Abstract:
Dam surveillance activities are based on observing the structural behaviour and interpreting
the past behaviour supported by the knowledge of the main loads. For day-to-day activities, datadriven
models are usually adopted. Most applications consider regression models for the analysis of
horizontal displacements recorded in pendulums. Traditional regression models are not commonly
applied to the analysis of relative movements between blocks due to the non-linearities related to the
simultaneity of hydrostatic and thermal effects. A new application of a multilayer perceptron neural
network model is proposed to interpret the relative movements between blocks measured hourly in a
concrete dam under exploitation. A new methodology is proposed for threshold definition related to
novelty identification, taking into account the evolution of the records over time and the simultaneity
of the structural responses measured in the dam under study. The results obtained through the
case study showed the ability of the methodology presented in this work to characterize the relative
movement between blocks and for the identification of novelties in the dam behaviour.