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Prediction of Compressive Strength of Plain Concrete Confined with Ferrocement using Artificial Neural Network (ANN) and Comparison with Existing Mathematical Models

机译：使用人工神经网络（ANN）预测普通混凝土压缩强度局限性，与现有数学模型的比较

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S. U. Khan; T. Ayub; S. F. A. Rafeeqi;
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年度 2013
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1. Prediction of Compressive Strength of Plain Concrete Confined with Ferrocement using Artificial Neural Network (ANN) and Comparison with Existing Mathematical Models [J] . S. U. Khan, T. Ayub, S. F. A. Rafeeqi American Journal of Civil Engineering and Architecture . 2013,第1期

机译：人工神经网络（ANN）预测高铁约束混凝土的抗压强度及与现有数学模型的比较
2. Prediction of Compressive Strength of Plain Concrete Confined with Ferrocement using Artificial Neural Network (ANN) [J] . Tehmina Ayub, M. Fadhil Nuruddin, Sadaqat Ullah Khan, International Journal of Soft Computing and Software Engineering . 2013,第3期

机译：人工神经网络（ANN）预测加铁约束的普通混凝土抗压强度
3. An Artificial Neural Networks model for the prediction of the compressive strength of FRP-confined concrete circular columns [J] . Cascardi Alessio, Micelli Francesco, Aiello Maria Antonietta Engineering Structures . 2017,第JUNa1期

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4. Compressive Strength Prediction of Concrete Recycled Aggregates made from Ceramic Tiles using Feedforward Artificial Neural Network (FANN) [C] . Chan Chee Kin, Mashitah Mat Don, Ahmad Z International Symposium on Process Systems Engineering . 2012

机译：使用前馈人工神经网络（FANN）的陶瓷砖制成的混凝土再循环压缩强度预测
5. Developing an accurate forecasting model for temporal and spatial ambulance demand via artificial neural networks: A comparative study of existing forecasting techniques vs. an artificial neural network. [D] . Setzler, Hubert Holland, III. 2007

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6. Learned Prediction of Compressive Strength of GGBFS Concrete Using Hybrid Artificial Neural Network Models [O] . In-Ji Han, Tian-Feng Yuan, Jin-Young Lee, 2019

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7. Prediction of Concrete Compressive Strength Slump using Artificial Neural Networks (ANN) [O] . Yasir M. H. Badawi, Yousif Hummaida Ahmed 2021

机译：使用人工神经网络预测混凝土抗压强度和坍塌（ANN）

Prediction of Compressive Strength of Plain Concrete Confined with Ferrocement using Artificial Neural Network (ANN) and Comparison with Existing Mathematical Models

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