Anna Osello’s research while affiliated with University of Turin and other places

The Architecture, Engineering, Construction, and Operations (AECO) industry is facing digital transformation led by new information management methods like Building Information Modelling (BIM), Extended Reality (XR) technologies, and Artificial Intelligence (AI). This change is driven by technological advances that optimize data management and the need to assist humans, according to Society 5.0’s model.

This systematic review aims to synthesise Web-GIS evidence for managing natural hazards to share state-of-the-art practices and policies. A comprehensive search in SCOPUS, among other databases, identified 1775 articles published between 2014 and 2023. Following a selection process based on the PRISMA model, 65 articles met the inclusion criteria. The analysis revealed a growing trend over the past decade, with most research concentrated in the last three years. Eight crucial subtopics within the Web-GIS domain have emerged: Integrated Spatial Analysis and Modelling , Technologies and Infrastructure, Visualisation and User Interface Design, Decision Support Systems, Real-time Monitoring and Early Warning, Disaster Recovery and Resilience, Citizen and Social Media Integration, and Multi-Stakeholder Collaboration. A substantial contribution of the literature has been identified in Decision Support Systems and Integrated Spatial Analysis, reflecting their vital role in strategising and predicting hazard impacts. Furthermore, a geographical distribution analysis revealed significant Web-GIS applications in countries like Italy and China, alongside a deficit in low-and middle-income countries. It also highlights potential gaps in hazard studies , including the need to prioritise heatwave management in the face of climate change. This research calls for policymakers and practitioners to leverage evidence-informed decision making and foster community collaboration for enhanced natural disaster resilience.

In this work, we present a novel distributed software platform for patients with neurodegenerative diseases that affect motor neurons. The linking point between this wide range of diseases is the strong social impact they have, degrading the freedom of action of the patient; the loss of functionality of motor neurons, caused by progressive degradation, makes the body unable to move. Thus, our solution aims at making patients more autonomous in their daily activities and at improving remote health-care monitoring for medical staff by combining virtual reality, building information modeling (BIM), the Internet of Things (IoT), and eye-tracking technologies. Starting from the 3-D parametric BIM model, patients can virtually explore the surrounding environment (through eye movements) and control the environment by interacting with IoT smart devices, thus performing different tasks.

Shared attention effects on learning and memory demonstrate that experiences are amplified when we are not alone. Virtual reality poses new challenges to the study of co-presence. Above all, is coattending together with someone else’s avatar in an immersive VR setting comparable with shared experiences at a neural processing level? In the present study we investigate shared attention effects in VR for the first time. We recorded mismatch negativities (MMN) during an auditory roving paradigm, a well-known index of implicit perceptual learning. EEG responses to deviant and standard sounds were registered while subjects were alone (Solo condition) or together (Other condition) with a virtual avatar (Virtual scenario) or physically present confederate (Physical scenario). We found an overall main effect of co-presence on MMN revealed by a point-by-point 2 × 2 ANOVA, thereby replicating previous studies on physical co-presence. Additionally, we found no significant interaction between the scenario (Physical vs. Virtual) and co-presence (Solo vs. Other). Our results indicate that virtual immersive co-presence mimics physical co-presence.

Reinforcement Learning (RL) has emerged as a promising approach to solve complex problems in many different domains, including the Architecture, Engineering, Construction and Operation (AECO) industry. RL is a type of machine learning that focuses on training an agent to interact with an environment in order to maximize a reward signal. In the AECO industry, RL has been used to optimize building design, construction planning and scheduling, and building energy management. This contribution presents an application of RL for the generation of an InfraBIM model of a tunnel built by mechanised excavation using a Tunnel Boring Machine (TBM). In particular, the present application was developed to minimise the distance between the theoretical layout and the operational one to be executed by the TBM, considering the geometry of the ring and the structural joints required for the solidity of the structure. RL has the potential to improve efficiency, sustainability, and safety in the AECO industry by enabling intelligent decision-making and optimization across different phases of the construction process.

Over the years, the use of Building Information Modelling (BIM) methods has changed the workflows of various actors, providing better integration across different domains and allowing for new ways of working. This paper outlines the interest towards the integration of BIM with geotechnical/geological modelling (GeoBIM), which still represents a major challenge in several respects. The present study aims to provide a flexible method for assessing various integration strategies and to establish a preferred workflow based on the selected requirements and preference parameters. This integration is designed to provide a final federated model that can also be archived in the as-built documentation. Four alternative processes were selected to analyse the specific types of data and transformations required in the process. Each process was applied on a real case study in order to test the developed assessment framework using the Analytical Hierarchy Process (AHP). Due to technological advances, modelling and integration workflows change rapidly; however, our results show that the proposed evaluation framework is universal and adaptable to any new integration approach. Further research efforts must be undertaken to reduce attribute data losses and enhance the interoperability between systems and professionals.