A DBSCAN-based automated operational modal analysis algorithm for bridge monitoring

Advanced data analysis techniques are of paramount importance for the Structural Health Monitoring (SHM) of civil buildings and infrastructures. In particular, Automated Operational Modal Analysis (AOMA) algorithms are necessary for the output-only monitoring of such massive and large structures. The unsupervised estimation of their modal parameters from ambient vibrations enables assessing their integrity efficiently and continuously. This is particularly important for reinforced concrete (RC) bridges, which need constant maintenance. In this context, the classic cluster-based, multi-stage approach is effective in cleaning the stabilisation diagram and discerning stable and unstable modes. However, due to the shortcomings of binary classification with (k = 2)means clustering, the labelling between 'possibly physical' and 'certainly spurious' modes may not be completely reliable. The procedure described here applies Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to bypass this limitation. This allows, among other advantages, to automatically detect and remove outliers, differently from the traditional techniques. The algorithm is fully automated, including the data-driven setting of DBSCAN parameters. Its viability is tested here on a real, full-scale case study, the Z24 road bridge dataset.

Automated summary

Advanced data analysis techniques, such as Automated Operational Modal Analysis (AOMA) algorithms, are crucial for the Structural Health Monitoring (SHM) of civil buildings and infrastructures. AOMA enables the unsupervised estimation of modal parameters from ambient vibrations, allowing for efficient and continuous assessment of the integrity of massive structures like reinforced concrete (RC) bridges. However, the reliability of the classification between 'possibly physical' and 'certainly spurious' modes using binary clustering may be limited. This study applies Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to overcome this limitation and provides automated outlier detection and removal.