Damage detection in reinforced concrete bridge: an ANN-based approach for inspection

Bridges and viaducts play a fundamental role in the logistics infrastructure of a region. Early damage detection is essential for these assets′ managers to promote less invasive and lower-cost maintenance, and to extend their service life. This work presents an artificial intelligence (AI) based approach for detecting and locating damage in reinforced concrete bridges and viaducts. The methodology combines artificial damage scenarios obtained through a finite element model calibrated based on field tests and uses Artificial Neural Networks (ANN) to diagnose a case study viaduct’s condition. The model adjustment was achieved using field test data of the structure’s dynamic characteristics, and by adjusting the material properties of its structural elements and the boundary conditions. Multiple damage scenarios were created in key structural elements with different severity levels. Two neural networks were developed to diagnose the structure’s health: the first detects and locates the damage, and the second estimates its severity. The proposed algorithms exhibited the necessary accuracy for an accurate diagnosis. The proposed methodology can be used as an auxiliary tool for inspection and maintenance planning of the studied asset.