[1]任松,姜德义,杨春和..基于遗传算法的浅埋隧道开挖地表沉降神经网络预测[J].郑州大学学报(工学版),2006,27(03):46-49.[doi:10.3969/j.issn.1671-6833.2006.03.011]
　Ren Song,JIANG Deyi,Yang Chunhe.Prediction of surface settlement neural network for shallow buried tunnel excavation based on genetic algorithm[J].Journal of Zhengzhou University (Engineering Science),2006,27(03):46-49.[doi:10.3969/j.issn.1671-6833.2006.03.011]
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基于遗传算法的浅埋隧道开挖地表沉降神经网络预测()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
27
期数:
2006年03期
页码:
46-49
栏目:
出版日期:
1900-01-01

文章信息/Info

Title:
Prediction of surface settlement neural network for shallow buried tunnel excavation based on genetic algorithm
作者:
任松; 姜德义; 杨春和.
重庆大学西南资源开发及环境灾害控制工程教育部重点实验室,重庆,400044;中国科学院武汉岩土力学研究所,武汉,430071, 重庆大学西南资源开发及环境灾害控制工程教育部重点实验室,重庆,400044
Author(s):
Ren Song; JIANG Deyi; Yang Chunhe
关键词:
遗传算法;  神经网络;  隧道;  地表沉降
Keywords:
DOI:
10.3969/j.issn.1671-6833.2006.03.011
文献标志码:
A
摘要:
分析了城市浅埋隧道开挖地表沉降的主要影响因素,并建立了基于遗传算法的神经网络浅埋隧道开挖地表沉降预测模型.使用有限元数值模拟正演算法获得神经网络模型学习样本,对模型进行学习训练.该预测模型在某市轻轨隧道地表沉降预测中进行使用,结果表明:基于遗传算法的神经网络对隧道开挖地表沉降的预测是可行的,预测结果比较准确,能较好地指导隧道施工,确保地表建筑物的安全.
Abstract:
The main influencing factors of surface settlement in urban shallow buried tunnel excavation were analyzed, and a neural network surface settlement prediction model for shallow buried tunnel excavation based on genetic algorithm was established. The finite element numerical simulation forward algorithm is used to obtain the neural network model learning samples and train the model. The prediction model is used in the prediction of surface settlement of light rail tunnel in a city, and the results show that the prediction of surface settlement of tunnel excavation by neural network based on genetic algorithm is feasible, and the prediction results are relatively accurate, which can better guide tunnel construction and ensure the safety of surface buildings.

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更新日期/Last Update: 1900-01-01