The work progress control is a key aspect for correcting deviations in construction, but currently is a task still carried out very manually by personnel moved to the execution place. This work proposes to digitize and automate the procedure through the combination and contrast of digital models of the actual state of the work and the theoretical planning. The models of the real situation are generated from the laser scanning executed by drones, the theoretical planning is reflected in the BIM4D models of the project, and their combination is automated with Feature Manipulation Engine (FME) visual programming routines. A web-based digital twin platform allows access to the end user of the service in an agile way. The methodology developed has been validated with its application on a residential building in the structural erection phase in Helsinki (Finland).

Post-blast operations in tunnel construction represent a critical bottleneck due to mandatory downtime and hazardous environmental conditions. This study addresses these challenges by developing and validating an integrated cyber–physical architecture that coordinates an autonomous Unmanned Aerial Vehicle (UAV) and an Autonomous Wheel Loader (AWL) under the supervision of a Digital Twin acting as central operational digital interface. Specifically, this technology was designed to access the tunnel, evaluate post-blasting conditions, and initiate operations during mandatory exclusion periods for personnel. The system was validated in a realistic, Global Navigation Satellite System (GNSS)-denied tunnel environment emulating post-detonation visibility constraints. The results demonstrate that the aerial agent successfully navigated and mapped the excavation front in less than 8 min, establishing a shared coordinate system for the ground machinery. Through this collaborative workflow, the autonomous deployment enabled operations to commence 50% to 80% earlier than conventional manual procedures. Furthermore, the system reduced daily operational time by approximately 8%, with an estimated return on financial investment between one and seven months. Overall, the proposed framework eliminates human exposure during high-risk inspections and transforms the fragmented excavation cycle into a continuous, data-driven process.

The design of bridge decks requires accurate transverse live load distribution; however, existing simplified methods like beam models fail to capture cross-section bending and distortion, while traditional Load Distribution Factors (LDFs) provide only approximate values for the most stressed beam. This study introduces a novel method to fill this gap by applying a virtual model that reduces the structural problem to two degrees of freedom per longitudinal rib: deflection and rotation. Unlike prior models, this approach explicitly accounts for increased torsional and transverse bending stiffness in box sections and incorporates distortion through a reduced shear area calculation. The methodology was validated through a parametric study of 75 box-girder decks involving 175 load states, alongside two experimental tests: a small-scale laboratory section and a full-scale load test of the Arlanzon Viaduct. Findings demonstrate a maximum divergence of less than 14% compared to traditional Finite Element Methods (FEM), with a mean error below 8%. Experimental results showed an accuracy level comparable to complex FEM models. This method significantly optimizes calculation time and material resources, offering a reliable alternative for bridge design using modern materials without requiring high computational power.

Road maintenance remains one of the most resource-intensive and hazardous operations in infrastructure management. Traditional inspection practices rely heavily on manual labour and discrete procedures, often resulting in limited scalability, operator exposure to traffic hazards, and inefficiencies in data collection. This paper presents a novel automated methodology that integrates Unmanned Aerial Vehicles (UAVs) and autonomous mobile robots (AMRs) to enable automated inspection and measurement of road assets through a digital twin (DT) system. The system leverages data fusion and real-time synchronisation between field agents and a centralised digital twin to monitor the retro-reflectivity of vertical and horizontal signage, detect obstacles and vegetation, and support data-driven maintenance planning. A case study conducted on the Italian highway network demonstrated improvements in operational safety, inspection efficiency, and measurement consistency. The results confirm that the integration of UAVs and AMRs within a digital twin framework can significantly improve sustainability, productivity, and workers’ safety in road maintenance operations.