Prediction of Unconfined Compressive Strength of Microfine Cement Injected Sands Using Fuzzy Logic Method
Prediction of Unconfined Compressive Strength of Microfine Cement Injected Sands Using Fuzzy Logic Method
复制标题
模糊逻辑法预测超细水泥注砂无侧限抗压强度
DOI：
10.21203/rs.3.rs-232296/v1
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Nurten AKGÜN TANBAY
中科院分区：
文献类型：
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作者：
Eray Yildirim;Eyubhan Avci;Nurten AKGÜN TANBAY
采用模糊逻辑方法对粉喷砂土的无侧限抗压强度进行了预测。在模糊逻辑模型中应用了Mamdani和Sugeno方法。此外，为了比较这两种方法，进行了回归分析。模型以水灰比和注水压力为输入变量，无侧限抗压强度为输出变量。该数据集包括427个样品，这些样品是用超细水泥实验性注入的。无侧限抗压强度的预测，通过建立隶属函数和规则库的每个输入（预测）参数的模糊逻辑模型。决定系数（R2）和均方误差（MSE）被用作评价所开发的模型的性能的标准。结果表明，Mamdani、Sugeno和回归三种应用模型的结果具有统计学意义，这些方法可用于未来基于预测的研究。结果表明，Sugeno模型预测无侧限抗压强度的效果最好。其次是Mamdani和回归模型。这项研究表明，模糊逻辑方法可以替代传统上用于预测过程的回归方法。
In this study, unconfined compressive strength values of sand soil injected with microfine cement were predicted using fuzzy logic method. Mamdani and Sugeno methods were applied in the fuzzy logic models. In addition, a regression analysis was carried out in order to compare these two methods. In the models, water/cement ratio and injection pressure were the input variables, and unconfined compressive strength was the output variable. The dataset includes 427 samples, which were experimentally injected with microfine cement. Predictions for unconfined compressive strength were obtained by creating membership functions and rule base for each input (predictive) parameter in fuzzy logic models. The coefficient of determination (R2) and Mean Square Error (MSE) were used as criteria for evaluating the performance of the developed models. The results suggested that the three applied models (i.e. Mamdani, Sugeno and regression) provided statistically significant results, and these methods could be used in the future prediction-based studies. The results showed that Sugeno model provided the best performance for predicting unconfined compressive strength. It was followed by Mamdani and Regression models, respectively. This study has suggested that the fuzzy logic method can be an alternative to the regression method which traditionally has been used in prediction process.