Resumen
Stress state evaluation in axially loaded structural members is significant for sustaining and preserving the service life of buildings. While successful monitoring furnishes staunch information on the health, integrity, safety and serviceability of structures, maintaining the structural performance of a building with time significantly depends on assessing the occurrence. Variations in the stress in axially loaded members may occur in masonry buildings or space structures caused by different conditions and human-induced factors. In the last decades, numerous nondestructive methods have been generated to furnish practical means for identifying axial load in the tie-rods of masonry buildings and in the structural members of space structures. Significant effort has been put into dynamic-based approaches, which make use of the vibrational response of the monitored member to investigate its condition and evaluate the axial load. In particular, wide laboratory and field tests have been executed worldwide, resulting in several findings. Meanwhile, with flourishing sensing technology and computing power, Artificial Intelligence (AI) applications, such as hybrid methods, optimization techniques and deep learning algorithms, have become more practicable and widely used in vibration-based axial stress prediction, with efficiency and, frequently, with strict precision. While there have been various manuscripts published on dynamic-based axial stress evaluation, there are no works in which the passage from traditional methods to combinations with AI approaches have been illustrated. This article aims to address this gap by introducing the highlights of the traditional methods, and furnish a review of the applications of AI techniques used for nondestructive-based axial stress prediction in tie-rods and structural members. Conclusions, including further studies and field developments, have also been mentioned at the end of the article.