The safety assessment of dams is a complex task that is made possible thanks to a constant monitoring of pertinent parameters. Once collected, the data is processed by statistical analysis models in order to describe the behaviour of the structure. The aim of those models is to detect early signs of abnormal behaviour so as to take corrective actions when required. Because of the uniqueness of each structure, the behavioural models need to adapt to each of these structures, thus flexibility is required. Simultaneously, generalisation capacities are sought, so a trade-off has to be found. This flexibility is even more important when the analysed phenomenon is characterised by non-linear features, as it is the case for the piezometric levels (PL) monitored at the rockconcrete interface of the arch dam that this study focuses on. In that case, the linear
models that are classically used by engineers show insufficient performances. Consequently, interest naturally grows for the advanced learning algorithms known as machine learning techniques. In this work, the aim is to compare the predictive performances and generalization capacities of three different Data Mining algorithms that are likely to be used for monitoring purposes: Artificial Neural Networks (ANN), Support Vector Machines (SVM) and Multiple Regression (MR). The achieved results show that SVM and ANN stand out as the most efficient algorithms, when it comes to analysing non-linear monitored phenomenon. Through a global sensitivity analysis, the influence of the models’ attributes was measured, evidencing a high impact of Z (relative trough) in PL prediction.info:eu-repo/semantics/publishedVersio
