  Tensioned cable nets can be used as supporting structures for the efficient
construction of lightweight building elements, such as thin concrete shell
structures. To guarantee important mechanical properties of the latter, the
tolerances on deviations of the tensioned cable net geometry from the desired
target form are very tight. Therefore, the form needs to be readjusted on the
construction site. In order to employ model-based optimization techniques, the
precise identification of important uncertain model parameters of the cable net
system is required. This paper proposes the use of Gaussian process regression
to learn the function that maps the cable net geometry to the uncertain
parameters. In contrast to previously proposed methods, this approach requires
only a single form measurement for the identification of the cable net model
parameters. This is beneficial since measurements of the cable net form on the
construction site are very expensive. For the training of the Gaussian
processes, simulated data is efficiently computed via convex programming. The
effectiveness of the proposed method and the impact of the precise
identification of the parameters on the form of the cable net are demonstrated
in numerical experiments on a quarter-scale prototype of a roof structure.
