基于钢板的阻尼器的预测,以改善基于可持续结构的RSM和ML方法的同心支框架的行为
Kennedy C Onyelowe1,2, Jorge Luis Yaulema Castañeda3, Ali F Hussain Adam4
1Department of Civil Engineering, Michael Okpara University of Agriculture, Umudike, Nigeria. konyelowe@mouau.edu.ng.
Scientific reports
|February 19, 2024
概括
这项研究使用人工智能预测了钢板阻尼器的刚度 (K) 和细度因子 (λ). 进化多项式回归 (EPR) 证明最有效,为结构设计和地震弹性提供了强大的预测.
科学领域:
- 结构工程 结构工程
- 材料科学 材料科学 材料科学
- 计算智能是一种计算智能.
背景情况:
- 基于钢板的阻尼器增强了同心支框架的行为.
- 这些阻尼器对于提高土木工程中的动态性能,结构弹性和安全性至关重要.
- 它们在减轻地震和极端环境负荷方面的有效性已得到证实.
研究的目的:
- 为了研究基于钢板的阻尼器的刚度 (K) 和细度系数 (λ).
- 利用人工智能技术来预测这些基本性质.
- 评估各种AI模型在预测阻尼器特性方面的性能.
主要方法:
- 采用响应表面方法 (RSM),人工神经网络 (ANN) 和进化多项式回归 (EPR).
- 根据钢板阻尼器的几何特性,利用了33个参数来进行模型训练和验证.
- 分析了弹性-不弹性-塑料曲 (EIP) 模式和弹性/剪切/弹性-剪切故障机制 (FSF-S).
主要成果:
- 进化多项式回归 (EPR) 实现了 λ 的 R2 值为 0.999 和 K 的 R2 值为 1.000,证明了卓越的预测准确性.
- 人工神经网络 (ANN) 也表现出高性能,超过了其他技术.
- 响应表面方法 (RSM) 为设计优化和理解模型行为提供了有价值的可视化工具.
结论:
- EPR被认为是预测钢板阻尼器刚度和细度因子的决定性模型.
- RSM为优化钢板阻尼器的结构设计提供了有利的功能,以提高强度和可持续性.
- 研究的人工智能技术显示出预测钢阻尼器属性的巨大潜力,并优化了抗应力特性.
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