Prediction of interlayer shear strength parameters for RCC dams using artificial neural network and fuzzy logic system

被引：0
|
作者：
Shen J. [1 ]
Xu Q. [1 ]
机构：
[1] State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing
来源：
Qinghua Daxue Xuebao/Journal of Tsinghua University | 2019年 / 59卷 / 05期
关键词：
Artificial neural networks; Fuzzy logic systems; Roller compacted concrete dams; Shear strength;
D O I：
10.16511/j.cnki.qhdxxb.2018.25.058
中图分类号：
学科分类号：
摘要：
Predicting the shear strength of roller compacted concrete dams always involves uncertainty and nonlinearities that are influenced by various factors. This study used the artificial neural network and the fuzzy logic system to predict the interlayer shear strength parameters of RCC dams. The data input to the artificial neural network and the fuzzy logic system was divided into data sets from lab tests and data sets from in-situ shear strength tests with five input parameters for the water-binder ratio, total amount of binder, fly ash content, interlayer processing methods and time intervals with two output parameters for f' and c'. The training and testing results show that the lab tests have better correlations than the in-situ tests. Moreover, the artificial neural network has better prediction accuracy than the fuzzy logic system. The predicted values from both the artificial neural network and the fuzzy logic system are in good agreement with the test data. Thus, both models can be used to predict the shear strength parameters and can provide reference predictions for evaluating combinations of layers. © 2019, Tsinghua University Press. All right reserved.
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页码：345 / 353
页数：8
相关论文
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