Two-phase liquid–gas flows are common in industries such as mining, energy, chemicals, and oil. The gamma-ray absorption technique is a non-contact method widely used to measure parameters for such flows. By analyzing signals from scintillation detectors, flow parameters can be determined and flow structures identified. This study evaluated four types of water–air flow regimes using selected computational intelligence methods. The experiments involved a water–air flow in a horizontal pipe with a 30 mm internal diameter, using two sealed Am-241 gamma ray sources and two scintillation probes type NaI(Tl). Eight features for fluid flow were extracted from the power spectral density and the cross-spectral density of the obtained measurement signals and then used as input for the classifier. Six computational intelligence methods, including k-means, a single decision tree, a support vector machine, a probabilistic neural network, a multilayer perceptron, and a radial basis function, were applied to identify the flow regime. The results showed that all of the methods provided good results of classification for the analyzed types of water–air flow.
