在基于数字图像相关性和计算机视觉识别模型的水泥回填充样本中,应变场和裂纹预测的演变
Huanbao Zhang1, Tao Gao2, Fulin Wang1
1School of Resources Environment and Safety Engineering, University of South China, Hengyang, 421001, China.
Scientific reports
|March 29, 2025
概括
这项研究表明,水泥与尾矿的比率,填充度和固化年龄如何影响水泥糊补充 (CPB) 的稳定性. 一个新的计算机视觉模型可以预测裂的早期出现,从而提高了采矿安全性和填充设计.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 材料科学 材料科学 材料科学
背景情况:
- 矿山安全在很大程度上取决于填充材料的稳定性.
- 混凝土糊补充 (CPB) 对于地面支和矿山稳定性至关重要.
研究的目的:
- 在不同的混合设计下分析CPB的应变场演变和裂纹发展.
- 开发一种用于CPB早期裂纹预测的定量方法.
主要方法:
- 单轴压力强度 (UCS) 的测试.
- 数字图像相关性 (DIC). 数字图像相关性.
- 计算机视觉识别 (CVR) 技术,特别是HSV-CVR模型.
主要成果:
- CPB的UCS随着水泥与尾矿比率的降低,填充度和固化年龄的降低而降低.
- 主要应变场的演变随着这些参数的减少而加速.
- 该HSV-CVR模型准确量化了菌株区域,并使早期裂纹预测成为可能.
结论:
- HSV-CVR模型提供了一个客观而精确的方法来分析CPB菌株和预测裂.
- 这些发现为优化CPB设计,监测和采矿操作中的风险管理提供了科学基础.
- 这项研究有助于通过更好地理解回填物质来提高采矿安全和运营效率.
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