The concept of digital twins is proposed as a new technology-led advancement to support the processes of the design, construction, and operation of built assets. Commonalities between the emerging definitions of digital twins describe them as digital or cyber environments that are bidirectionally-linked to their physical or real-life replica to enable simulation and data-centric decision making. Studies have started to investigate their role in the digitalization of asset delivery, including the management of built assets at different levels within the building and infrastructure sectors. However, questions persist regarding their actual applications and implementation challenges, including their integration with other digital technologies (i.e., building information modeling, virtual and augmented reality, Internet of Things, artificial intelligence, and cloud computing). Within the built environment context, this study seeks to analyze the definitions and characteristics of a digital twin, its interactions with other digital technologies used in built asset delivery and operation, and its applications and challenges. To achieve this aim, the research utilizes a thorough literature review and semi-structured interviews with ten industry experts. The literature review explores the merits and the relevance of digital twins relative to existing digital technologies and highlights potential applications and challenges for their implementation. The data from the semi-structured interviews are classified into five themes: definitions and enablers of digital twins, applications and benefits, implementation challenges, existing practical applications, and future development. The findings provide a point of departure for future research aimed at clarifying the relationship between digital twins and other digital technologies and their key implementation challenges.

The building stock accounts for a significant portion of worldwide energy consumption and greenhouse gas emissions. While the majority of the existing building stock has poor energy performance, deep renovation efforts are stymied by a wide range of human, technological, organisational and external environment factors across the value chain. A key challenge is integrating appropriate human resources, materials, fabrication, information and automation systems and knowledge management in a proper manner to achieve the required outcomes and meet the relevant regulatory standards, while satisfying a wide range of stakeholders with differing, often conflicting, motivations. RINNO is a Horizon 2020 project that aims to deliver a set of processes that, when working together, provide a system, repository, marketplace and enabling workflow process for managing deep renovation projects from inception to implementation. This paper presents a roadmap for an open renovation platform for managing and delivering deep renovation projects for residential buildings based on seven design principles. We illustrate a preliminary stepwise framework for applying the platform across the full-lifecycle of a deep renovation project. Based on this work, RINNO will develop a new open renovation software platform that will be implemented and evaluated at four pilot sites with varying construction, regulatory, market and climate contexts.

Journal of Information Technology in Construction

Buildings have a significant impact on energy consumption and carbon emissions. Smart buildings are deemed to play a crucial role in improving the energy performance of buildings and cities. Managing a smart building requires the modelling of data concerning smart systems and components. While there is a significant amount of research on optimising building energy using the smart building concept, there is a dearth of studies investigating the modelling and management of smart systems’ data, which is the starting point for establishing the necessary digital environment for representing a smart building. This study aimed to develop and test a solution for modelling and managing smart building information using an industry foundation classes (IFCs)-based BIM process. A conceptual model expressed in the SysML language was proposed to define a smart building. Five BIM approaches were identified as potential ‘prototypes’ for representing and exchanging smart building information. The fidelity of each approach is checked through a BIM-based validation process using an open-source visualisation platform. The different prototypes were also assessed using a multi-criteria comparison method to identify the preferred approach for modelling and managing smart building information. The preferred approach was prototyped and tested in a use case focused on building energy consumption monitoring to evaluate its ability to manage and visualise the smart building data. The use case was applied in a real case study using a full-scale demonstrator, namely, the ‘Nanterre 3’ (N3) smart building located at the CESI campus in Paris-Nanterre. The findings demonstrated that an open BIM format in the form of IFCs could achieve adequate modelling of smart building data without information loss. Future extensions of the proposed approach were finally outlined.

Several factors affect the process of BIM adoption within organisations (i.e. micro BIM adoption). An extensive collection of such factors were included in a unified BIM adoption taxonomy published in Ahmed and Kassem (2018). These factors are distributed across three areas of adoption drivers: the characteristics of the innovation itself (i.e., BIM), the external environment, and the internal environment of the adopting organisation. BIM adoption is a multistaged process that entails varying interactions between factors from across the three driver areas and the stages of the BIM adoption process. Hence, this study argues for an improved understanding of the adoption topic beyond the current level offered by common approaches such as ranking factors affecting adoption (conceived as a single decision/milestone) and analysing correlations. In particular, there is a need to analyse the complex inter-dependencies between factors affecting the adoption process and its individual stages (i.e., awareness, intention, decision, etc.). This paper aims to understand these inter-dependencies by considering the micro BIM adoption as a complex system. The paper investigates the relative levels of influence between the factors affecting the system and classifies such factors in cause and effect factors at different stages of the adoption process. To achieve this objective, the research employs the Fuzzy Decision Making Trial and Evaluation Laboratory (F-DEMATEL) method. The application of the F-DEMATEL considered the top 11 factors (as identified in Ahmed and Kassem, 2018) that affect BIM adoption within the UK Architecture sector. The F-DEMATEL was applied for the entire adoption process as a 'single' system (i.e. without separating it into multiple stages) and for each individual stage (i.e. awareness, intention, and decision). The results from the F-DEMATEL (i.e., classification of factors into cause and effect groups and into four quadrants, and their interrelationships) provided a new understanding of the BIM adoptions process. These results can be used to tailor and prioritise BIM implementation actions and investments when develop micro BIM adoption strategies.

To date a comprehensive analysis of interactions among BIM adoption factors, stages of the BIM adoption process, and time (e.g. time intervals of a national BIM initiative) is still lacking. This research aims to profile BIM adoption by mapping such interactions. The analysis is performed for the UK Architecture sector, which was represented with a sample of 177 organisations. To achieve the profiling of BIM adoption in the UK architecture sector, the study uses the outcomes from two types of inferential analysis: an ordinal logistic regression test to identify the 11 top factors influencing the BIM adoption process; and correlation analysis among the 11 top factors at different stages of the BIM adoption process (i.e. awareness, intention, and decision) and across three time intervals (i.e. pre-2011 as the time interval preceding the announcement of the UK BIM mandate, 2011-2016 as the time interval for the implementation of the UK national BIM strategy, and post-2016 as the time interval within which the mandate entered into effect). Capturing the interactions involved in the profiling of micro BIM adoption and assimilating the outcomes in a single model unravel a further understanding of the BIM adoption process. In particular, the results reveal a dynamic behaviour characterising the micro BIM adoption process where: (1) correlated pairs of adoption factors have a varying level of influence within each adoption stage; (2) the factors involved in each pair generally change across the two dimensions (stages of adoption, and time horizon); and (4) the pairs of factors influencing adoption stages over time often combine constructs from the three clusters of drivers identified in [1] (i.e. Innovation/BIM Characteristics