基于神经网络模型的立井冻结施工预测研究  

Study on the prediction of the vertical shaft freezing construction based on neural network model

作　　者：卫建军[1] 张杰[2] 

机构地区：[1]西安科技大学建筑与土木工程学院,陕西西安710054 [2]西安科技大学能源学院,陕西西安710054

出　　处：《采矿与安全工程学报》2015年第1期105-111,共7页Journal of Mining & Safety Engineering

基　　金：国家自然科学基金青年基金项目(51204113);国家自然科学基金项目(51474173)

摘　　要：在立井冻结施工过程中,及时了解不同深度、不同岩性地层下冻结壁交圈时间及其形成特性是实现科学施工的前提和基础。针对现有立井冻结设计存在的客观问题,应用神经网络系统理论,合理确定输入参数及输出参数,在学习训练的基础上,建立了胡家河矿井筒冻结施工信息的神经网络预测模型。采用该模型分别对主井、副井及风井冻结壁的交圈时间、内外侧扩展范围、平均扩展速度、有效厚度、井帮温度、荒径范围、平均温度等特性参数进行了工程预报,并对实测数据及预测数据进行了对比分析。结果表明:现场实测结果与预测结果基本吻合,预测模型准确度高,适用性广,为科学设计立井施工方法及其支护方案提供了理论依据。The premise and foundation of the scientific construction is to know the circle time and formation characteristics of the frozen wall under the different depths and lithologic strata in the process of vertical shaft freezing construction. In response to the existing objective problems of the vertical shaft freeze design, the reasonable input parameters as well as output parameters have been determined by using the neural network system theory. Based on the learning and training, the neural network prediction model of the vertical shaft freezing construction information has been set up. And then, the forming time and characteristic parameters such as inside and outside extended range, the average propagation velocity, effective thickness, the temperature of the wall, diameter range of the frozen rock-soil, average temperature have been predicted. Finally, the measured data and predicted data have been analyzed contrastively. The results show that, the measured results are consistent well with the predicted results. The prediction model has the advantages of the high prediction accuracy, wide applicability, which provides a theoretic foundation for the scientific design of the construction method and the supporting schemes during the course of the vertical shaft construction.

关 键 词：立井施工 冻结壁 神经网络 工程预报 

分 类 号：TD262[矿业工程—矿井建设]