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向使用可解释的数据驱动替代模型来处理基于性能的地震设计作为反向工程问题的方向
1Department of Civil and Environmental Engineering, Utah State University, Logan, UT, USA.
概括
本研究将地震设计视为一个反向问题,使用机器学习直接找到最佳结构参数. 这种方法有效地将建筑物的地震风险和维修成本降到最低.
科学领域:
- 工程 工程师 工程师 工程师
- 计算科学 计算科学
- 地震工程的工程是地震工程.
背景情况:
- 基于性能的地震设计 (PBSD) 传统上面临着计算效率低下的问题.
- 直接导出设计参数以满足特定的性能目标 (PO) 是一个挑战.
研究的目的:
- 开发一种使用逆向工程原理进行地震设计的新方法.
- 整合可解释的机器学习 (ML) 模型,以实现高效的性能映射.
- 优化结构参数以最大限度地减少地震损失.
主要方法:
- 形成地震设计作为一个反向问题.
- 实现可解释的ML模型,将设计变量映射到性能指标.
- 在遗传优化算法中将ML模型集成为评估函数.
- 将该方法应用于洛杉矶和查尔斯顿的钢铁和混凝土时刻框架.
主要成果:
- 在各种建筑类型和地震风险中使用替代ML模型实现了高精度 (R2>90%).
- 优化算法成功地确定了最佳成员属性,以最大限度地减少年度地震损失.
- 结果与已确定的工程原理相一致.
结论:
- 提出的反向工程方法与ML提供了一个有效的替代传统PBSD.
- 这种方法允许直接导出用于减少地震风险的设计参数.
- 该研究证明了开发的技术的实际应用和有效性.
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