and storage solutions. RINNO couples the above with innovative retrofitting processes, methods and tools, characterised by low tenants’ disruption. These comprise: efficient (off/on-site) construction strategies, on-the-job AR facilitating environment and multi-stakeholder collaboration, which are expected to shrink the time and cost required for deep renovation, while improving buildings’ performance; all with a short payback period of <4 years on average. The above will be supported business-wise with novel business models aligned with the circular economy principles, enriched with investment de-risking tools and advanced crowd-equity/crowd-lending schemes. RINNO is expected to impact the EU inefficient building stock by: - Contributing to an ambitious annual renovation rate of 3.5% - Primary energy savings of 165 GWh/year - A reduction of electricity cost by at least 30% - A total cost/time reduction in comparison with typical renovation by more than 30% and 40% respectively - An estimated reduction of 40,400 tons CO2-eq/year. RINNO optimised renovation roadmap will be demonstrated at 4 large-scale (3,386 m²) pilot use cases in Poland, Greece, Denmark and France after being pre-piloted, covering different EU climatic zones and markets of diverse maturity in the renovation sector.
Project website: https://rinno-h2020.eu/
Design for Manufacturing and Assembly DfMA for Industrialised Construction using Knowledge-Based Engineering (Merit)
(15th August 2024 - 14th August 2026)
The Knowledge-Based Engineering (KBE) project aims to develop and integrate a knowledge graph ontology with automation design tools to streamline design and manufacturing processes. The project focuses on implementing a knowledge graph API for external access from design software such as Revit/Dynamo and Rhino/Grasshopper, allowing users to retrieve essential product data, including cycle times, machinery, and production lines. The project supports design automation with modular algorithms and parametric approaches, specifically aimed at optimising manufacturing systems and components.
Building an Architectural Knowledge Base and Chatbot Assistant (Ryder)
(1st Nov 2024 – 30th Oct 2026)
Collaborating with Ryder Architecture, a leading architectural firm, this project addresses the challenge of efficiently managing their extensive unstructured data. By employing advanced techniques such as Retrieval-Augmented Generation and Knowledge Graphs, we are developing reliable pipelines to enable selective and precise information retrieval. This data engineering initiative forms a cornerstone of the Ryder AI System, providing the infrastructure needed to surface insights effectively and automate content generation to support the Architectural design process.
