整合系统动态和机器学习,用于在拆除过程中对建筑材料的环境影响分析
Babatunde Oluwaseun Ajayi1, Mayowa Emmanuel Bamisaye1, Hugo Miguel Magalhães Macedo2
1King Prajadhipok's Institute, Bangkok, 10210, Thailand.
Scientific reports
|July 27, 2025
概括
建筑拆迁对气候变化产生重大影响. 一项整合系统动力学和随机森林模型的研究发现,使用拆除机器人和液压分割器 (OEIC4) 是最可持续的选择,可以减少四倍的环境影响.
科学领域:
- 环境科学 环境科学
- 土木工程 土木工程是指土木工程.
- 可持续建筑 可持续建筑
背景情况:
- 建筑业对气候变化做出了重大贡献,建筑拆迁是环境退化的主要来源.
- 拆除过程涉及复杂,相互关联的变量,需要对环境影响减轻进行全面分析.
研究的目的:
- 开发和验证一个综合框架,用于评估建筑拆除对环境的影响.
- 确定最环境可持续的拆除工具组合和影响因素.
主要方法:
- 系统动力学 (SD) 建模与随机森林 (RF) 算法的集成.
- 用于验证SD模型和评估变量关系的RF算法.
- 进行灵敏度分析,以提高模型的稳定性和预测准确性.
主要成果:
- 射频模型显示了高的预测准确性 (R平方:0.97760.9990; MSE:0.00030.0356).
- 与其他组合相比,拆除工具组合OEIC4 (拆除机器人和液压分割器) 的环境影响高达四倍.
- 发现更短的拆除时间大大减少了整体环境影响.
结论:
- 综合的SD-RF框架为评估与拆除相关的环境影响提供了一种新方法.
- 这些发现支持循环经济原则,通过减少建筑中的污染和浪费.
- 这项研究与全球气候变化缓解目标保持一致,通过促进可持续的拆除实践.
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