Automated identification of infrastructure damage using real-time monitoring and machine learning

Rapid advances in infrastructure health monitoring and sensing technologies allow monitoring of assets continuously and in real-time throughout their life span. However, smart and automated techniques for decision-making (e.g. maintaining or improving infrastructure performance) are in their infancy. The revolution in new sensing capabilities has led to rapidly increasing volumes of data, which makes traditional data analysis techniques inadequate. Adoption of Big Data (BD) analytics and Artificial Intelligence (AI) techniques is urgently needed to automatically integrate information from multiple sensors, extract knowledge and inform decision-making.

A novel damage identification and localisation approach is proposed, which utilises multi-level data fusion and anomaly detection techniques. Numerical models of a simply supported bridge and a continuous bridge were simulated for data generation.

Accelerations, deflections and bending moments obtained at multiple sensor locations when the bridge is subjected to a moving vehicle were used as the input. A damagesensitive feature was established via coupling principal component analysis with the Mahalanobis distance, allowing for initial data dimensionality reduction and information integration. Anomaly detection using a deep convolutional autoencoder was performed to identify the presence of damage on the bridge. It is demonstrated that the proposed approach is independent of the mass and speed of the moving vehicles. The approach is highly successful in identifying and localising damage(s), even for extremely small damage severity (e.g. 1% reduction in the second moment of area of an element with 0.5m in length in the simply supported bridge, and 15% reduction in the second moment of area of an element with 0.166m in length in the continuous bridge), and multiple damaged cases on the bridges, achieving accuracies of over 93% for all tested cases of the simply supported bridge and over 91% for the majority of the tested cases of the continuous bridge. The proposed damage localisation approach can accurately determine damage location(s) for both single and multiple damage cases. It is shown how the proposed framework can be adopted for early damage detection to enable reliable decision-making and maximise structural safety.