Buildings significantly contribute to climate change. Reusing structural components from buildings, particularly concrete—the most widely used construction material—offers a promising pathway within a Circular Economy to reduce this impact. However, demonstrating the feasibility of this approach and investigating supportive methods and tools is essential. Therefore, our research focuses on the reuse of precast concrete, aiming to reduce its carbon footprint and advance sustainable construction practices.

The ability to share data can facilitate cooperation and decision making throughout the entire life cycle of buildings, from initial stages of planning, through design and construction, up to the management of assets and towards end of life and recycling or reuse. Accordingly, the different actors of the Construction industry can share data and functionalities across software platforms through automated processes. Such processes involve stakeholders with heterogeneous backgrounds; for this reason, it is of value to make data available to people without expert knowledge of specific programs or computer systems.

This study is concerned with digital protocols for automated capturing of real-time sensor data for the assessment of building performance. The research explores a Large Language Model (LLM) driven protocol for sensor data acquisition to evaluate building performance, leveraging the existing database where building data is stored. Using pre-written prompts which instruct how to carry out tasks in a step-by-step manner, the LLM can utilize pre-defined functions built upon API calls for communication with sensor data, including data acquisition, post-processing, and interpretation. The LLM receives instructions by the User using natural language. Initial tests underscore the protocol feasibility, highlighting its potential utility to improve the data communication of existing digital twins for individuals without professional expertise in building management. A case study exemplifies how human instructions in natural language trigger the LLM to invoke a request for indoor climate sensor data in a building.

The developed tool was tested by professionals working in the Construction industry and the Educational Sector to provide feedback on its practical application. This evaluation helped to identify weaknesses for future developments and proved the flexibility and adaptability of the method.

As advancements in Artificial Intelligence (AI), particularly in LLMs, continue to surge, there is anticipation for further refinements. This includes cost reduction, enhanced stability, and the integration of more advanced functionalities such as advanced data analysis using machine learning coded by LLM.

This study proposes a concept and workflow for solar, context-adaptive and reusable facades. Integrating solar control with parametric facade design, the workflow uses solar radiation to inform facade modules with variable openness or properties (e.g. frit cover), enabling envelopes to adapt to urban context changes while promoting circularity. The method was tested through simulations, assessing daylight, glare, energy and circularity in changing urban scenarios. A Solar Circularity Indicator (SCI) was introduced to track façade alterations and reuse. In the 100 m new obstruction scenario, 79% of facade modules were maintained, while 29% of altered modules were reused, yielding an 85% SCI. Sunlight Autonomy metrics aligned well with SCI. Re-design improved Spatial Daylight Autonomy by up to 4% with minimal energy increase (<1%). Our solution provided 2% more useful daylight (100–3000 lux) than glazed facades and 11% less glare. The workflow provides a framework for circular, performance-based designs that preserve aesthetics and adaptability.

Due to urbanization and growing density in cities in the past century, metrics were introduced to assess daylight performance such as minimum sunlight hours and the daylight factor. The paper initially explores the shortcomings of early-stage daylight and sunlight evaluation methods. A novel methodology called Sunlight Autonomy (SA) is proposed for evaluating sunlight performance in buildings. The SA is based on the “Exposure to sunlight” criteria in EN 170307 “Daylight in Buildings,” where a computational method is used for the evaluation on a specified day. The SA concept expands the analysis temporally over the entire year, and spatially on building facades, leading to new metrics for a point of evaluation, and spatial metrics for buildings. The SA methodology is analyzed in a case study across four European cities. The SA metrics on facades between February 1st and March 21st, days in EN 17037, led to differences up to 63%. This revealed a significant shortcoming in EN 17037, relevant for Nordic regions. The differences of spatial metrics between March 21st and 50% of the year were within 5%, and up to 33% between February 1st and 75% of the year. The timestep affects the metrics and a window evaluation showed that the error of a 10-minute analysis was within 5% of daily insolation and 5 days for the annual SA. The potential of these metrics for urban planning and the architectural design process is examined. The interaction between SA and EN 17037, as well as other ongoing research developments, is discussed.

Concrete is the most used construction material, accounting for 8% of global CO₂ emissions. Various strategies aim to reduce concrete's embodied carbon, such as using supplementary cementitious materials, utilizing cleaner energy, and carbonation. However, a large potential lies in reusing concrete for new buildings in a Circular Economy, thereby closing material loops and avoiding CO₂ emissions.

