Atmospheric corrosion, especially in coastal environments, is a major structural problem affecting metallic structures in various sectors. Structural health monitoring systems based on satellite information can help to ensure the proper behavior of civil structures and are an interesting alternative for remote locations. The aim of this case study is to relate remote sensing information to the results of experimental studies for potential structural damage characterization. The ultimate idea is to characterize any environment without long testing periods or sampling costs. Comparative nondestructive experimental tests involving different locations, sampling techniques, and study periods are performed. The results obtained are analyzed and compared with meteorological satellite data characterization at each site. The experimental test results show sufficient statistical significance (p < 0.05), confirming that the areas potentially most susceptible to corrosion can be identified using information from remote sensing satellites based on orientation, wind conditions, and wind origin. This can be used to facilitate the remote design and monitoring of structures more accurately with a stability guarantee.

Smart Manufacturing is a goal to be achieved, and the most advanced manufacturing approaches are being used to pursue this objective. Within this context, industry development aims to attain an intelligent manufacturing using, for example, virtual models that simulate production lines. This paper presents the architecture of a Digital Twin for emulating the rolls replacement process within a wire rod rolling mill. The model is developed in Python, using a backtracking algorithm to select the suitable set of rolls as a first basic approach for the validation of the system. It may be used in the future to improve the production system automating the decision for the replacement of rolls as alternative to the current human-decision process.