Liang Jiaming, Li Zhanchao, Pan Litan, Khailah Ebrahim Yahya, Sun Linsong, Lu Weigang
College of Water Resources Science and Engineering, Yangzhou University, Yangzhou, 225009, Jiangsu, China.
Intelligent Water Conservancy Research Institute, Nanjing Jurise Engineering Technology Co., Ltd, Nanjing, 210032, Jiangsu, China.
Sci Rep. 2023 Jul 24;13(1):11955. doi: 10.1038/s41598-023-38590-z.
Dam numerical simulation is an important method to research the dam structural behavior, but it often takes a lot of time for calculation when facing problems that require many simulations, such as structural parameter back analysis. The surrogate model is widely used as a technology to reduce computational cost. Although various methods have been widely investigated, there are still problems in designing the surrogate model's optimal Design of Experiments (DoE). In addition, most of the current DoE focuses on establishing a single-output problem. Designing a reasonable DoE for high-dimensional outputs is also a problem that needs to be solved. Based on the above issues, this research proposes a sequential surrogate model based on the radial basis function model (RBFM) with multi-outputs adaptive sampling. The benchmark function demonstrates the applicability of the proposed method to single-input & multi-outputs and multi-inputs & multi-outputs problems. Then, this method is applied to establishing a surrogate model for dam numerical simulation with multi-outputs. The result demonstrates that the proposed technique can be sampled adaptively and samples can be targeted based on the function form of the surrogate model, which significantly reduces the required sampling and calculation cost.
大坝数值模拟是研究大坝结构行为的重要方法，但在面对需要多次模拟的问题（如结构参数反分析）时，计算往往需要耗费大量时间。代理模型作为一种降低计算成本的技术被广泛应用。尽管已经对各种方法进行了广泛研究，但在设计代理模型的最优实验设计（DoE）方面仍存在问题。此外，当前大多数DoE都集中在建立单输出问题上。为高维输出设计合理的DoE也是一个需要解决的问题。基于上述问题，本研究提出了一种基于径向基函数模型（RBFM）的具有多输出自适应采样的序贯代理模型。基准函数证明了所提方法对单输入多输出和多输入多输出问题的适用性。然后，将该方法应用于建立具有多输出的大坝数值模拟代理模型。结果表明，所提技术能够进行自适应采样，并且可以根据代理模型的函数形式进行有针对性的采样，这显著降低了所需的采样和计算成本。
