Digital twins (DTs) represent an emerging technology that allows interaction between assets and their virtual replicas and enclose geometry from modeling procedures and dynamism from AI. DTs serve different purposes, e.g., testing how devices behave under diverse conditions or monitoring processes and supporting improvement. However, until now, the use of DTs for monitoring constructions has been limited, as they are frequently used only as a high-quality 3D digital representation without connecting to other systems, dynamic analysis, or simulation. This work proposes creating a DT for monitoring the construction of a wind farm. It draws a comparison between the as-designed models (from the design phase) and the as-built models (that represent the actual construction at different times). As a result, the DT can help to control deviations that may occur during construction. The authors propose using Unreal Engine to create an interface that includes as-designed models obtained from the building information modeling (BIM) and as-built models corresponding to different steps during the construction. The result is a video game-type interactive application with a timeline tool that allows going through the construction stages recorded in the as-built models and comparing them to the as-designed model.

Multi-agent systems integrate a great variety of artificial intelligence techniques from different fields, these systems have made it possible to create intelligent systems more efficiently. On the other hand, virtual reality applications are accepted as viable techniques in different areas such as visualization, simulation, design, and research. The combined use of these two technologies has led to the development of realistic and interactive applications. This work aims to do a Systematic Mapping Study (SMS) relying on the guidelines of Kitchenham and Petersen to analyze the state of the art of VR applications using multi-agent systems. Inclusion and exclusion criteria have been applied to identify relevant papers, 82 articles were selected and categorized according to the publication type, the research type, the asset type, and the purpose of the work. A complete review of the 82 selected articles was performed, based on the research questions that were established. This review made it possible to clarify the open lines of research that exist and to know where research in this field can be directed.

This article presents a comprehensive forensic tool for crime scene and traffic accident investigations, integrating advanced 3D reconstruction and semantic and dynamic analyses; the tool facilitates the accurate documentation and preservation of crime scenes through photogrammetric techniques, producing detailed 3D models based on images or video captured under specified protocols. The system includes modules for semantic analysis, enabling object detection and classification in 3D point clouds and 2D images. By employing machine learning methods such as the Random Forest model for point cloud classification and the YOLOv8 architecture for object detection, the tool enhances the accuracy and reliability of forensic analysis. Furthermore, a dynamic analysis module supports ballistic trajectory calculations for crime scene investigations and the vehicle impact speed estimation using the Equivalent Barrier Speed (EBS) model for traffic accidents. These capabilities are integrated into a single, user-friendly platform offering significant improvements over existing forensic tools, which often focus on singular tasks and require expertise. This tool provides a robust, accessible solution for law enforcement agencies, enabling more efficient and precise forensic investigations across different scenarios.

Digital twins (DTs) represent an emerging technology that can allow interaction between physical assets and their virtual replicas