Parameter Identification for Digital Fabrication: A Gaussian Process Learning Approach
This work provides a more efficient and cost-effective method for identifying crucial model parameters for civil engineers and construction workers dealing with tensioned cable net structures, which is an incremental improvement over existing methods.
This paper addresses the challenge of identifying uncertain model parameters for tensioned cable nets used in lightweight building construction. By employing Gaussian process regression, the proposed method can map cable net geometry to these parameters using only a single form measurement, which is a significant cost reduction compared to previous methods. Numerical experiments on a quarter-scale roof structure prototype demonstrate the method's effectiveness and the impact of precise parameter identification on the cable net's form.
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.