This article attempts to analyse and predict vertical displacements of measurement-and- control networkpoints located on civil structures founded on expansive soils, using artificial neural networks. Geodetic monitoring ofcivil structures consists in regular measurements of control point networks and interpretation of results. The obtainedvalues of displacement provide sets of significant data which enable determination of the influence of changes in groundwaterconditions of the subsoil on the deformation processes occurring in structures founded on it. Using such data sets,it is possible to draw conclusions regarding the dynamics of the occurrence of deformation and to develop a geometricmodel of displacements.In recent years, methods of prediction based on artificial intelligence have been increasingly prominent. Neural networks andevolutionary algorithms, which can supplement each other, make advanced tools applied in the process of prediction of deformations.In order to forecast displacements of control points, demonstrating changes in a civil structure, multi-layer artificialneural networks are employed in this article, taught using the method of error backpropagation and gradient optimization methods.The analysed results in the form of height differences were obtained through a series of measurements on a civil structure,taken by means of precise levelling at monthly intervals.

DOI: https://doi.org/10.3846/enviro.2017.220