Predicting durability of geosynthetics by neural networks

MIN Xing; ZHANG Meng-xi; TAO Chen

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Predicting durability of geosynthetics by neural networks

更新时间：2026-06-25

- Predicting durability of geosynthetics by neural networks
- Vol. 27, Issue 2, (2006)
- 作者机构：
  
  上海大学土木工程系,上海,200072
- 作者简介：
- 基金信息：
- DOI：
  CLC： TU443
- Online First：25 October 2013，
  
  Published：2006
- 稿件说明：
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MIN Xing, ZHANG Meng-xi, TAO Chen. Predicting durability of geosynthetics by neural networks[J]. 2006, 27(2).
DOI：

MIN Xing, ZHANG Meng-xi, TAO Chen. Predicting durability of geosynthetics by neural networks[J]. 2006, 27(2). DOI：

摘要

以BP人工神经网络为工具，利用其强大的非线性映射能力，在综合分析土工合成材料耐久性影响因素的基础上将土工合成材料所处环境的温度、湿度、紫外线照射情况以及土工合成材料的老化时间作为网络的输入参数，以描述土工合成材料耐久性状态的强度和延伸率作为网络的输出，建立了神经网络模型。采用大量试验数据对网络进行了训练和检验，并对土工合成材料的耐久性进行了预测。结果表明，预测值和试验结果比较接近，该网络能较好地反映土工合成材料耐久性与其影响因素之间的非线性映射关系。

Abstract

The ANN model has great potential in predicting durability of geasynthetics

because of its unique learning. training and prediction characteristics. By contrast to the regression method

the ANN method doesn’t demand the regulation of the experiment data. Based on 31 groups of experimental data obtained from aging tests of non-woven polyester

a BP neural network model for predicting durability of geosynthetics is developed. The model proposed has four parameters (the temperature

relative humidity

aging time and ultraviolet radiation) at input layer and two parameters (strength and percentage elongation) at output layer. The comparison between the prediction values and experimental results shows that the model proposed has high accuracy for predicting durability of geocynthetics.

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