Predicting terrane crack depth of complex fire hole using artificial neural network

CHANG Bin; LI Ning; ZHANG Ping

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Predicting terrane crack depth of complex fire hole using artificial neural network

更新时间：2026-06-25

- Predicting terrane crack depth of complex fire hole using artificial neural network
- Vol. 24, Issue 2, (2003)
- 作者机构：
  
  西安理工大学岩土所,陕西,西安,710048
- 作者简介：
- 基金信息：
- DOI：
  CLC： TU43
- Online First：19 August 2014，
  
  Published：2003
- 稿件说明：
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CHANG Bin, LI Ning, ZHANG Ping. Predicting terrane crack depth of complex fire hole using artificial neural network[J]. 2003, 24(2).
DOI：

CHANG Bin, LI Ning, ZHANG Ping. Predicting terrane crack depth of complex fire hole using artificial neural network[J]. 2003, 24(2). DOI：

摘要

以神经网络为基本工具，利用其强大的非线性映射能力，并结合有限元法动力分析成果，为预测在复合射孔条件下岩层裂缝扩展深度提供了一条新的途径。结果表明，用神经网络方法能够充分体现爆燃参数与岩层裂缝扩展深度之间的非线性隐含关系，具有较高的拟合精度，其结果与有限元方法的计算值接近，简单实用。

Abstract

By using ANN and its powerful nonlinear mapping ability and combining production of FEM dynamic analysis

a new way to predicting terrane crack depth of complex fire hole is offered. The result has indicated that the ANN can incarnate the connotative nonlinear relation between explosive parameters and terrane crack depth fully. This method has higher fitting precision

and its result coinsides with the result of FEM.

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