Analysis of Dam Natural Frequencies Using a Convolutional Neural Network

The accurate estimation of dam natural frequencies and their evolution over time can be very important for dynamic behaviour analysis and structural health monitoring. However, automatic modal parameter estimation from ambient vibration measurements on dams can be challenging, e.g., due to the influence of reservoir level variations, operational effects, or dynamic interaction with appurtenant structures. This paper proposes a novel methodology for improving the automatic identification of natural frequencies of dams using a supervised Convolutional Neural Network (CNN) trained on real preprocessed sensor monitoring data in the form of spectrograms. Our tailored CNN architecture, specifically designed for this task, represents the first of its kind. The case study is the 132 m high Cabril arch dam, in operation since 1954 in Portugal; the dam was instrumented in 2008 with a continuous dynamic monitoring system. Modal analysis has been performed using an automatic modal identification program, based on the Frequency Domain Decomposition (FDD) method. The evolution of the experimental natural frequencies of Cabril dam over time are compared with the frequencies predicted using the parameterized CNN based on different sets of data. The results show the potential of the proposed neural network to complement the implemented modal identification methods and improve automatic frequency identification over time.