Low-Complexity Approach to Intelligent SHM by Combining Machine Learning Models Using Single-Sensor Data

Abstract

Structural Health Monitoring (SHM) systems, when applied to large civil engineering structures such as bridges, process high-volume data and run computationally intensive algorithms, which typically require important processing power to ensure low inference latency to enable real-time monitoring and rapid decision-making. In this study, we propose a novel resource-efficient approach to optimizing SHM for civil structures. The methodology integrates lightweight machine learning models that rely exclusively on single-sensor data, enabling deployment at the sensor level (smart sensors). By aggregating outputs from multiple sensors, the approach captures spatial information, introduces diversity, and benefits from an averaging effect, significantly improving overall performance compared to individual sensor-based predictions. This single-sensor strategy ensures low computational complexity while maintaining high accuracy, making it particularly suitable for resource-constrained environments. To evaluate the effectiveness of the proposed methodology, we applied it to the Z24 benchmark dataset, a widely recognized SHM resource for civil structures. The objective was to classify various damage scenarios based on data collected from accelerometers deployed on the bridge. The results demonstrate competitive performance with minimal computational complexity, highlighting the scalability and suitability of such an approach for large-scale SHM applications. Ultimately, this study underscores the potential of resource-efficient SHM solutions, contributing to developing sustainable and intelligent monitoring systems.