Comparison of methods of prediction of compressive strength of concrete using multiple linear regression in microsoft excel and artificial neural networks in RStudio

2021 ◽  
Author(s):  
Mohammed Hussain ◽  
G. Yedukondalu ◽  
Y. Kamala Raju ◽  
V. Kamakshi Prasad
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Comparison Of Methods ◽  
Microsoft Excel ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural

scholarly journals Prediction of Compressive Strength of Concrete in Wet-Dry Environment by BP Artificial Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Chengyao Liang ◽  
Chunxiang Qian ◽  
Huaicheng Chen ◽  
Wence Kang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Artificial Neural Networks ◽  
Statistical Analysis ◽  
Influencing Factors ◽  
Mechanical Performance ◽  
Tidal Zone ◽  
Structure Degradation ◽  
Compressive Strength Of Concrete ◽  
Artificial Neural

Engineering structure degradation in the marine environment, especially the tidal zone and splash zone, is serious. The compressive strength of concrete exposed to the wet-dry cycle is investigated in this study. Several significant influencing factors of compressive strength of concrete in the wet-dry environment are selected. Then, the database of compressive strength influencing factors is established from vast literature after a statistical analysis of those data. Backpropagation artificial neural networks (BP-ANNs) are applied to establish a multifactorial model to predict the compressive strength of concrete in the wet-dry exposure environment. Furthermore, experiments are done to verify the generalization of the BP-ANN model. This model turns out to give a high accuracy and statistical analysis to confirm some rules in marine concrete mix and exposure. In general, this model is practical to predict the concrete mechanical performance.

Agroclimatology-Based Yield Model for Carrot Using Multiple Linear Regression and Artificial Neural Networks

2013 ◽  
Vol 105 (3) ◽  
pp. 863-873 ◽  
Author(s):  
Arumugam Thiagarajan ◽  
Rajasekaran R. Lada ◽  
Sivakami Muthuswamy ◽  
Azure Adams
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Yield Model ◽  
Artificial Neural
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