Integrating Ambient Vibration Monitoring and Machine Learning for Condition Assessment of Heritage Masonry Bridges: A Venetian Case Study

Hamid Imani moghaddam, S. Russo, Raimondo Betti

Preserving the structural integrity of heritage masonry arch bridges presents unique challenges, particularly within historically dense environments like Venice where non-invasive methods are paramount. Ambient vibration monitoring (AVM) offers a well-established starting point, allowing us to capture the dynamic behavior of these structures under their normal operating conditions. Our ongoing work involves a large-scale AVM campaign across Venice, using synchronized velocimeters to systematically gather vibration data. From this, we extract key dynamic parameters like dominant frequencies, mode shapes and damping ratios, alongside essential geometric features. Simultaneously, careful visual inspections provide the critical qualitative context regarding each bridge's state of conservation. By integrating these distinct quantitative and qualitative data streams, we have been developing a unique baseline archive crucial for moving beyond characterization towards meaningful interpretation. The central aim of this paper is to explore how this integrated dataset can be leveraged to address the significant challenge of automated condition assessment. We employ a supervised machine learning framework to bridge the gap between measured data and assessed structural condition. The curated archive, which links the dynamic and geometric measurements (our inputs) to discrete condition categories derived from the visual inspections (our outputs), provides the training ground for established classification algorithms – specifically Support Vector Machines (SVM) and Random Forest (RF). Standard pre-processing techniques, including feature scaling, are incorporated to ensure the classifiers can effectively learn from the data without scale-induced biases. Initial explorations with this framework have already yielded valuable insights. For instance, feature importance analysis of the Random Forest model, strongly suggests that the dynamic parameters captured via AVM—particularly damping ratio and dominant frequency—offer considerably more diagnostic information about condition than the simpler geometric descriptors alone. This finding reinforces the value embedded in vibration monitoring. Moreover, our work confirms the feasibility of training these models to distinguish between different condition states using only the combined instrumental and geometric data. Importantly, from an engineering perspective, we recognize that raw classification accuracy is insufficient; ensuring high sensitivity (Recall) in identifying potentially compromised structures is critical for responsible heritage management and risk mitigation. This combined AVM-ML methodology provides a scalable foundation for developing more objective, data-supported tools to assist in condition assessment and guide proactive maintenance efforts for vital cultural infrastructure. Further research will necessarily involve expanding the dataset and undertaking rigorous validation of the models.