Adaptive neuro-fuzzy inference system prediction model for the mechanical behaviour of rice husk ash and periwinkle shell concrete blend for sustainable construction

2021 ◽  
Author(s):  
George U. Alaneme ◽  
Elvis M. Mbadike ◽  
Uzoma I. Iro ◽  
Iberedem M. Udousoro ◽  
William C. Ifejimalu
Keyword(s):  
Prediction Model ◽  
Rice Husk ◽  
Mechanical Behaviour ◽  
Fuzzy Inference System ◽  
Rice Husk Ash ◽  
Fuzzy Inference ◽  
Sustainable Construction ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Periwinkle Shell

Thermal Error Prediction of the Spindle Using Adaptive Neuro-Fuzzy Inference System

2015 ◽  
Vol 789-790 ◽  
pp. 263-267
Author(s):  
Yan Lei Li ◽  
Ming Yan Wang ◽  
You Min Hu ◽  
Bo Wu
Keyword(s):  
Prediction Model ◽  
Predictive Model ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Thermal Error ◽  
Temperature Data ◽  
Current Sensor ◽  
Error Prediction ◽  
Inference System ◽  
Neuro Fuzzy

This paper proposes a new method to predict the spindle deformation based on temperature data. The method introduces ANFIS (adaptive neuro-fuzzy inference system). For building the predictive model, we first extract temperature data from sensors in the spindle, and then they are used as the inputs to train ANFIS. To evaluate the performance of the prediction, an experiment is implemented. Three Pt-100 thermal resistances is used to monitor the spindle temperature, and an inductive current sensor is used to obtain the spindle deformation. The experimental results display that our prediction model can better predict the spindle deformation and improve the performance of the spindle.

Mechanical behaviour optimization of saw dust ash and quarry dust concrete using adaptive neuro-fuzzy inference system

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
George Uwadiegwu Alaneme ◽  
Elvis Michael Mbadike ◽  
Imoh Christopher Attah ◽  
Iberedem Monday Udousoro
Keyword(s):  
Mechanical Behaviour ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Saw Dust ◽  
Neuro Fuzzy ◽  
Quarry Dust

scholarly journals Prediction of Compressive Strength of Rice Husk Ash Concrete through Different Machine Learning Processes

Crystals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 352
Author(s):  
Ammar Iqtidar ◽  
Niaz Bahadur Khan ◽  
Sardar Kashif-ur-Rehman ◽  
Muhmmad Faisal Javed ◽  
Fahid Aslam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Rice Husk ◽  
Carbon Dioxide Emissions ◽  
Rice Husk Ash ◽  
Fuzzy Inference ◽  
Correlation Factor ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Data Points

Cement is among the major contributors to the global carbon dioxide emissions. Thus, sustainable alternatives to the conventional cement are essential for producing greener concrete structures. Rice husk ash has shown promising characteristics to be a sustainable option for further research and investigation. Since the experimental work required for assessing its properties is both time consuming and complex, machine learning can be used to successfully predict the properties of concrete containing rice husk ash. A total of 192 data points are used in this study to assess the compressive strength of rice husk ash blended concrete. Input parameters include age, amount of cement, rice husk ash, super plasticizer, water, and aggregates. Four soft computing and machine learning methods, i.e., artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), multiple nonlinear regression (NLR), and linear regression are employed in this research. Sensitivity analysis, parametric analysis, and correlation factor (R2) are used to evaluate the obtained results. The ANN and ANFIS outperformed other methods.

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