Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (36): 234-237.

• 工程与应用 • Previous Articles     Next Articles

Comprehensive evaluation model of engineering geology adopted improved Levenberg-
Marquardt neural network

WANG Mingsheng1,2,LV Xikui2   

  1. 1.School of Civil Engineering,Beijing Jiaotong University,Beijing 100044,China
    2.School of?Transportation,Shijiazhuang Tiedao University,Shijiazhuang 050043,China
  • Received:1900-01-01 Revised:1900-01-01 Online:2011-12-21 Published:2011-12-21



  1. 1.北京交通大学 土木建筑工程学院,北京 100044
    2.石家庄铁道大学 交通运输学院,石家庄 050043

Abstract: On the reliable remote sensing geological information,it establishes the evaluation and forecast model of railway route geological hazard using the adaptive adjustment learning step Levenberg-Marquardt neural network.It achieves effective assessment and prediction of engineering geological conditions and geological hazard.It studies the LM neural network’s building,training and simulation methods.Through the quantitative analysis of the evaluation unit,evaluation results are integrated into three-dimensional geographical environment of railway location system and implement the evaluation results visualization.It helps the engineers achieve the visual geological analysis and evaluation.Theoretical analysis and case study show,the Levenberg-Marquardt neural network based on the adaptive adjustment learning step has high accuracy and speed advantages,is an ideal geological hazard assessment method.

Key words: Levenberg-Marquardt neural network, engineering geology, prediction and evaluation, step adaptive adjustment

摘要: 以可靠的遥感地质信息为基础,采用步长自适应调整的Levenberg-Marquardt神经网络建立了工程地质灾害综合评价模型,实现对地质条件和地质灾害危险性的有效评价。通过对地质灾害危险性评价单元进行分析量化,将评价结果集成在三维地理环境中,实现了评价结果的三维可视化,实现对地质条件进行直观分析和评价。实例验证表明,基于步长自适应调整的LM神经网络具有准确度高、速度快的优点,是一种较为理想的工程地质综合评价方法。

关键词: 列文伯格-马夸尔特神经网络, 工程地质, 预测评价, 步长自适应调整