# Structural Health Monitoring: Latest Applications and Data Analysis
###### Abstract
Today, this area is one of the main branches of work on current control systems, and in the context of the rapid development of artificial intelligence algorithms, it will be an integral part of practical measurement systems. Advanced analysis methods make it possible to search for non-obvious and complex relationships and correlations between data, which allows for achieving ever-higher efficiency and accuracy rates of newly developed systems and technologies. This modern trend is also visible in the articles published in this Special Issue relating to a wide range of areas: from assessing the condition of operating building structures of various sizes to testing the properties and state of materials. In [4], the verification of the random forest algorithm for non-invasive assessment of excessively damp and salty historical brick walls is presented. The developed method, which provides quantitative assessment, was validated using archival research and experimental tests in two historical buildings. The results showed satisfactory linear correlation coefficients and low absolute and relative errors, confirming the effectiveness of the random forest algorithm for evaluating the moisture content of brick walls without destructive intervention. Advanced data processing techniques are also used in the research presented in article [5], where the authors emphasize the importance of continuous and reliable dam safety monitoring data for the evaluation and control of dam operation and security. The article proposes a cokriging spatial model based on variable importance for data repair, considering the spatial and environmental factors that influence dam safety. This model aims to improve the accuracy of data repair and ensure that monitoring data reflect the actual safety conditions of the dam. In article [6], authors investigate the use of magnetic Barkhausen noise MBN combined with classical machine learning methods to assess the grade and magnetic anisotropic properties of electrical sheets. The study compares the performance of classical machine learning and deep learning approaches using 26 algorithms. The results show that while the highest accuracy was achieved by machine learning models based on artificial neural networks and ensemble bagged trees, the deep learning model consistently outperformed classical machine learning methods by approximately 10% in terms of accuracy. A neural network approach is also utilized in [7]--this paper addresses the challenge of detecting hidden corrosion at rivet sites in aircraft structures. The proposed approach combines machine learning and electromagnetic testing to effectively identify the corrosion of various sizes and locations. The results demonstrate that the approach can detect 89.48% of hidden corrosion with a training data utilization of only 20%, which can be further improved to 99.0% with 60-80% of the training data. The results presented in the aforementioned works confirm the need to take into account artificial intelligence algorithms in the implementation of newly built SHM systems, and the wide range of presented applications indicates the large scale and importance of the problem in the context of future development. Aside from the previously mentioned aspects, the authors of this Special Issue placed significant emphasis on subjects pertaining to the advancement of novel materials and methodologies for their fabrication. In [8], the authors discuss the ultrasonic testing of Ti6 Al4V material produced using conventional and laser bed fusion methods, with a focus on additive manufacturing. Additive manufacturing, due to a different production process to standard methods, can significantly affect the properties of the materials produced, which determines the need for a different approach to the utilized monitoring techniques. The study aims to determine the acoustic properties of the material and develop an inspection scheme for adhesive joints in additive manufacturing components.