This study focuses on the reuse of precast concrete elements. We present a digital workflow for assessing reuse by predicting the remaining service life, estimating CO₂ uptake by natural carbonation, and calculating the embodied carbon savings of concrete reuse. Both carbonation rates from EN 16757 and our investigation were applied to a case study building.

While EN 16757 rates suggest that most precast elements have reached the end of their service life, our assessment shows that these elements have a sufficient lifespan for reuse. Plaster and coverings significantly delay carbonation and extend service life. During the first service life following EN 16757, carbonation was 19,2 kg CO₂/m³, whereas our prediction was 5,4 kg CO₂/m³. Moreover, CO₂ uptake during service life, including reuse, was less than 6% of the embodied carbon. The climate benefits of reuse greatly exceeded those of carbonation.

Furthermore, carbonation did not have a decisive influence when applying Cut-Off, Distributed, and End-of-Life allocations for assessing embodied carbon of re-used elements in subsequent life cycles. The digital workflow is useful in quickly assessing lifespan, carbonation, and embodied carbon of concrete. It can be leveraged as a decision-making tool when designing for reuse.

Several assessment methodologies have been proposed to measure the environmental impact of buildings. However, these methodologies require processing data which is often not available or requires a high integration effort. In this paper, we propose an ontology to describe the use and reuse of prefabricated components in buildings. This ontology describes the relation between the physical object, the building component, with the digital object that represents the element in the building information model. We show that this ontology can be used to answer questions like which building components have been reused and which activities were involved in the life cycle of a building. 

Nowadays, the Digital Twin (DT) concept is one of the most talked-about topics in the building sector. Previous research has addressed both the definition of DT concept and the development of applications for specific purposes, such as facility and asset management, built environment performance monitoring etc. However, the specificity of such applications reduces both its replicability and understanding. This results in the spread of confusion when approaching such concepts. For this reason, this study explores how DTs can be effectively used for educational purposes. The methods used include the integration of several systems and tools, highlighting different approaches for data integration and capture. The proposed methodology was tested on a real case study, the U-building on the KTH Campus in Stockholm, which is equipped with a Building Management System (BMS). The building is used as a pilot in master courses to allow students to produce a first attempt of using a DT, by connecting real-time monitored data and digital objects. The results show how efficient data communication helps towards the organization and assimilation of complex concepts. Furthermore, the testing of several approaches proves the replicability and flexibility of the method within different environments and for several uses.

Concrete production contributes to around 8-9% of global CO₂ emissions. Reusing building components in a circular economy can contribute to closing material loops and lowering CO₂ emissions. When reusing concrete elements, it is necessary to have effective methods for evaluating their reuse potential. In this study, a novel digital workflow is developed to support the reuse of precast concrete elements by evaluating their lifespan based on carbonation depth. The workflow relies on automated retrieval of material quantities and information from a digital model. This model is then coupled with environmental data on construction products and calculation methods for CO₂ uptake in concrete by carbonation. The remaining service life of concrete elements was calculated for a case study. For reference, CO₂ uptake during the first service life was estimated at 4973 kg CO₂ or 4% of the embodied carbon. Hence, the potential benefits of reuse outweigh those of carbonation. The presented approach supports the decision-making process when evaluating the reuse potential for concrete elements. The digital workflow can help designers make quick decisions concerning the lifespan and carbon footprint of concrete. The digital tool can be extended in future work with more parameters to evaluate additional sustainability indicators.

This paper reviews digital tools for supporting the Circular Economy (CE) in the built environment. The study provides a bibliometric analysis and focuses on computer-aided design (CAD), building information modeling (BIM), and computational plugins that can be used by practitioners. While Life Cycle Assessment (LCA) is the primary methodology for evaluating buildings' environmental performance, the study identifies tools beyond LCA, including computational methods and circularity indicators, that can support the evaluation of circular design strategies. Our review highlights limitations in tools’ functionalities, including a lack of representative data for LCA and underdeveloped circularity indicators. The paper calls for further development of these tools in terms of interoperability aspects, integration of more sources of data for LCA and circularity, and possibilities for a comprehensive evaluation of design choices. Computational plugins offer greater flexibility, while BIM-LCA integrations have the potential to replace dedicated LCA software and spreadsheets. Additionally, the study identifies opportunities for novel digital methods, such as algorithms for circular design with various types of reused building elements, and sharing of digital twins and material passports. This research can inform future studies and support architects and engineers in their efforts to create a sustainable built environment.

The purpose of this deliverable is to develop a Common Data Environment (CDE) model as well as workflow solutions for the ReCreate project to effectively store, share, and exchange data related to precast concrete reuse across the project’s country clusters.