AI-Based Self-Learning System in Distributed Structural Health Monitoring and Control

Abstract

Artificial intelligence is predicted to play a big part in self-learning, industrial automation that will negotiate the bandwidth of structural health and control systems. The industrial structural health and control system based on discrete sensors possesses insufficient spatial coverage of sensing information, while the distributed condition monitoring has been mainly studied at the sensor level, relatively few studies have been conducted at the artificial intelligence level. This paper presents an innovative method for distributed structural health and control systems based on artificial intelligence. The structural condition was divided into regional and local features, the feature extraction and characterization are performed separately. Structural abnormality recognition and risk factor calculation method were proposed by considering the response values and the distribution patterns of both the regional and the local structural behaviours. The test results show that the method can effectively identify the full-scale and local damage of the structure, respectively. Subsequently, structural safety assessment method for long-span structures at kilometres level in view of fully length strain distributions measured by distributed fiber optic sensors were developed. A series of load tests on the long-span structure were carried out. Finite element (FE) model was developed using finite element code, ABAQUS, and an extensive parametric study was conduct to explore the effect of load cases on the structural responses. The differences in the structural response results among load test, structural safety assessment and FE simulation were investigated. It is shown that AI-based self-learning system could offer suitable speed in deployment, reliability in solution and flexibility to adjust in distributed structural health monitoring and control.