本文已被:浏览 99次 下载 55次
投稿时间:2024-05-13 修订日期:2024-06-19
投稿时间:2024-05-13 修订日期:2024-06-19
中文摘要: 充填采矿法在提高矿石回收率,防止地表沉陷及实现绿色矿山建设方面展现出显著优势。充填体的强度特性是影响充填效果关键因素,通过实验评估充填体的抗压强度既耗费时间又花费颇高,而基于深度学习的强度预测模型能够有效解决该问题。本文基于已有试验结果,构建了包含2338条样本数据的数据库。采用均方根误差、平均绝对误差、决定系数为评价指标,对BP、PSO-BP、SVM和GA-SVM四种预测模型进行了对比评估,结果表明GA-SVM模型对充填体强度的预测效果最为理想,可作为充填体强度预测的有效工具。利用随机森林算法进行对影响充填体强度的13个因素进行重要性评估,结果显示养护龄期为最重要的影响因素。这一研究为矿山充填体抗压强度预测提供了有效参考。
Abstract:The filling mining method demonstrates significant advantages in enhancing ore recovery, preventing surface subsidence, and achieving environmentally friendly mine construction. The strength characteristic of backfill plays a crucial role in determining the effectiveness of the filling process. Evaluating the compressive strength of backfill through experiments is both time-consuming and costly; however, a deep learning-based strength prediction model can effectively address this issue. This study establishes a database comprising 2338 sample data based on experimental results. Four prediction models (BP, PSO-BP, SVM, and GA-SVM) are compared and evaluated using root-mean-square error, average absolute error, and determination coefficient as evaluation criteria. The findings indicate that the GA-SVM model yields the most accurate predictions for backfill strength and can serve as an effective tool for such predictions. Furthermore, the random forest algorithm is employed to assess the importance of 13 factors influencing backfill strength; notably, curing age emerges as the most critical factor. This research offers valuable insights into predicting the compressive strength of mine backfill.
文章编号:20240513002 中图分类号: 文献标志码:
基金项目:国家重点研发计划项目资助(2022YFE0129200)
作者 | 单位 | |
吕奉斌 | 北京工业大学 | l1095635717@163.com |
张志红* | 北京工业大学 | zhangzh2002@126.com |
郭利杰 | 矿冶科技集团有限公司 | guolijie@bgrimm.com |
刘光生 | 矿冶科技集团有限公司 | liuguangsheng@bgrimm.com |
引用文本:
吕奉斌,张志红,郭利杰,刘光生.充填体数据库建立及基于GA-SVM的强度预测模型[J].矿冶,2025,34(1):.
吕奉斌,张志红,郭利杰,刘光生.充填体数据库建立及基于GA-SVM的强度预测模型[J].矿冶,2025,34(1):.