Tuned mass damper (TMD) is a type of energy absorbers that can mitigate the vibrations of the main system if its frequency and damping ratios are well adjusted. By adopting simple assumptions on the structure and loadings, many analytical and empirical relationships have been presented for the estimation of the parameters for TMDs. In this research, methods based on the artificial intelligence (AI) techniques are proposed for optimal tuning of the TMD parameters of the main damped-structure for three kinds of loadings: white-noise base acceleration, external white-noise force, and harmonic base acceleration. For this purpose, a dataset using the cuckoo search (CS) optimization algorithm is created. The performance of the proposed methods based on the radial basis function (RBF) neural network, feed-forward neural network (FFNN), adaptive neuro-fuzzy inference system (ANFIS), and random forest (RF) techniques are evaluated by some statistical indicators. The results show the proper performance of these methods for the optimal estimation of the TMD parameters. Overall, the ANFIS method results in best matching with the observed dataset. Moreover, the simulation results indicate that the TMD’s optimal frequency ratio is reduced, while its optimal damping ratio is increased, against the increase in the TMD mass ratio of the main structure subjected to harmonic base acceleration. This trend with a less slope is observed for the optimal frequency ratio of the TMD in the main structure subjected to external white-noise force; however, the optimal damping ratio of the TMD is independent of its mass ratio in this case. Similar results are obtained for the main structure subjected to white-noise base acceleration.

中文翻译：

---

不同激励下阻尼结构中调谐质量阻尼器优化设计的人工智能预测模型

调谐质量阻尼器 (TMD) 是一种能量吸收器，如果调整好其频率和阻尼比，可以减轻主系统的振动。通过对结构和载荷采用简单的假设，已经提出了许多分析和经验关系来估计 TMD 的参数。在这项研究中，提出了基于人工智能 (AI) 技术的方法，用于优化调整主要阻尼结构的 TMD 参数，用于三种载荷：白噪声基加速度、外部白噪声力和谐波基加速度。为此，创建了一个使用杜鹃搜索 (CS) 优化算法的数据集。基于径向基函数 (RBF) 神经网络、前馈神经网络 (FFNN)、自适应神经模糊推理系统 (ANFIS) 和随机森林 (RF) 技术通过一些统计指标进行评估。结果表明，这些方法对于 TMD 参数的最佳估计具有适当的性能。总体而言，ANFIS 方法与观察到的数据集最匹配。此外，仿真结果表明，TMD的最佳频率比降低，而其最佳阻尼比增加，而主结构在谐波基础加速度下的TMD质量比增加。这种斜率较小的趋势是在外白噪声作用下主体结构中TMD的最佳频率比被观察到的；然而，在这种情况下，TMD 的最佳阻尼比与其质量比无关。