A smart decision tool for the prediction of tunnel crown displacements

Artificial intelligence (AI) provides rigorous solutions to many engineering problems. In geotechnical engineering, AI tools are used mostly in deformation prediction and back analysis. In this paper, deformation monitoring data from measured absolute displacements in a tunnel in Greece, excavated at a complex geological system with fractured and loose cataclastic gouge, are used to train an artificial neural network (ANN) for the prediction of crown displacements along the tunnel. A Multi-layered perceptron neural network has been developed to be used as a quick smart tool for deformation behavior prediction (crown displacements) of the tunnel using the monitoring data measurements as target data and input training data from deformation parameters. The same deformation parameters are applied in finite element models (FEM) that simulate generalized sections of the tunnel from which a set of deformation results is obtained. A detailed description of the developed ANN is given and results are shown. Finally, the deformation results from the ANN tool, the FEM models, and the actual field measurements are provided and the potential of the proposed ANN method as a quick tool for tunnel deformation prediction is discussed.