This article makes a comparison between different Digital Image Correlation methods to determinate the main mechanical characteristics of composite materials. More specifically Carbon Fiber Reinforced Polymers. For this purpose, several tensile tests were carried out using the same camera and lens model. Different statistical methods as well as probabilistic numerical simulations were performed with the aim of evaluating the discrepancies between methods, and between different mechanical parameters. We want to highlight the consistency of the results, enabling the possibility of using 3D methods with non-planar specimen for determining the mechanical properties of Carbon Fiber Reinforced Polymers. In this case, the novelty is focused on the use of different configurations (2D and 3D) to study the differences in terms of results. the objective is not the specific characterization of CFRP, but to analyze the way in which the use of a dataset from DIC3D or, on the contrary, from DIC2D affects the final results. According to this, it is possible to concluded that significative differences arise in the evaluation of the elastic properties that could be assigned to the uncertainties of the methods. However, this significance does not appear in the results of the probabilistic simulation.

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.

Earth building technologies are increasingly being used to promote a natural and sustainable construction model and to empower self-building in resource-limited areas. This work focuses on investigating the use of different types of stabilising additives in compressed earth blocks (CEBs). To this end, empirical studies and laboratory analyses of earth samples taken from different sites in Ecuador were combined. Once the most suitable earth for use as a building material was determined, four types of CEBs were produced using equipment designed ad hoc to encourage self-building: earth-based, fibre additives, cementitious additives, and additives of other origin. The panels were characterised by means of compression tests to analyse their mechanical behaviour, obtaining the most promising results for the additivated samples with the highest percentage of cement and for the sample containing ground reeds, with a compressive strength of 3.3 MPa and 0.7 MPa, respectively. These samples were then subjected to more extensive tests using digital image correlation to analyse their full field strains and cracks, where the samples stabilised with cement showed a more homogeneous and consistent behaviour. Finally, an economic and comparative study with conventional construction systems was carried out to demonstrate the feasibility of using the proposed earth materials for cleaner and more economical buildings, mainly due to cost savings and lower pollution in terms of transport when using local resources.

This work focuses on the automatic identification of forest fire risk areas along high-voltage power lines through the development of a tool and its validation on a real forest area. The tool allows one to automate the whole process, which includes the classification of the point cloud, the computation of the catenary of the wires using different calculation methods, the estimation of the vegetation growth and the identification of the risk areas. To this end, a coarse-to-fine approach is proposed, so that a preliminary analysis is performed with public airborne LiDAR data, and then a more detailed inspection is provided with drone LiDAR data over those areas classified as high risk. The tool and the methodology developed were validated along a high-voltage power line of 53 km in a real forest area. The results show that although the preliminary analysis based on public airborne LiDAR data is more conservative, it is very useful for selecting those areas of higher risk for further analysis with drone LiDAR data.