The World Health Organization (WHO) reports that all over the world there exist around 2.2 billion people who are visually impaired or having low vision. Being this trend growing, the development of navigation assistants supporting Blind and Visually Impaired People (BVIPs) is of urgent need. Over the last few decades, researchers have developed systems that can make BVIPs more independent and aware of their surroundings. This contributes to create a human-centered built environment that prioritizes occupants, focusing on their needs, well-being, and experiences. Nevertheless, an integrated solution that can support BVIPs in both indoor and outdoor unprepared environments is still missing. To address this need, this study proposes an Audio Augmented Reality (AAR) navigation assistant for BVIPs that integrates Global Navigation Satellite Systems (GNSS) and Computer Vision (CV) technologies. Such a system, deployed as a smartphone-based application, enables high-accuracy user localization seamlessly in both indoor and outdoor scenarios and without any manual procedure. Furthermore, AAR technology, superimposing audio information into people’s physical 3D environments, is applied to provide audio spatial guidance to BVIPs. Experimental tests, carried out in a university campus, show the possibility for eye-folded users to follow a reference path with a meter-level divergence.

Improving safety conditions at work is still a major challenge worldwide. Most fatal accidents occur in the AECO sector, regardless of the stringency of safety measures or the frequency of inspections. In addition, worksite safety is still assessed by traditional methods involving manual checklists and inspections that are mostly carried out visually. Support for inspection procedures at construction sites could be introduced through the implementation of innovative technologies, but the digitization that is sweeping the construction industry still struggles to address safety aspects. Official documents such as safety plans do not yet find a shared expression in the BIM approach, and innovative technological tools are not yet widely used. In this scenario, the proposed research work aims to support inspection processes through BIM modeling, the use of Augmented Reality and blockchain. These technologies are also integrated with a seamless indoor and outdoor localization system. Finally an information flow from site to office is pursued through image gathering and registering in a platform for real-time verification support configuring this way a true Digital Twin for safety management.

The digitization that is impacting the construction sector still encounters areas where it is hindered. Safety is one of them—in Italy, even health and safety plans have to be digitally modeled. In this article, a methodology is presented for the digitization of health and safety plans and their visualization on-site using augmented reality and a seamless system for indoor–outdoor localization. Safety requirements for equipment are modeled as customized property within BIM models. Then, the interoperable-format IFC is used to upload the model in a BIM platform. On-site outdoor localization is ensured by a GPS-RTK system, while for indoor spaces, an artificial intelligence algorithm that recognizes features is used. In this research, an application that supports a seamless outdoor–indoor transition is proposed, with the display of inspection information through augmented reality and a blockchain notarization of images taken on-site and aligned with BIM models. The results of indoor and outdoor alignment are presented below. The experiment regarding augmented reality information display and the photo notarization procedure are also reported. This methodology improves the site inspection process by supporting the traceability of operations.

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.

Over the last couple of decades, significant efforts have been made to develop structural health monitoring solutions. The growing need for the dynamic characterization of structures supports the implementation of condition assessments, maintenance, and monitoring strategies for existing and new civil engineering structures, and to provide increased safety for the public. Wireless monitoring systems are still being improved as the technology is finding a wider use for the monitoring of civil engineering structures, thanks to their easier installation and reduced costs when compared to the wired counterparts. In this context, this paper presents a new wireless network system for the dynamic characterization of civil engineering structures, whose distinguishing features comprise combining cutting-edge accelerometers, excellent signal synchronization, low battery consumption nodes, and a cloud-based framework to support the monitoring operations. The performance characteristics are validated through laboratory tests and are demonstrated on a newly constructed 211 m tall building.

Managing the built environment according to the principles of the Circular Economy (CE), with the aim of limiting energy consumption and environmental pollution, is a highly topical issue. In this context, digitization can play a crucial role in promoting virtuous scenarios. However, the review of the literature reveals a distinct lack of in-depth investigation of the intersection between promotion of strategies related to the CE of the built environment using a digital approach, as Digital Twin (DT). In fact, in a sector characterized by low permeability to the introduction of new technologies, introducing digitization with the aim of greater efficiency is not easy. In recent years the DT approach has been gaining ground, namely, the development of digital copies that make it possible to investigate or simulate scenarios in real time. This brief review aims to examine the existing studies and to identify challenges and further research needs. The results will ensure a deeper understanding of the topic, facilitating its development and promoting circular building management practices.

Navigation systems combined with Augmented Reality (AR) constitute an effective solution for helping user accessing unfamiliar environments, like indoor public facilities. Implementing AR navigation systems requires determining the 6-Degrees-of-Freedom (6-DoF) localization of the user. In indoor environments, lack of Global Navigation Satellite System (GNSS) signals makes localization more challenging compared to outdoor environments. A variety of positioning systems have emerged for indoor localization which are based on several system strategies, location methods, and technologies. High-accuracy, low-cost, easy to use, and no need for any technical expertise are key features to ensure large-scale application of AR navigation systems. This study provides an answer to these key requirements by proposing a markerless infrastructure-free localization system for AR registration in indoor environments based on the comparison between a query image and a 3D mapping assumed as a reference. Since most people in public facilities are equipped with tablets or smartphone devices, they should be provided with further functionalities that will help them in day-to-day life and work. To this purpose, the proposed localization system was implemented as a web-service and tested in a university campus to assess applicability for AR registration purposes. Experiment results showed very promising localization accuracy and computational efficiency satisfying the “fine-precision” accuracy threshold (i.e., 1°/0.100 m) for near real-time AR applications.

