Onyelowe Kennedy C, Yaulema Castañeda Jorge Luis, Adam Ali F Hussain, Ñacato Estrella Diego Ramiro, Ganasen Nakkeeran
Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria.
Department of Civil Engineering, School of Engineering Technology, University of the Peloponnese, Patras, Greece.
Sci Rep. 2024 Feb 19;14(1):4065. doi: 10.1038/s41598-024-54845-9.
The stiffness (K) and slenderness factor (λ) of a steel plate-based damper has been studied on the basis of elastic-inelastic-plastic buckling (EIP) modes and flexural/shear/flexural-shear failure mechanisms (FSF-S), which has been designed for the improvement of the behavior of concentrically braced frames. Steel plate-based dampers offer significant benefits in terms of mode shapes and failure mechanisms, contributing to improved dynamic performance, enhanced structural resilience, and increased safety of civil engineering structures. Their effectiveness in mitigating dynamic loads makes them a valuable tool for engineers designing structures to withstand extreme environmental conditions and seismic events. This study was undertaken by using the learning abilities of the response surface methodology (RSM), artificial neural network (ANN) and the evolutionary polynomial regression (EPR). Steel plate dampers are special structural designs used to withstand the effect of special loading conditions especially seismic effects. Its design based on the prediction of its stiffness (K) and slenderness factor (λ) cannot be overlooked in the present-day artificial intelligence technology. In this research work, thirty-three entries based on the steel plate damper geometrical properties were recorded and deployed for the intelligent forecast of the fundamental properties (λ and K). Design ratios of the steel plate damper properties were considered and models behavior was recorded. From the outcome of the model, it can be observed that even though the EPR and ANN in that order outclassed the other techniques, the RSM produced model minimization and maximization features of the desirability levels, color factor scales and 3D surface observation, which shows the real model behaviors. Overall, the EPR with R of 0.999 and 1.000 for the λ and K, respectively showed to be the decisive model but the RSM has features that can be beneficial to the structural design of the studied steel plate damper for a more robust and sustainable construction. With these performances recorded in this exercise, the techniques have shown their potential to be applied in the prediction of steel damper stiffness with optimized characteristic features to withstand structural stresses.
基于弹性 - 弹塑性屈曲（EIP）模式以及弯曲/剪切/弯剪破坏机制（FSF - S），对一种用于改善同心支撑框架性能的钢板阻尼器的刚度（K）和长细比（λ）进行了研究。钢板阻尼器在振型和破坏机制方面具有显著优势，有助于改善土木工程结构的动力性能、增强结构恢复力并提高安全性。它们在减轻动荷载方面的有效性使其成为工程师设计能承受极端环境条件和地震事件结构的宝贵工具。本研究采用了响应面法（RSM）、人工神经网络（ANN）和进化多项式回归（EPR）的学习能力。钢板阻尼器是用于承受特殊荷载条件（尤其是地震作用）影响的特殊结构设计。在当今人工智能技术下，基于其刚度（K）和长细比（λ）预测的设计不可忽视。在本研究工作中，记录了基于钢板阻尼器几何特性的33个数据，并用于对基本特性（λ和K）的智能预测。考虑了钢板阻尼器特性的设计比例并记录了模型行为。从模型结果可以看出，尽管EPR和ANN依次优于其他技术，但RSM产生了合意水平、颜色因子尺度和三维表面观测的模型最小化和最大化特征，这显示了实际模型行为。总体而言，EPR对λ和K的R值分别为0.999和1.000，显示为决定性模型，但RSM具有的特征对所研究的钢板阻尼器的结构设计有益，可实现更坚固和可持续的建筑。通过本次实验记录的这些性能，这些技术已显示出其在预测具有优化特征以承受结构应力的钢阻尼器刚度方面的应用潜力。
