Structural health assessment of existing dams based on non-destructive testing, physics-based models and machine learning tools

Abstract

The safe operation of dams is ensured by monitoring systems that collect periodic information on environmental conditions (for example, temperature and water level) and on the structural response to external actions. In newly built or retrofitted facilities, large networks of sensors can take daily measurements that are automatically transferred to servers. In other cases, additional information can be acquired, occasionally or systematically, through emerging drone-based non-contact full-field techniques.

The measurements are processed by various analytical and machine learning tools trained on historical data sets, capable of highlighting any anomalous recordings. Monitoring data can also support the accurate calibration of a physics-based model of the structure, usually built in the finite element framework. The analyses carried out by the digital twin allow the experimental database to be expanded with the displacements evaluated in the event of extreme environmental conditions, damage or collapse mechanisms never occurred before.

This contribution illustrates an integrated approach to the safety assessment of existing dams that combines experimental, computational and data processing methodologies. Attention is particularly focused on model calibration procedures and on the uncertainties that influence the characteristics of the joints. The presented results of the validation studies performed by the Authors on benchmark and real-scale problems highlight the merits and limitations of alternative approaches to data exploitation and remote measurement.