No abstract

The AEC industry is nowadays one of the most hazardous industries in the world. The construction sector employees about 7% of the world's work force but is responsible for 30-40% of fatalities. As statistics demonstrate, interferences between workers-on-foot and moving vehicles have caused several injuries and fatalities over the years. Despite safety organizational measures, passive safety devices imposed by regulations and efforts from training procedures, scarce improvements have been recorded. Recent research studies propose technology driven approaches as the key solutions to integrate standard health and safety management practices. This is motivated by the evidence that the dynamics of complex systems can hardly be predicted; rather a proactive approach to health and safety is more effective. Current technologies installed on construction equipment can usually react according to a strict logic, such as sending proximity alerts when workers and equipment are too close. Nevertheless, these approaches barely do make informed decisions in real-time, e.g. including the level of reactiveness of the endangered worker. In similar circumstances a digital twin of the construction site, updated by real-time data from sensors and enriched by artificial intelligence, can pro-actively support activities, forecasting dangerous scenarios on the base of several factors. In this paper a laboratory mock-up has been assumed as the test case, supported by a game engine, which is able to replicates the job site for the execution of bored piles. In such a scenario populated by an avatar of a sensor-equipped worker and a virtual driller, a Bayesian network, implemented within the game engine and fed in runtime by sensor data, works out collision probability in real-time in order to send warnings and avoid fatal accidents.

High fragmented and hardly accessible information often leads to struggling management of diffused assets. BIM and GIS integration is promising to develop effective digital Asset Management Systems (AMS) to facilitate information sharing and collaborative management. This paper presents a replicable methodological approach to develop a pilot BIM-GIS, web-based AMS for the University of Turin. The main aim is to overcome document-based and fragmented management, avoiding ineffective decisions during the operational and maintenance phase. The key step of integrating data from several heterogeneous sources in an accessible, centralized database is deep in described. Furthermore, two demonstrators are illustrated, discussing the first results and AMS potentials.

Natural Language Processing (NLP) is widely used to solve several tasks in different construction fields. However, there are no applications related to the predesign phase. Analysing the Italian public design call for tender procedure, possible information criticalities can be identified. The study proposes a formalization, from a System Engineering (SE) perspective, of the implementation of an NLP-based system in the pre-design phase of an Italian public procedure. SE focuses on the formalization of main stakeholders, system values and functions via IDEF0, Analytical Hierarchy Process (AHP), and Quality Function Deployment (QFD).

Digital Twins (DTs) and process digitalization are promising to bridge the gap towards Product Lifecycle Management (PLM) in construction industry. In a PLM view, DTs should born in the early design as virtual Prototypes (DTPs) useful as the basis for future DT Instances (DTIs) to manage the whole lifecycle. DTPs could help to overcome a discrete project performances view and enable an holistic one, exploitable for bids evaluation besides performance and sustainability optimization. The research adopts a PLM view to define a methodology aimed at developing a DTPs System which could lead a disruptive change in tenders evaluation, enhancing Green Public Procurement adoption.