科技导报

基于GA-SVM模型预测减水剂对充填体强度的影响

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科技导报 | 研究论文 2015,33(11): 44-48

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科技导报 | 研究论文 2015, 33(11): 44-48

基于GA-SVM模型预测减水剂对充填体强度的影响

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张钦礼, 李浩, 刘吉祥, 刘群武, 陈秋松

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Prediction of water-reducers influence on strength of backfill body using GA-SVM model

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出版时间: 2015-06-13 doi: 10.3981/j.issn.1000-7857.2015.11.007

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改善充填料浆的工作性能、提高充填体的强度和密实性是矿山充填法的研究方向之一。充填配比实验表明, 在充填材料的制备过程中加入适量的高效减水剂可以提高充填体的抗压强度。为得到经济、高效的减水剂添加参数, 以萘系、氨基、木钙和脂肪族4 种减水剂结合新型充填胶凝材料, 应用某矿山的全尾砂进行配比实验, 建立GA-SVM 预测模型进行优化选择。在优选过程中, 以4 种减水剂的添加量作为输入因子, 以充填体28 d 龄期单轴抗压强度作为综合输出因子, 根据室内试验, 建立训练、验证样本集;建立支持向量机(SVM)回归预测模型, 通过遗传算法(GA)对SVM 模型参数进行优化选择, 当4 种减水剂组合添加的质量分数依次为0、0.35%、0.30%、0.60%, 抗压强度预测值为4.20 MPa。与实验对比, 该模型预测结果的相对误差能控制在1%以下, 精确度较高, 为减水剂添加参数的优选提供了一种新思路。

关键词

充填料浆 / 减水剂 / 单轴抗压强度 / 支持向量机 / 遗传算法

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To improve the working performance of filling slurry, increasing strength and density of backfill body is a research direction of mine filling method. Filling ratio experiments showed that adding appropriate amount of water reducers during the preparation of the filling material could increase the compressive strength of the filling body. In order to obtain economic and efficient water reducers and parameters, four kinds of water reducers, i.e., naphthalene, amino, wood, and calcium binding aliphatic were used for new filling materials. A match experiment with certain mine backfilling materials was carried out, and a GA-SVM prediction model was established to optimize the selection. The four kinds of water reducers were used as the input data and the 28 days compressive strengths of filling body were confirmed to be the synthesized output data. Some training and validating samples were established through indoor experiment; a support vector machine (SVM) regression model was established. Then, the model parameters were optimized through the genetic algorithm (GA). The results show that the best tailing concentrations of the four kinds of water reducers were 0, 0.35%, 0.30%, and 0.60%, and that the compressive strength of filling body could be 4.20 MPa. Compared with the experiment results, the relative error of the prediction result can be controlled within about 1%. This model provides a new method to optimize sedimentation parameters.

Key words

filling slurry / water-reducers / compressive strength / support vector machine / genetic algorithm

引用本文

张钦礼, 李浩, 刘吉祥, 刘群武, 陈秋松. 基于GA-SVM模型预测减水剂对充填体强度的影响. 科技导报, 2015 , 33 (11) : 44 -48 . DOI: 10.3981/j.issn.1000-7857.2015.11.007

ZHANG Qinli, LI Hao, LIU Jixiang, LIU Qunwu, CHEN Qiusong. Prediction of water-reducers influence on strength of backfill body using GA-SVM model[J]. Science & Technology Review, 2015 , 33 (11) : 44 -48 . DOI: 10.3981/j.issn.1000-7857.2015.11.007

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2015年第33卷第11期

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doi: 10.3981/j.issn.1000-7857.2015.11.007

接收时间：2014-11-25
首发时间：2015-06-11
出版时间：2015-06-13

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收稿日期：2014-11-25
修回日期：2015-01-16

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https://castjournals.cast.org.cn/joweb/kjdb/CN/10.3981/j.issn.1000-7857.2015.11.007

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2种不同金属材料的力学参数

科 Family	属数 Number of genus	种数 Number of species	占总种数比例 Percentage of total species (%)	属 Genus	种数 Number of species	占总种数比例 Percentage of total species (%)
鹅膏菌科Amanitaceae	2	11	5.26	鹅膏菌属 Amanita	10	4.78
小菇科 Mycenaceae	2	12	5.74	丝盖伞属 Inocybe	5	2.39
多孔菌科 Polyporaceae	8	14	6.70	蜡蘑属 Laccaria	5	2.39
红菇科 Russulaceae	3	23	11.00	小皮伞属 Marasmius	6	2.87
				小菇属 Mycena	11	5.26
				光柄菇属 Pluteus	5	2.39
				红菇属 Russula	17	8.13
				栓菌属 Trametes	5	2.39

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