Advances in Science and Technology Vol. 173

Abstract: Faced with today's critical issues in the management and maintenance of infrastructures - due to factors such as age, structural complexity and the limitations of traditional inspections - this study systematically addresses the entire set of maintenance issues, proposing the integrated use of innovative digital technologies as an answer. The research presents an advanced methodology based on the synergy between Building Information Modeling (BIM), Internet of Things (IoT) and Machine Learning (ML), with the aim of supporting the predictive and dynamic management of infrastructures through the creation of Digital Twins. In this paper, this methodology is applied to two case studies of bridges in Italy, which are currently being monitored. The application focuses on the initial stages of the process: digital modelling of the works (with the creation of BIM models containing static data), installation of sensors for the collection of dynamic data in real time, and subsequent integration of this information for the generation of a functional Digital Twin. This unified model allows not only the management of warning systems based on predefined thresholds, but also the elaboration of in-depth structural diagnoses to support the planning of maintenance interventions. The proposed methodology also represents a strategic step towards the evolution of advanced predictive models, favouring a more proactive, efficient and sustainable approach to maintenance.

Abstract: The domestic construction industry continues to face persistent productivity challenges, often leading to increased project costs and schedule delays. Despite considerable advances in digital technologies, the practical implementation of digital solutions at construction sites remains limited, due to insufficient consideration of realistic working conditions and user needs. In this study, a digital supervision system optimized for mobile devices was developed to digitize core supervision processes, including inspection request confirmation, inspection execution, result notification, and access to supervision records. Investigation was adopted, involving experts with over 15 years of field experience, to systematically evaluate the usability and field applicability of the system. The assessment focused on four key domains: physical accessibility and usability, improvement of work efficiency, ease of learning and accessibility, and reflection of existing work practices. Both importance and performance were rated for each domain, and the gaps were analyzed to identify priority areas for improvement. The results revealed that the integration with existing workflows was the most critical factor for successful adoption, yet also exhibited the greatest performance gap. While the system excelled in data management and retrieval, there remained a strong need for further automation and enhanced user guidance, particularly in inspection execution. All participants expressed a high intention to use the system in the future, underscoring its potential value despite current limitations. This study highlights the necessity of user-centered approaches in digital system development and provides practical recommendations for advancing the digital transformation of construction supervision. Future research should expand the sample size and construction types to validate and refine these findings.