The EU Green Deal, beginning in 2019, promoted a roadmap for operating the transition to a sustainable EU economy by turning climate issues and environmental challenges into opportunities in all policy areas and making the transition fair and inclusive for all. Focusing on the built environment, the voluntary adoption of rating systems for sustainability assessment is growing, with an increasing market value, and is perceived as a social responsibility both by public administration and by private companies. This paper proposes a framework for shifting from a static sustainability assessment to a digital twin (DT)-based and Internet of Things (IoT)-enabled dynamic approach. This new approach allows for a real-time evaluation and control of a wide range of sustainability criteria with a user-centered point of view. A pilot building, namely, the eLUX lab cognitive building in the University of Brescia, was used to test the framework with some sample applications. The educational building accommodates the daily activities of the engineering students by constantly interacting with the sensorized asset monitoring indoor comfort and air quality conditions as well as the energy behavior of the building in order to optimize the trade-off with renewable energy production. The framework is the cornerstone of a methodology exploiting the digital twin approach to support the decision processes related to sustainability through the whole building’s life cycle.

Digitisation of the built environment is seen as a significant factor for innovation in the Architecture, Engineering, Construction and Operation sector. However, lack of data and information in as-built digital models considerably limits the potential of Building Information Modelling in Facility Management. Therefore, optimisation of data collection and management is needed, all the more so now that Industry 4.0 has widened the use of sensors into buildings and infrastructures. A literature review on the two main pillars of digitalisation in construction, Building Information Modelling and Internet of Things, is presented, along with a bibliographic analysis of two citations and abstracts databases focusing on the operations stage. The bibliographic research has been carried out using Web of Science and Scopus databases. The article is aimed at providing a detailed analysis of BIM–IoT integration for Facility Management (FM) process improvements. Issues, opportunities and areas where further research efforts are required are outlined. Finally, four key areas of further research development in FM management have been proposed, focusing on optimising data collection and management.

Purpose: This paper provides a critical review of developments in the adaptability of buildings. The purpose of this paper is to determine the current “state-of-the-art”, describe current thinking and trends in research and practice, and identify issues and gaps that further research can address. It provides a basis for a scientific and practical understanding of the interdependencies across different design criterion. This paper increases the awareness of architects, engineers, clients and users on the importance of adaptability and its role in lowering impacts over the lifecycle of buildings as part of the infrastructure system.Design/methodology/approach: This paper draws mainly from the literature as its source of evidence. These were identified from established databases and search engines (e.g. Scopus, ISI Web of Knowledge and Google Scholar) using keywords such as adaptability, adaptable, adaptation, and flexibility. Over 80 sources including books, journal papers, conference proceedings, research reports and doctoral theses covering the period 1990 to 2017 were reviewed and categorised. An inductive approach was used to critically review and categorise these publications and develop a framework for analysis.Findings: The concept of adaptability includes many dimensions which can broadly fall into two categories: changes to buildings and user adaptations to buildings. However, previous research has mostly focussed on the former, with many attempts to identify building attributes that facilitate adaptability, and some considerations for its assessment. Key areas that have not been adequately addressed and which require further research include: user/occupant adaptations, cost, benefits and implications of various adaptability measures, and the development of a standardised assessment methodology that could aid in decision making in the design stage of buildings.Research limitations/implications: The adaptability strategies considered in this review focussed mainly on building components and systems, and did not include the contribution of intelligent and smart/biological systems. The coverage is further limited in scope due to the period considered (1990-2017) and the exclusion of terms such as “retrofit” and “refurbishment” from the review. However, the findings provide a solid basis for further research in the areas identified above. It identifies research issues and gaps in knowledge between the defined needs and current state-of-the-art on adaptive building for both research and practice.Originality/value: This paper is a review of research into a highly topical subject, given the acknowledged need to adapt buildings over their lifecycle to environmental, economic or social changes. It provides further insights on the dimensions of adaptability and identifies areas for further research that will contribute to the development of robust tools for the assessment of building adaptability, which will enhance the decision-making process of building design and the development of a more sustainable built environment.

BIM is representing a shift in the traditional process of building delivery. Its adoption in US reached 71% in 2012 rising from 17% in 2007; moreover, Europe is going to adopt BIM for public contracting as promoted by the European Union Public Procurement Directive. Meanwhile, BIM is widely diffused in UK and Northern Europe, as it includes a more accurate documentation, less rework and shorter project timelines. The use of BIM to provide data for energy performance evaluation and sustainability assessment is defined Green BIM and pioneering design organizations are adopting this approach to enable integrated design, construction and maintenance towards Net Zero Energy buildings. Green BIM includes Building Energy Modelling dealing with project energy performance to identify options optimising building energy efficiency during the life cycle. By allowing revisions during the design phase, project teams can ensure that customers’ green ambitions beyond regulation compliance can be realized, together with technical and economic requirements. Thus, BIM can provide information to support the calculation of a number of credit points to define goal levels of sustainability related to rating systems. The aim of the paper is to investigate the opportunity to include the “green dimension” in BIM considering the more diffused rating systems

The built environment is responsible for roughly 40% of global greenhouse emissions, making the sector a crucial factor for climate change and sustainability. Meanwhile, other sectors (like manufacturing) adopted Artificial Intelligence (AI) to solve complex, non-linear problems to reduce waste, inefficiency, and pollution. Therefore, many research efforts in the Architecture, Engineering, and Construction community have recently tried introducing AI into building asset management (AM) processes. Since AM encompasses a broad set of disciplines, an overview of several AI applications, current research gaps, and trends is needed. In this context, this study conducted the first state-of-the-art research on AI for building asset management. A total of 578 papers were analyzed with bibliometric tools to identify prominent institutions, topics, and journals. The quantitative analysis helped determine the most researched areas of AM and which AI techniques are applied. The areas were furtherly investigated by reading in-depth the 83 most relevant studies selected by screening the articles’ abstracts identified in the bibliometric analysis. The results reveal many applications for Energy Management, Condition assessment, Risk management, and Project management areas. Finally, the literature review identified three main trends that can be a reference point for future studies made by practitioners or researchers: Digital Twin, Generative Adversarial Networks (with synthetic images) for data augmentation, and Deep Reinforcement Learning.