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集体学习为重建金属结构的可持续方法预测预测
Tetiana Vlasenko1, Taras Hutsol2,3, Vitaliy Vlasovets4
1Department of Management, Business and Administration, State Biotechnology University, Alchevsky St., 44, Kharkiv, 61002, Ukraine.
Scientific reports
|January 8, 2025
概括
机器学习使用非破坏性测试预测建筑钢的可重复使用性. 这支持了欧洲的绿色协议,使得在建筑物使用寿命结束时可以做出明智的决定,重新使用钢材.
科学领域:
- 材料科学 材料科学 材料科学
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
背景情况:
- 欧洲绿色协议强调产品的重复使用和耐用性,特别是建筑钢材.
- 生命周期评估 (LCA) 对环境影响至关重要,但在预测寿命末钢材性能方面存在差距.
- 在生命周期结束阶段 (C1-C4,D) 预测钢材的可重复使用性是一项挑战.
研究的目的:
- 开发一种机器学习模型,用于预测建筑钢材的可重复使用性.
- 通过非破坏性磁力方法来确定钢的率强度,以进行重复使用评估.
- 支持对再利用建筑钢材的知情决策.
主要方法:
- 利用机器学习 (ML) 方法,专注于回归问题.
- 雇员集体学习结合多个模型以提高预测准确度.
- 应用了权重组合方法,整合了8个模型来预测拉伸强度.
主要成果:
- 权重组合方法实现了最高的预测准确性 (MSE = 441 MPa,RMSE = 21 MPa).
- 该模型在预测拉伸强度方面表现出高准确度和低结论延迟 (IL = 0.119 秒).
- 非破坏性磁性测试数据被有效地用于属性预测.
结论:
- 机器学习,特别是组合方法,显著提高了建筑钢材可重复使用性的预测.
- 基于这种ML模型的自动化工具可以帮助建筑专业人员做出对钢材再利用的明智决策.
- 这种方法在建筑物使用寿命结束阶段 (D阶段) 管理钢材再利用流程方面取得了重大进展.
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