Archetypal Use of Artificial Intelligence for Bridge Structural Monitoring

• Published in: Applied sciences 2020-10, Vol.10 (20)
• Main Authors: Chiaia, Bernardino, De Biagi, Valerio
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Bridges; Evaluation; Structural stability
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app10207155

Structural monitoring is a research topic that is receiving more and more attention, especially in light of the fact that a large part our infrastructural heritage was built in the Sixties and is aging and approaching the end of its design working life. The detection of damage is usually performed through artificial intelligence techniques. In contrast, tools for the localization and the estimation of the extent of the damage are limited, mainly due to the complete datasets of damages needed for training the system. The proposed approach consists in numerically generating datasets of damaged structures on the basis of random variables representing the actions and the possible damages. Neural networks were trained to perform the main structural monitoring tasks: damage detection, localization, and estimation. The artificial intelligence tool interpreted the measurements on a real structure. To simulate real measurements more accurately, noise was added to the synthetic dataset. The results indicate that the accuracy of the measurement devices plays a relevant role in the quality of the monitoring. Keywords: structural health monitoring; damage detection; damage localization; hybrid approach; neural network