A machine learning framework for seismic risk assessment of industrial equipment

The paper aims to propose a novel machine learning framework for seismic risk assessment of industrial facilities. In this respect, a compound artificial neural network model is employed, which is based on two different artificial neural network models in series. The first artificial neural network is a regression model employed to generate samples of a vector-valued intensity measure. The second one is a classification model that is used to predict structural damage, starting from the outcomes of the first artificial neural network model. The datasets used for training and validation of the two artificial neural networks are based on hazard-consistent accelerograms and numerical analyses that are performed with an efficient finite element model of the structure. The methodology entails a preliminary feature selection phase for the identification of the aforementioned vector-valued of intensity measures that better classifies the damage/no-damage condition of the structure. This phase is implemented through the principal component analysis method. Subsequently, the Metropolis–Hastings algorithm is used to generate samples of a selected intensity measure, feeding the first ANN model. In turn, the chosen features are used as input parameters of the second ANN model to generate samples of damage/no-damage events. Using the two ANN in series, the mean annual frequency of exceeding a specific limit state is derived. The proposed framework is validated using a typical multi-storey steel frame, focusing on the seismic risk assessment of a vertical storage tank located at the first floor of the primary structure. The proposed method exhibits some clear advantages of combining numerical models with ANN techniques, mainly related to: a reduced computational time; the avoidance of any prior information on the probabilistic model of fragility curves; and the use of model-driven data.