When it comes to monitoring of huge structures, main issues are limited time, high costs and how to deal with the big amount of data. In order to reduce and manage them, respectively, methods from the field of optimal design of experiments are useful and supportive. Having optimal experimental designs at hand before conducting any measurements is leading to a highly informative measurement concept, where the sensor positions are optimized according to minimal errors in the structures’ models. For the reduction of computational time a combined approach using Fisher Information Matrix and mean-squared error in a two-step procedure is proposed under the consideration of different error types. The error descriptions contain random/aleatoric and systematic/epistemic portions. Applying this combined approach on a finite element model using artificial acceleration time measurement data with artificially added errors leads to the optimized sensor positions. These findings are compared to results from laboratory experiments on the modeled structure, which is a tower-like structure represented by a hollow pipe as the cantilever beam. Conclusively, the combined approach is leading to a sound experimental design that leads to a good estimate of the structure’s behavior and model parameters without the need of preliminary measurements for model updating.

在监视大型结构时，主要问题是时间有限，成本高以及如何处理大量数据。为了分别减少和管理它们，实验优化设计领域的方法是有用的和支持性的。在进行任何测量之前，先要有最佳的实验设计，这会导致信息量大的测量概念，即根据结构模型中的最小误差对传感器位置进行优化。为了减少计算时间，在考虑不同错误类型的情况下，提出了在两步过程中使用Fisher信息矩阵和均方误差的组合方法。错误描述包含随机/偶然和系统/流行病部分。将这种组合方法应用于使用人工加速时间测量数据并人工添加误差的有限元模型上，可以优化传感器位置。将这些发现与实验室对模型结构的实验结果进行比较，该模型结构为以空心管为悬臂梁的塔状结构。最终，组合方法导致了合理的实验设计，从而可以对结构的行为和模型参数进行良好的估算，而无需为模型更新进行初步测量。它是一种以空心管为悬臂梁的塔状结构。最终，组合方法导致了合理的实验设计，从而可以对结构的行为和模型参数进行良好的估计，而无需为模型更新进行初步测量。它是一种以空心管为悬臂梁的塔状结构。最终，组合方法导致了合理的实验设计，从而可以对结构的行为和模型参数进行良好的估计，而无需为模型更新进行初步测量。