Characterization of Relative Movements between Blocks Observed in a Concrete Dam and Definition of Thresholds for Novelty Identification Based on Machine Learning Models

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, data-driven 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.