The digital transformation of the Architecture, Engineering and Construction sector is accelerating the adoption of digital platforms as critical enablers of data integration, stakeholder collaboration and process optimization. This paper presents a systematic review of 125 peer-reviewed journal articles (2015–2025), selected through a PRISMA-guided search using the Scopus database, with inclusion criteria focused on English-language academic literature on platform-enabled digitalization in the built environment. Studies were grouped into six thematic domains, i.e., artificial intelligence in construction, digital twin integration, lifecycle cost management, BIM-GIS for underground utilities, energy systems and public administration, based on a combination of literature precedent and domain relevance. Unlike existing reviews focused on single technologies or sectors, this work offers a cross-sectoral synthesis, highlighting shared challenges and opportunities across disciplines and lifecycle stages. It identifies the functional roles, enabling technologies and systemic barriers affecting digital platform adoption, such as fragmented data sources, limited interoperability between systems and siloed organizational processes. These barriers hinder the development of integrated and adaptive digital ecosystems capable of supporting real-time decision-making, participatory planning and sustainable infrastructure management. The study advocates for modular, human-centered platforms underpinned by standardized ontologies, explainable AI and participatory governance models. It also highlights the importance of emerging technologies, including large language models and federated learning, as well as context-specific platform strategies, especially for applications in the Global South.

The application of multimodal data in road maintenance has attracted considerable attention due to its potential to enhance decision-making processes and improve infrastructure resilience. This paper ...

Despite its great potential, Augmented Reality (AR) still struggles to be widely used in real processes in the construction industry. This is mainly due to limitations associated with current AR registration methodologies, including the lack of continuity between different scenarios, drifts over distances, and the need to prepare the scene in advance with alignment infrastructures and to resort to manual registration procedures. In addition, users may not be skilled enough or allowed to prepare the considered environment, since this is a task that requires a significant amount of measurement and calibration. To promote the application of AR in Facility Management (FM), which typically involves both complex indoor and outdoor environments, an automatic localization method is needed, thus providing accurate AR registration in mixed and unprepared environments. In order to fill this gap, a system implementing a markerless approach for seamless indoor-outdoor AR registration has been developed by integrating multiple AR registration engines with a cloud platform. These engines, primarily relying on Global Navigation Satellite Systems Real-Time Kinematic (GNSS-RTK) and Computer Vision (CV) technologies, are managed by an additional system component that automatically assigns priority depending on the current scenario. The system proposed in this study is tested on site on an FM use case related to a university campus for qualitative assessment. Furthermore, a quantitative assessment of the system’s accuracy is also performed. In both evaluations, the system shows very promising results in terms of (i) applicability for FM operations, and (ii) accuracy. Specifically, BIM holograms are automatically superimposed to their real counterparts seamlessly, in mixed indoor-outdoor scenarios, without manual procedures, demonstrating applicability of the proposed approach in unprepared environments. In terms of accuracy, the AR overlaying discrepancies in outdoor and indoor scenarios (D_(L-SQ)) result equals 0.090 m and 0.082 m with a maximum among all the analyzed scenes (D_M) of 0.075 m and 0.071 m, respectively. Given these results, the work presented in this paper provides a substantial contribution to the dissemination of AR technologies in the FM field and to the support of FM activities in terms of better coordination, visualization, communication and on-field access to high-quality information. This paper extends the work presented at the 23rd International Conference on Construction Applications of Virtual Reality (CONVR 2023).

Manual geometric and semantic alignment of inspection data with existing digital models (field-to-model data registration) and on-site access to relevant information (model-to-field data registration) represent cumbersome procedures that cause significant loss of information and fragmentation, hindering the efficiency of civil infrastructure inspections. To address the bidirectional registration challenge, this study introduces a high-accuracy automatic registration method and system based on Augmented Reality (AR) that streamlines data exchange between the field and a knowledge graph-based Digital Twin (DT) platform for infrastructure management, and vice versa. A centimeter-level 6-DoF pose estimation of the AR device in large-scale, open unprepared environments is achieved by implementing a hybrid approach based on Real-Time Kinematic and Visual Inertial Odometry to cope with urban-canyon scenarios. For this purpose, a low-cost and non-invasive RTK receiver was prototyped and firmly attached to an AR device (i.e., Microsoft HoloLens 2). Multiple filters and latency compensation techniques were implemented to enhance registration accuracy. The system was tested in a real-world scenario involving the inspection of a highway viaduct. Throughout the use case inspection, the system seamlessly and automatically provided field operators with on-field access to existing DT information (i.e., open BIM models) such as georeferenced holograms and facilitated the enrichment of the asset’s DT through the automatic registration of inspection data (i.e., images) with the open BIM models included in the DT. This study contributes to DT-based civil infrastructure management by establishing a bidirectional and seamless integration between virtual and physical entities.

Since statistics show a growing trend in blindness and visual impairment, the development of navigation systems supporting Blind and Visually Impaired People (BVIP) must be urgently addressed. Guiding BVIP to a desired destination across indoor and outdoor settings without relying on a pre-installed infrastructure is an open challenge. While numerous solutions have been proposed by researchers in recent decades, a comprehensive navigation system that can support BVIP mobility in mixed and unprepared environments is still missing. This study proposes a novel navigation system that enables BVIP to request directions and be guided to a desired destination across heterogeneous and unprepared settings. To achieve this, the system applies Computer Vision (CV)—namely an integrated Structure from Motion (SfM) pipeline—for tracking the user and exploits Building Information Modelling (BIM) semantics for planning the reference path to reach the destination. Audio Augmented Reality (AAR) technology is adopted for directional guidance delivery due to its intuitive and non-intrusive nature, which allows seamless integration with traditional mobility aids (e.g., white canes or guide dogs). The developed system was tested on a university campus to assess its performance during both path planning and navigation tasks, the latter involving users in both blindfolded and sighted conditions. Quantitative results indicate that the system computed paths in about 10 milliseconds and effectively guided blindfolded users to their destination, achieving performance comparable to that of sighted users. Remarkably, users in blindfolded conditions completed navigation tests with an average deviation from the reference path within the 0.60-meter shoulder width threshold in 100% of the trials, compared to 75% of the tests conducted by sighted users. These findings demonstrate the system’s accuracy in maintaining navigational alignment within acceptable human spatial tolerances. The proposed approach contributes to the advancement of BVIP assistive technologies by enabling scalable, infrastructure-free navigation across heterogeneous environments.

Information flow management has historically been a problem in the construction industry. Fragmentation of processes within the sector, interdisciplinarity, and lack of unambiguous methods for passing information are some of the obstacles that make it difficult to have a non-splintered flow of data. BIM approach is a valuable support for optimal information flow management. NLP with vocal assistants can facilitate the access and understanding of the information to non-technical users. This research proposes the development of a system for querying BIM models through natural language and without the use of specific keywords.