摘要
为准确评价隧洞施工TBM掘进适应性,保障TBM安全、高效施工,提出一种基于灰色关联分析(GRA)与麻雀搜索算法(SSA)优化Elman神经网络的TBM掘进适应性预测模型。首先,从地质条件、掘进参数、不良地质、施工组织4个方面综合考虑,初步选取13个主要影响因素,建立隧洞TBM掘进适应性评价指标体系;然后,利用GRA分析指标与掘进适应性间的关联性,引入SSA优化Elman神经网络,提高模型性能,并采用留一交叉验证法验证模型的准确性及可靠性,使得模型最接近原始数据分布特征;最后,结合北疆水利工程某标段中待测样本对模型预测效果进行验证,同时与Elman、PSO-Elman、BP神经网络模型预测结果及现场实际结果对比分析。结果表明:SSA-Elman模型预测结果与实际工程结果吻合度较高,该模型能够正确、有效地对TBM掘进适应性进行预测评价,且具有合理性和可操作性,可为隧洞TBM适应性评价提供一种新方法。
In this study,a tunnel boring machine(TBM)tunneling adaptability prediction model based on the Elman neural network improved by grey relational analysis(GRA)and sparrow search algorithm(SSA)is proposed to accurately evaluate the adaptability of a TBM in a tunnel and ensure safe and efficient TBM tunneling.First,13 primary influencing factors are preliminarily selected to establish an evaluation index system of tunnel TBM tunneling adaptability based on geological conditions,tunneling parameters,unfavorable geologies,and construction organization.Second,the correlation between the index and tunneling adaptability is analyzed using GRA.Then,SSA is introduced to optimize the Elman neural network to improve the model′s performance,and the left-one-cross-validation method is used to validate the model′s accuracy and reliability,so that the model is close to the original data′s distribution characteristics.Finally,the model′s applicable results in a bid section of the North Xinjiang water diversion project are compared with those of Elman,PSO-Elman,BP neural network models,and the field data.The comparative results show that the SSA-Elman model′s prediction results are the most consistent with the actual engineering results,indicating that the model can correctly and effectively predict and evaluate the TBM tunneling adaptability with high rationality and operability.The results can provide a new method for evaluating tunnel TBM adaptability.
作者
赵雪
顾伟红
ZHAO Xue;GU Weihong(College of Civil Engineering,Lanzhou Jiaotong University,Lanzhou 730070,Gansu,China)
出处
《隧道建设（中英文）》
CSCD
北大核心
2022年第11期1879-1888,共10页
Tunnel Construction
基金
国家自然科学基金(51668037)。
关键词
隧洞施工
TBM掘进适应性
灰色关联分析
麻雀搜索算法
ELMAN神经网络
tunnel construction
driving adaptability of tunnel boring machine
grey correlation analysis
sparrow search algorithm
Elman neural network
