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The development of fragility curves is an important step in seismic risk assessment within the scope of performance‐based earthquake engineering. The goal of this work is to use machine learning methods (regression‐based tools) to forecast the large‐span precast parking structural responses and fragility curves. This study proposes five predicting models based on machine learning to evaluate the seismic performance of the large‐span precast parking structures, including: neural networks, genetic algorithm‐based neural networks, support vector machine, decision tree and random forest. A database that includes 453 numerical synthetic results was used to train and test the machine learning models. The seismic performance of large‐span precast parking structures were predicted using the constructed machine learning models. Finally, the sensitivity analysis of input parameters was conducted. From this paper we can conclude that: (1) the genetic optimization‐based neural networks' predicting model has the most accurate predictive ability for seismic fragility estimation and (2) the structural responses and the fragility curves of parking structures are related to the differences of the stiffness of the connectors and the number of floors, of which the stiffness of the connectors should be given special attention. [ABSTRACT FROM AUTHOR]

The reliable design of multiphase micro‐structured apparatus requires a precise knowledge of the internal flow regime. Previous research indicated that classifiers based on artificial neural networks (ANN) are relatively simple to develop and provide a reasonable accuracy when trained with data for specific inlet designs. This paper introduces advanced ANN classifiers capable of predicting all relevant flow regimes regardless of the inlet design with a recall of 94 % and above for Taylor, churn, dispersed, rivulet, and parallel flows, between 89 % and 94 % for annular and bubbly flows, and 83 % for Taylor‐annular flow. These classifiers were trained and validated by using more than 13,000 experimental data points extracted from 97 flow maps. [ABSTRACT FROM AUTHOR]