In this paper, artificial neural networks (ANNs) are used to develop an efficient method for rapid and approximate force response analyses of a bridge population. The single-span reinforced concrete T-beam bridge population in Pennsylvania State is taken as a particular case study. First, a statistical analysis is conducted to examine implicit and explicit dependencies between various geometrical and structural parameters of the bridges, and the governing bridge parameters are identified along with their ranges of variation within the population. Then, a set of sample bridges are randomly generated using different combinations of the governing parameters within their predefined ranges of variation. An exact finite element analysis is implemented for each sample bridge, and the maximum moment and shear responses in beams are obtained at critical locations under various combinations of standard truck loads. An ANN is implemented to learn the relationship between the bridge parameters (inputs) and responses (outputs) based on the sample set and to make predictions for other bridges that are not present in the set. The performances of a variety of different ANN architectures are tested, and their prediction capabilities are measured and compared.