巷道通风摩擦阻力系数遗传投影寻踪回归预测  

Ventilation resistance coefficient prediction of tunnels based on GA-projection pursuit regression

作　　者：戚志鹏 高科[1,2] 刘玉姣[1,2] 曹鹏[1] 袁可一 QI Zhipeng;GAO Ke;LIU Yujiao;CAO Peng;YUAN Keyi(College of Safety Science&Engineering,Liaoning Technical University,Huludao 125105,Liaoning,China;Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of Education,Huludao 125105,Liaoning,China)

机构地区：[1]辽宁工程技术大学安全科学与工程学院,辽宁葫芦岛125105 [2]矿山热动力灾害与防治教育部重点实验室,辽宁葫芦岛125105

出　　处：《安全与环境学报》2024年第7期2648-2657,共10页Journal of Safety and Environment

基　　金：国家自然科学基金项目(52074148,52104194)。

摘　　要：为满足智能通风准确获得巷道摩擦风阻的需求,基于遗传算法的投影寻踪回归预测方法预测矿井通风摩擦阻力系数。研究空气密度变化和巷道有无支护两种类型时模型预测的准确性。利用通风摩擦阻力系数影响因素进行训练,对不同支护类型巷道采集学习样本建立模型,使用部分样本进行验证。将模型预测结果与主成分分析预测和BP神经网络预测结果进行比较。对不同支护类型巷道进行了预测,最大误差为1.76%,平均误差为1.07%;仅对圆木支护进行了分析,最大误差为1.73%,平均误差为0.79%;对不同密度无支护巷道预测表明,平均误差为-0.99%,最大误差为-1.01%,风流密度对模型预测结果的准确性基本没有影响。无论是风流密度还是支护形式,该方法预测精度均优于主成分分析和BP神经网络。The Genetic Algorithms(GA)-projection pursuit regression method is employed to predict the ventilation resistance coefficient in mines.The method consists of two steps:building a projection regression model and using a genetic algorithm to find the best projection vector.GA is used to search for the optimal projection direction in the projection pursuit,enabling the transformation of the influencing factors of the high-dimensional ventilation resistance coefficient into a lower-dimensional space.This algorithm combines the data dimension reduction characteristics of projection pursuit with the global search capability of genetic algorithms.This study considers different airflow densities and two types of roadways:supported and unsupported.Factors such as the diameter of the wooden column,longitudinal diameter,roadway cross-section area,roadway perimeter,shotcrete part of the perimeter,metal beam thickness,column thickness,shed spacing,roadway height,and roadway width are used for training.The model is established using 142 learning samples collected from the field,and its performance is validated using 42 actual testing samples.The predicted results are compared with those obtained from Principal Component Analysis(PCA)prediction and Backpropagation Neural Network(BPNN)prediction.The outcomes reveal that the average error of this method is 1.76%,with a maximum error of 1.07%.To further validate the model s reliability,an in-depth analysis and validation are conducted focusing on a specific category of roadway,demonstrating the method s consistency and accuracy.Remarkably,the average error rate is found to be only 1.73%,with a negligible maximum error of 0.79%.Additionally,this study researches the ventilation resistance coefficient prediction of unsupported roadways,resulting in an average error of 1.73%and a maximum error of 0.79%.The model s predictive accuracy is minimally affected by different airflow densities.Regardless of various types of supports or the selection of a specific support type,this method exh

关 键 词：安全工程 矿井通风 摩擦阻力系数 遗传优化算法 投影寻踪回归模型 降维 

分 类 号：X936[环境科学与工程—安全科学]