Blast Oxygen Furnace (BOF) slag represents one of the largest waste fractions from steelmaking. Therefore, slag valorisation technologies are of high importance regarding the use of slag as a secondary resource, both in the steel sector and in other sectors, such as the construction or cement industries. The main issue regarding the use of BOF slag is its volumetric instability in the presence of water; this hampers its use in sectors and requires a stabilisation pre-treatment. These treatments are also cost-inefficient and cause other environmental issues. This paper analyses the use of untreated BOF slag from a technical and environmental point of view, suggesting it as an alternative to natural aggregates in road surface layers and asphalt pavements. A comprehensive analysis of the requirements to be met by raw materials used in asphalt mixes was performed, and a pilot test was carried out with two different mixtures: one mix with limestone as coarse aggregate and another with 15% BOF slag. Furthermore, the global warming impacts derived from each mix with different aggregates were measured by Life Cycle Analysis (LCA), and a transport sensitivity analysis was also performed. The results show how the utilization of BOF slag as coarse aggregate in road construction improves the technical performance of asphalt mixtures (Marshall Quotient 4.9 vs. 6.6). Moreover, the introduction of BOF slag into the asphalt mix as a coarse aggregate, instead of limestone, causes a carbon emissions reduction rate of more than 14%.

Land degradation, and especially acidification, are global issues that need to be addressed. A common practice to correct this problem is the use of lime or chemical fertilisers that involve the extraction of raw materials. This study proposes a more sustainable alternative using Basic Oxygen Furnace (BOF) slag. BOF slag is the main waste from the steel industry that is usually accumulated in landfills, which also implies environmental impacts. In this study, a series of laboratory tests have been carried out to analyse the feasibility of using BOF slag for the reclamation of degraded land. For soil acidification, BOF slag will be analysed as a liming agent. On the other hand, the benefits slag can provide as a nutrient source will be tested. As an added value, pre-treated and untreated slag will be compared. The results of these short-time experiments show how BOF slag could be a sustainable alternative as liming agent and amendment. Its use increased the levels of some micro and macronutrients available for plant growth and improved soil quality. It could, therefore, be a sustainable management practice that makes an important contribution to the circular economy.

Galvanised steel atmospheric corrosion is a complex multifactorial phenomenon that globally affects many structures, equipment, and sectors. Moreover, the International Organization of Standardization (ISO) standards require specific pollutant depositions values for any atmosphere classification or corrosion loss prediction result. The aim of this research is to develop predictive models to estimate corrosion loss based on easily worldwide available parameters. Experimental data from internationally validated studies were used for the data mining process, basing their characterisation on seven globally accessible qualitative and quantitative variables. Self-Organising Maps including both supervised and unsupervised layers were used to predict first-year corrosion loss, its corrosivity categories, and an uncertainty range. Additionally, a formula optimised with Newton’s method has been proposed for extrapolating these results to long-term results. The predictions obtained were compared with real values using Euclidean distances to know its similarity degree, offering high prediction performance. Specifically, evaluation results showed an average saving of up to 16% in coatings using these predictions. Therefore, using the proposed models reduces the uncertainty of the final structures state by predicting their material loss, avoiding initial over-dimensioning of structures, and meeting the principles of efficiency and sustainability, thus reducing costs.

Atmospheric corrosion, especially in coastal environments, presents a major challenge for the long-term durability of metallic and concrete infrastructure due to chloride deposition from marine aerosols. With a significant portion of the global population residing in coastal zones—often associated with intense industrial activity—there is growing demand for accurate and early corrosion prediction methods. Traditional standards for assessing atmospheric corrosivity depend on long-term empirical data, limiting their usefulness during the design stage of infrastructure projects. To address this limitation, this study develops predictive models using machine-learning techniques, namely gradient boosting, support vector machine, and neural networks, to estimate chloride deposition levels based on easily accessible climatic and geographical parameters. Our models were trained on a comprehensive dataset that included variables such as land coverage, wind speed, and orientation. Among the models tested, tree-based algorithms, particularly gradient boosting, provided the highest prediction accuracy (F1 score: 0.8673). This approach not only highlights the most influential environmental variables driving chloride deposition but also offers a scalable and cost-effective solution to support corrosion monitoring and structural life assessment in coastal infrastructure.

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.

Wind abrasion, caused by particles transported by strong winds impacting on structures, can lead to their degradation. Although this phenomenon has hardly been studied in this context, it is becoming increasingly important due to new trends in infrastructure location, especially in renewable energy terms. Metallic structures are particularly vulnerable to degradation by the action of windblown sand particles. However, characterising such secluded sites is complicated, and remote sensing systems and satellite information become crucial. The objective of this research is to identify and delineate the geographic areas that are vulnerable to this phenomenon by employing a hybrid model with historical data and the semi-automatic classification of multispectral satellite images. The model is based on critical variables identified by the scientific community and case studies documented in the literature. The methodology used for the study consists of four phases, including creating a scientifically robust database, downloading and managing satellite and historical long-term information, segmenting the regions of interest, and modelling using supervised classification techniques. The proposed algorithm shows very accurate results (R² = 0.9922) and the overall system approach is presented as a useful and generalizable method to address this problem, increasing the existing knowledge on material wear by particle action, and contributing to optimizing the initial design of resilient structures.