This track focuses on innovative data-driven modeling approaches for structural health monitoring applications. Researchers are encouraged to present methodologies that enhance predictive accuracy and reliability in assessing structural integrity.

This session explores the integration of predictive maintenance techniques within engineering frameworks. Contributions should highlight the role of data analytics in optimizing maintenance schedules and reducing operational costs.

This track addresses advanced methods for feature extraction and selection from sensor data in structural health monitoring. Papers should discuss techniques that improve the efficiency and effectiveness of data interpretation.

This session invites contributions on the application of supervised and unsupervised learning algorithms for damage detection in structures. Emphasis will be placed on novel approaches that enhance detection capabilities using real-world data.

This track examines the application of deep learning methodologies to vibration analysis in structural health monitoring. Researchers are encouraged to present findings that demonstrate the effectiveness of these techniques in identifying anomalies.

This session focuses on innovative approaches to anomaly detection within structural health monitoring systems. Contributions should explore algorithms and frameworks that enhance the identification of unusual patterns in sensor data.