基于深度学习的建筑和拆除废弃物粉碎设备的最佳选择
Mingyuan Zhang1, Xiaoli Liu1, Lingjie Kong2
1Department of Construction Management, Dalian University of Technology, Dalian, 116024, China.
Journal of environmental management
|October 1, 2025
概括
本研究引入了一种深度学习方法,以优化建筑和拆除废弃物 (CDW) 粉碎设备的选择. 该方法准确评估废物特性,降低成本和可持续回收利用对环境的影响.
科学领域:
- 环境工程 环境工程
- 废物管理中的人工智能
- 可持续的建筑材料 可持续的建筑材料
背景情况:
- 对于建筑和拆除废弃物 (CDW) 的传统手工压碎设备选择是低效和昂贵的.
- 粉碎设备对回收的聚合物质量,回收成本和环境排放产生重大影响.
- 优化设备选择对于高效的CDW回收和可持续建筑至关重要.
研究的目的:
- 使用深度学习开发CDW粉碎设备的最佳选择方法.
- 准确评估CDW大小和体积,以便明智地选择设备.
- 尽量减少环境成本,提高CDW回收过程的效率.
主要方法:
- 实现了Mask R-CNN,用于基于深度学习的CDW识别和细分.
- 利用粗暴力算法和3D体积重建来计算尺寸和质量分布.
- 综合生命周期评估 (LCA) 和支付意愿 (WTP) 进行环境成本分析.
主要成果:
- 与其他细分模型相比,Mask R-CNN模型显示出更高的准确性.
- 测量尺寸和质量分布的总体误差在5%以内.
- 拟议的方法有效地通过平衡运营和环境成本来确定最佳的粉碎设备.
结论:
- 基于深度学习的方法为CDW回收项目提供了准确的辅助决策支持.
- 精选的粉碎设备有效控制环境排放,促进低碳CDW回收利用.
- 这种方法通过有效的废物管理来支持建筑行业的可持续发展.
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