The integration of blockchain and digital twins (DT) for better building-lifecycle data management has recently received much attention from researchers in the field. In this respect, the adoption of enabling technologies such as artificial intelligence (AI) and machine learning (ML), the Internet of Things (IoT), cloud and edge computing, Big Data analytics, etc., has also been investigated in an abundance of studies. The present review inspects the recent studies to shed light on the foremost among those enabling technologies and their scope, challenges, and integration potential. To this end, 86 scientific papers, recognized and retrieved from the Scopus and Web of Science databases, were reviewed and a thorough bibliometric analysis was performed on them. The obtained results demonstrate the nascency of the research in this field and the necessity of further implementation of practical methods to discover and prove the real potential of these technologies and their fusion. It was also found that the integration of these technologies can be beneficial for addressing the implementation challenges they face individually. In the end, an abstract descriptive model is presented to provide a better understanding of how the technologies can become integrated into a unified system for smartening the built environment.

The heterogeneity of the architecture, engineering and construction (AEC) industry reflects on digital building models, which differ across domains and planning phases. Data exchange between architectural design and structural analysis models poses a particular challenge because of dramatically different representations of building elements. Existing software tools and standards have not been able to deal with these differences. The research on inter-domain building information modelling (BIM) frameworks does not consider the geometry interpretations for data exchange. Analysis of geometry interpretations is mostly project-specific and is seldom reflected in general data exchange frameworks. By defining a data exchange framework that engages with varying requirements and representations of architectural design and structural analysis in terms of geometry, which is open to other domains, we aim to close the identified gap. Existing classification systems in software tools and standards were reviewed in order to understand architectural design and structural analysis representations and to identify the relationships between them. Following the analysis, a novel data management framework based on classification, interpretation and automation was proposed, implemented and tested. Classification is a model specification including domain-specific terms and relationships between them. Interpretations consist of inter-domain procedures necessary to generate domain-specific models from a provided model. Automation represents the connection between open domain-specific models and proprietary models in software tools. Practical implementation with a test case demonstrated a possible realization of the proposed framework. The innovative contribution of the research is a novel framework based on the system of open domain-specific classifications and procedures for the inter-domain interpretation, which can prepare domain-specific models on central storage. The main benefit is a centrally prepared domain-specific model, relieving software developers from so-far-unsuccessful implementation of complex inter-domain interpretations in each software tool, and providing end users with control over the data exchange. Although the framework is based on the exchange between architectural design and structural analysis, the proposed central data management framework can be used for other exchange processes involving different model representations.

Building information modelling promises model-based collaboration between stakeholders in the project design stage. However, data exchange between physical and analytical building models used for architectural design and structural analysis respectively rarely takes place due to numerous differences in building element representation, especially the representation of geometry. This paper presents the realization of a novel data exchange framework between architectural design and structural analysis building models, based on open interpretations on central storage. The exchange is achieved with a new system architecture, where the program redDim was developed to perform the interpretations, including the most challenging transformations of geometry. We deliver a proof of concept for the novel framework with a prototype building model and verify it on two further building models. Results show that structural-analysis models can be correctly automatically created by reducing dimensionality and reconnecting building elements. The proposed data exchange provides a base for missing standardization of interpretations, which facilitates the non-proprietary automated conversion between physical and analytical models. This research fills the gap in the existing model-based communication that could lead to a seamless data exchange.

The increasing digitalization and thus evidently advancing change and necessary flexibility in the AEC indus-try, requires new business models, processes and strategies. blockchain (BC) and decentralized applications (DApps) or smart contracts are still underused for the architecture, engineering and construction (AEC) indus-try. The literature review of BC and its potential of inclusion into model-based communication, has shown that it is not just a technology that is ready to use, but requires a thorough insight into the design process of domain-specific stakeholders, their interests and their collaboration workflows for a holistic BIM-BC-supported solution for the design phase. This paper introduces process modeling of domain-specific processes and BIM-workflows in the design phase. We propose a conceptual framework for the implementation of a de-sign process with BC based on the integration of three underlying theories: design theory, configuration theo-ry and task-technology fit. The main premise is, before we can capture a process we need to understand it (de-sign), further in order to re-engineer it for DLT we need to adapt it to changing requirements (configure) and then continually re-adjust IT and business processes interdependence (task-technology fit).