Nowadays, buildings renovation is a subject of special interest since the building and construction sector is the main body responsible for energy consumption and emissions. Hence, it is necessary to concentrate on refurbishment to achieve Europe’s climate neutrality by 2050 according to European Agenda goals. The BIM4EEB Project, a BIM-based fast toolkit for the efficient renovation of residential buildings, directs the attention toward developing an exhaustive toolkit based on Building Information Modeling (BIM) to be adopted in the renovation of existing residential buildings, to make the flow of information efficient, decreasing intervention working time while improving building performances, quality, and comfort for inhabitants. BIM4EEB is developing a BIM management system connected to an operational and multifunctional toolkit for various architecture, engineering, and construction (AEC) stakeholders, integrating a set of tools for improving BIM adoption in renovation environments based on an interoperable flow of information. This paper presents the Horizon2020 Project and the framework used to develop the toolkit. In addition, the first outcomes of the toolkit development are outlined. The validation procedure in real environments has started to demonstrate the efficacy and applicability of the methodology and tools. Although the project is still in progress, benefits connected to the framework and the BIM-based toolkit result in an enhanced building renovation process.

In the last few years, the technology re-evolution has deeply transformed several aspects of everyday life. For sure, one technology with a strong impact is the so-called Internet of Things (IoT). The IoT paradigm made it possible to break down the data barrier between the vertical domains on which the traditional information and communication technology (ICT) world was organized. Recently, the designers of home automation systems have begun looking to the IoT paradigm to ease the deployment of systems that are able to collect data from different plants. Such a situation has driven further evolution from the traditional automation system, where logic is defined by the programmer or by the user, to a cognitive system that is able to learn from the user’ habits regarding what should be the best configuration of plants. Several countries are funding renovations of public and private buildings for improving energy efficiency. Generally, such renovations are only focusing on the structure of the building and of its energy performance (e.g., the thermal envelope, window units, air-conditioning plants, and renewable generators) and largely ignoring the use of intelligent devices. On the contrary, scientific literature and practice have demonstrated that the wider use of IoT sensors, as well as distributed and remote intelligence, is fundamental to optimize energy consumption. This research work aimed to identify issues due the application of cognitive solutions during the renovation phase of buildings. In particular, the paper presents a cognitive architecture to support the operation and management phases of buildings, thanks to the massive digitalization of the entire supply chain of the construction sector from the single building element to the entire construction process. Such an architecture is capable of combining data from the IoT sensors and actuators of smart objects installed during the renovation phase, as well as legacy building automation systems. As an indication of the capability of the proposed solution, an intelligent window device was developed and validated. Within the Energy, Life Styled, and Seismic Innovation for Regenerated Buildings (ELISIR) project, window units are equipped with sensors to monitor indoor and outdoor condition behaviours of users. In addition, windows are able to react to changes in the environment by means of actuators that enable motorized opening and shading. Thanks to the cognitive layer designed in the project, the window is able to automatically define the best rules for opening and shading by using the local controller to satisfy user’s habits and energy efficiency targets. The cognitive layer defines the appropriate rules for opening and shading using the decision tree algorithm applied to the data generated by the sensors in order to infer users’ preferences. For this research, two prototypes of the window units were installed in two offices of Politecnico di Milano, Italy. The accuracy of this algorithm to classify the users’ behaviour and preferences was found to be around 90%, considering an observation interval of two months.

The architecture, engineering, construction, and operations industry is distinguished by having plenty of and a variety of data, which makes the acquisition, storage, retrieval, and use of information difficult. Due to a data exchange system primarily based on paper-based transmission, multiple classification systems, the use of inconsistent criteria and practices, and a significant number of stakeholders involved during the building life cycle (each with distinct requirements and levels of access to information), the entire construction process must deal with ineffective information exchange among actors. Instead of multiple sources of information and tools to collect, store, and share data, one single source of information could become a reference point for numerous stakeholders. In this regard, a digital building logbook is assumed to be a collector of building-related data starting from the design phase, which plays a fundamental role in information management. This paper proposes a systematic literature review aimed at identifying the main features of the tool, investigating its growth in the construction sector. The results show that the digital building logbook’s main application is in the operations and maintenance field with relevance to renovation. However, a common model is absent, varying greatly based on the country and category of building. This analysis contributes to increasing awareness by identifying the attributes, gaps, and potentialities of the subject matter.