Full long-term extreme buffeting response calculations using sequential Gaussian process surrogate modeling

The full long-term method (FLM) provides the most accurate estimates of the long-term (i.e., 10-100 years) extreme buffeting responses in the design of wind-sensitive structures. However, it suffers from high computa-tional demand if the number of uncertain turbulence parameters becomes high. In this paper, we propose a new algorithm for efficient solution of the full long-term problem through sequential Gaussian Process (GP) surrogate modeling. The algorithm efficiently trains a GP surrogate model that replaces the buffeting response calculation while exploiting the specific properties of the long-term problem. The performance of the algorithm is demon-strated on a practical design problem of estimating extreme buffeting-induced stresses of a long-span suspension bridge, where the results are compared with the ones obtained from other viable methods. The results show that the algorithm achieves computational efficiency comparable to the highly efficient IFORM, while also signifi-cantly enhancing the accuracy of the response estimate. The algorithm also has the added benefit of converging to the true solution of the full long-term problem, in contrast to the IFORM solution, which converges to an approximate solution.

Automated summary

This paper proposes a new algorithm for efficiently solving the full long-term problem through sequential Gaussian Process (GP) surrogate modeling. The algorithm trains a GP surrogate model to replace the buffeting response calculation in wind-sensitive structures, improving computational efficiency and accuracy of the response estimate. The algorithm is demonstrated on a practical design problem and compared with other methods, showing comparable efficiency and enhanced accuracy.