Damage localization and quantification in offshore jacket structures using signal processing and intelligent system

Abstract

Offshore platforms operate in harsh environmental conditions, and a significant portion of their structure is submerged underwater. Consequently, inspection and repair operations are costly and complex, making it crucial to have a precise damage detection system to increase safety and reduce maintenance expenses. This paper presents an intelligent signal processing-based system for damage detection in offshore jacket platforms. First, two damage indicators based on the signal's entropy and power are introduced to localize damage in jacket-type offshore structures. Then, an intelligent system that combines an artificial neural network and several signal processing techniques is used to quantify damage in various parts of these structures. To this end, various signal processing techniques extract the response signal features. Next, the proposed neural network model is trained using a data set, including the extracted feature vectors and the severity of the known damage scenarios. As a case study, an offshore jacket platform installed in the Persian Gulf was examined to evaluate the proposed method's effectiveness. The results reveal that the provided method detects the damage locations and severities with high processing speed and accuracy. Further, selecting suitable features and performing initial processing on training feature vectors can increase the accuracy of detecting damage intensity.