Health diagnosis of concrete dams using hybrid FWA with RBF-based surrogate model
Health diagnosis of concrete dams using hybrid FWA with RBF-based surrogate model
复制标题
使用混合 FWA 和基于 RBF 的代理模型对混凝土坝进行健康诊断
DOI：
10.1016/j.wse.2019.09.002
复制
发表时间：
2019
影响因子：
4
通讯作者：
Fei Kang
中科院分区：
文献类型：
--
作者：
Si-qi Dou;Jun-jie Li;Fei Kang
大坝结构健康监测是保证大坝健康安全的重要手段。提出了一种基于混合烟花算法（FWA）和径向基函数（RBF）模型的混凝土坝健康状态反分析方法。利用不同库水位下的位移变化，识别材料的弹性模量，诊断混凝土坝的损伤。FWA算法是一种全局优化的智能算法。该混合算法结合了模糊加权算法和模式搜索算法，具有很强的局部寻优能力。基准函数算例和混凝土坝拟实验算例表明，混合模糊加权算法提高了原算法的收敛速度和鲁棒性。为了解决逆分析中的时间消耗问题，建立了基于径向基函数的代理模型来代替部分有限元方法。混凝土坝的算例表明，基于径向基函数的代理模型的使用显着减少了反分析的计算时间，而对识别精度的影响很小。提出的混合FWA与RBF网络相结合，可以快速、准确地确定材料的弹性模量，进而判断混凝土坝的健康状况。
Structural health monitoring is important to ensuring the health and safety of dams. An inverse analysis method based on a novel hybrid fireworks algorithm (FWA) and the radial basis function (RBF) model is proposed to diagnose the health condition of concrete dams. The damage of concrete dams is diagnosed by identifying the elastic modulus of materials using the displacement changes at different reservoir water levels. FWA is a global optimization intelligent algorithm. The proposed hybrid algorithm combines the FWA with the pattern search algorithm, which has a high capability for local optimization. Examples of benchmark functions and pseudo-experiment examples of concrete dams illustrate that the hybrid FWA improves the convergence speed and robustness of the original algorithm. To address the time consumption problem, an RBF-based surrogate model was established to replace part of the finite element method in inverse analysis. Numerical examples of concrete dams illustrate that the use of an RBF-based surrogate model significantly reduces the computation time of inverse analysis with little influence on identification accuracy. The presented hybrid FWA combined with the RBF network can quickly and accurately determine the elastic modulus of materials, and then determine the health status of the concrete dam.