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).

The rapid and global aging of population is outlining the need for environments that can provide support for these individuals during their daily activities. The challenge of an aging society is being addressed through the incorporation of new technologies into the home environment, which is nothing less than Ambient Assisted Living (AAL). To date, some of the AAL solutions exploit AI models to recognize the elderly’s behaviors through data collected by sensors. In recent times, Digital Twins (DTs) at building level have begun to appear on the construction domain. These are still under development but through the integration of users into assessments, they improve efficiency, prevention, and prediction of likely events through real-time AI computing. The integration of DT and AAL defines cognitive buildings which aim to learn at scale, reason with a purpose, and co-operate with users in a natural way. This research aims to develop DT models to achieve scenario awareness to provide support to elderly people living alone and suffering from cognitive disorders. The proposed multi-agent architecture is based on a five-layer system that autonomously develops high-level knowledge to detect anomalies in the home environment scenarios and therefore support the user. Bayesian networks (BNs) are exploited to perform high-level deductive reasoning on low-level multi-modal information, thus recognizing senseless or dangerous behaviors, environmental disruptions, changes in behavioral patterns, and serious medical events. Bi-directional user-system interaction provides user support by leveraging Speech-To-Text and Text-To-Speech AI agents. Three main functions were tested: real-time data integration, anomaly detection, and two-way interaction.

BPMN choreography is a modeling language capable to describe scenarios where several independent participants have to collaborate in a climate of opposing interests and therefore are forced to trust each other. For this reason, in many contexts, a strong need for transparency, responsibility, and choreography compliance arise by the various participants. Blockchains and smart contracts, thanks to their characteristic of providing a decentralized and consensus‐based validation mechanism, seem to be able to meet these needs in an untrusted scenario. Nevertheless, most of the related work focused either on transparency, accountability, or compliance, but none on all three of them. Furthermore, such works do not take into account the nondeterministc nature of choreographies. This work aims at using blockchains and smart contracts in this scenario providing a formally well‐defined set of tools to match all three the aforementioned requirements. This work applies the proposed techniques to a case study from the construction industry, an economical relevant application domain where the demand for transparency, accountability, and compliance with procurement contracts (that can be modeled as choreographies) is very strong.

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.

The management of the built environment is a topic that requires reference to the management of complex systems. In fact, the variety of domains involved means that the management of urban centers is not only complicated, and therefore it is not enough to model a set of rules that are representative of phenomena related to the real environment. Not only that, but what is evident is that emergency management lacks the ability to access real-time information that could be decisive. Having tools that provide real-time data, that reprocess it, and that are able to provide an enriched and slightly predictive view of what is happening offers the possibility of having a real impact in the management of the built environment. In this sense, digital twins are a valuable approach to achieving the desired results. Digital twins through the integration of technologies such as lnternet of Things (IoT), simulators, Artificial Intelligence (AI), and Augmented Reality (AR) technologies make it possible to develop systems capable of exploiting the concept of collective intelligence, in a digital version, through a large number of heterogeneous agents working according to stigmergic mechanisms. This research work aims to propose its own architecture of digital twins for the management of resilient urban centers, with particular reference to the management of post-earthquake reconstruction scenarios

Resilient management of the built environment is a strategic objective, made even more evident by recent events, not only in terms of managing chronic stresses such as social, economic and financial stresses, but especially emergency situations. Human settlements can be considered complex systems and when disaster strikes, the degree of complexity is amplified exponentially. Thus, traditional management systems, based on classical paradigms, may be completely ineffective. Therefore, new management approaches are needed to implement safety, resilience and sustainability. In this context, this paper illustrates a research where Digital Twin (DT) is exploited as an adaptive system for the built environment, as a support to optimize post-disaster reconstruction processes with a focus on reactive security management, in order to implement resilience in a smart city perspective. Following this vision, the research project aims to realise a first extended framework enabling the implementation of digital twins in the built environment, then defining some DT demonstrators in the laboratory.

Abstract. In this contribution a point cloud classification in an urban context has been presented. The aim of the work is to test a semi-automatic classification approach and to verify its usefulness in the scan-to BIM process, and to validate how much it is straightforward for the definition of different point cloud LODs. The work methodology is structured in three phases. The first concerns data acquisition and processing through geomatic instruments and methodologies that guarantee a complete and expeditious survey such as ground-based MMS and UAV for aerial photogrammetry. The second phase concerns the testing of an online software that per-forms point cloud classifications through AI algorithms. The system allows either to use standard classifiers that are already available, or to create a customizable catalogue of the different classes that one wants to attribute to the urban scene. Following the automatic classification process, where all objects have been identified, manual corrections can be made to improve the classification of objects into specific classes. The third step is object detection and extraction. Here, the relationship between automatic classification, point cloud density, object identification and the various degrees of LOD definition was explored. The higher the LOD, the greater the number of objects that can be identified, particularly those elements related to street furniture and urban facilities. Once these objects have been classified, it is then possible to extract them in interoperable format. This allows such data to be managed and shared through BIM platform.