Abstract:
This work focuses on the dynamic monitoring behaviour of concrete dams, with a specific emphasis on the Baixo Sabor dam as a case study. The main objective of the dynamic monitoring is to continuously observe the dam's behaviour, ensuring it remains within expected patterns and issuing alerts if deviations occur. The monitoring process relies on on-site instruments and behaviour models that use pattern recognition, thereby avoiding explicit dependence on mechanical principles.
The undertaken work aimed to develop, calibrate, and compare statistical and machine learning models to aid in interpreting the observed dynamic behaviour of a concrete dam. The methodology included several key steps: operational modal analysis of acceleration time series, characterization of the temporal evolution of observed magnitudes and influential environmental and operational variables, construction and calibration of predictive models using both statistical and machine learning methods, and the comparison of their effectiveness.
Both Multiple Linear Regression (MLR) and Multilayer Perceptron Neural Network (MLP-NN) models were developed and tested. The results showed that while both models effectively captured and predicted the dam's behaviour, the neural network slightly outperformed the regression model in prediction accuracy. However, the linear regression model is easier to interpret.
In conclusion, both methods linear regression and neural network models are suitable for the analysis and interpretation of monitored dynamic behaviour. For large-scale projects like the Baixo Sabor dam, multilayer perceptron - neural networks offer significant advantages in handling intricate data relationships, thus providing better insights into the dam's dynamic behaviour.