The prediction analysis of compressive strength and electrical resistivity of environmentally friendly concrete incorporating natural zeolite using artificial neural network

2022 ◽  
Vol 317 ◽  
pp. 125876
Author(s):  
Amir Ali Shahmansouri ◽  
Maziar Yazdani ◽  
Mehdi Hosseini ◽  
Habib Akbarzadeh Bengar ◽  
Hamid Farrokh Ghatte
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Electrical Resistivity ◽  
Natural Zeolite ◽  
Environmentally Friendly ◽  
Prediction Analysis ◽  
Artificial Neural

scholarly journals Metaheuristic Prediction of the Compressive Strength of Environmentally Friendly Concrete Modified with Eggshell Powder Using the Hybrid ANN-SFL Optimization Algorithm

Materials ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6172
Author(s):  
Seyed Vahid Razavi Tosee ◽  
Iman Faridmehr ◽  
Chiara Bedon ◽  
Łukasz Sadowski ◽  
Nasrin Aalimahmoody ◽  
...  
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Optimization Algorithm ◽  
Environmentally Friendly ◽  
Shuffled Frog Leaping ◽  
Artificial Neural

The aim of this article is to predict the compressive strength of environmentally friendly concrete modified with eggshell powder. For this purpose, an optimized artificial neural network, combined with a novel metaheuristic shuffled frog leaping optimization algorithm, was employed and compared with a well-known genetic algorithm and multiple linear regression. The presented results confirm that the highest compressive strength (46 MPa on average) can be achieved for mix designs containing 7 to 9% of eggshell powder. This means that the strength increased by 55% when compared to conventional Portland cement-based concrete. The comparative results also show that the proposed artificial neural network, combined with the novel metaheuristic shuffled frog leaping optimization algorithm, offers satisfactory results of compressive strength predictions for concrete modified using eggshell powder concrete. Moreover, it has a higher accuracy than the genetic algorithm and the multiple linear regression. This finding makes the present method useful for construction practice because it enables a concrete mix with a specific compressive strength to be developed based on industrial waste that is locally available.

scholarly journals Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Kraiwut Tuntisukrarom ◽  
Raungrut Cheerarot
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Compressive Strength ◽  
Bottom Ash ◽  
Strength Development ◽  
Ann Model ◽  
Explicit Equation ◽  
Strength Behavior ◽  
Artificial Neural ◽  
Hidden Layer

The objective of this work was to examine the compressive strength behavior of ground bottom ash (GBA) concrete by using an artificial neural network. Four input parameters, specifically, the water-to-binder ratio (WB), percentage replacement of GBA (PR), median particle size of GBA (PS), and age of concrete (AC), were considered for this prediction. The results indicated that all four considered parameters affect the strength development of concrete, and GBA with a high fineness can act as a good pozzolanic material. The optimal ANN model had an architecture with two hidden layers, with six neurons in the first hidden layer and one neuron in the second hidden layer. The proposed ANN-based explicit equation represented a highly accurate predictive model, for which the statistical values of R2 were higher than 0.996. Moreover, the compressive strength behavior determined using the optimal ANN model closely followed the trend lines and surface plots of the experimental results.

Sign in / Sign up

Export Citation Format

Share Document