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).

The maintenance of industrial buildings is essential to ensure the operability, safety, and efficiency of these facilities, which are key pillars of the industrial sector. Proper maintenance management has a direct impact on productivity and the continuity of operational processes. However, managing the maintenance of these built assets pose significant challenges, particularly in terms of efficiency in inspection, diagnosis, and the planning of corrective and preventive activities, requiring substantial investment to preserve functionality and prevent disruptions. To address these challenges, this paper presents the development of an intelligent system for advanced damage diagnosis in industrial buildings. The system integrates the MIVES multi-criteria decision-making methodology to objectively assess the structural condition of built assets, incorporating complementary technologies: drones for inspection; artificial intelligence (AI) for data analysis and automatic damage detection; extended reality (XR) for the relocalisation of previously captured environments and the spatial positioning of identified damage; and radiance fields (such as 3D Gaussian Splatting) for 3D scene reconstruction and digital twin generation. A prototype workflow for damage detection was developed by training deep-learning algorithms trained on field images of pathologies in industrial built assets and generating digital twins of the facility using photogrammetry techniques and 3D Gaussian Splatting. This workflow supports the detection and visualization of potential pathologies and provides the data necessary for their objective assessment within the MIVES-base condition condition-assessment framework. These developments are integrated into an application that allows managers and asset owners to access and track inspection information through structured damage records, spatial localization, and digital visualization tools that support maintenance planning and decision-making.