scholarly journals Application of Adaptive Neuro-Fuzzy Inference System for Evaluating Compressive Strength of Concrete

2021 ◽  
Vol 21 (2) ◽  
pp. 176-188
Author(s):  
Deepak Kumar Sinha ◽  
Rupali Satavalekar ◽  
Senthil Kasilingam
Keyword(s):  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Compressive Strength Of Concrete

scholarly journals An artificial intelligence approach for concrete hardened property estimation

2020 ◽  
Vol 14 (2) ◽  
pp. 40-52 ◽  
Author(s):  
Tu Trung Nguyen ◽  
Kien Dinh
Keyword(s):  
Artificial Intelligence ◽  
Sensitivity Analysis ◽  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Fine Aggregate ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Compressive Strength Of Concrete

An alternative method using Artificial Intelligence (AI) to predict the 28-day strength of concrete from its primary ingredients is presented in this research. A series of 424 data samples collected from a previous study were employed for developing, testing, and validation of Adaptive Neuro-Fuzzy Inference System (ANFIS) models. Seven mix parameters, namely Cement, Blast Furnace Slag, Fly Ash, Water, Superplasticizer, Coarse Aggregate, and Fine Aggregate were used as the inputs of the models while the output was the 28-day compressive strength of concrete. In the first step, different models with various input membership functions were explored and compared to obtain an optimal ANFIS model. In the second step, that model was utilized to predict the compressive strength value for each concrete sample, and to compare with those obtained from the compressive test in laboratory. The results showed that the selected ANFIS model can be used as a reliable tool for predicting the compressive strength of concrete with Root Mean Squared Error values of 5.97 MPa and 7.73 MPa, respectively, for the training and test sets. In addition, the sensitivity analysis results revealed that the accuracy of the proposed model improved with an increase in the number of input parameters/variables. Keywords: artificial intelligence; adaptive neuro-fuzzy inference system; concrete strength; sensitivity analysis.

scholarly journals Coparison of artificial intelligence methods for predicting compressive strength of concrete

2021 ◽  
Vol 73 (06) ◽  
pp. 617-632
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Inference System ◽  
Artificial Intelligence Methods ◽  
Neuro Fuzzy ◽  
Compressive Strength Of Concrete

Compressive strength of concrete is an important parameter in concrete design. Accurate prediction of compressive strength of concrete can lower costs and save time. Therefore, thecompressive strength of concrete prediction performance of artificial intelligence methods (adaptive neuro fuzzy inference system, random forest, linear regression, classification and regression tree, support vector regression, k-nearest neighbour and extreme learning machine) are compared in this study using six different multinational datasets. The performance of these methods is evaluated using the correlation coefficient, root mean square error, mean absolute error, and mean absolute percentage error criteria. Comparative results show that the adaptive neuro fuzzy inference system (ANFIS) is more successful in all datasets.

Prediction of Concrete Strength Using Fuzzy Neural Networks

2011 ◽  
Vol 243-249 ◽  
pp. 6121-6126 ◽  
Author(s):  
Jing Xu ◽  
Xiu Li Wang
Keyword(s):  
Neural Networks ◽  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Concrete Strength ◽  
Fuzzy Neural Networks ◽  
Output Data ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Neural

The purpose of this paper is to develop the Ⅰ-PreConS (Intelligent PREdiction system of CONcrete Strength) that predicts the compressive strength of concrete to improve the accuracy of concrete undamaged inspection. For this purpose, the system is developed with adaptive neuro－fuzzy inference system (ANFIS) that can learn cube test results as training patterns. ANFIS does not need a specific equation form differ from traditional prediction models. Instead of that, it needs enough input-output data. Also, it can continuously re-train the new data, so that it can conveniently adapt to new data. In the study, adaptive neuro－fuzzy inference system (ANFIS) based on Takagi-Sugeno rules is built up to prediction concrete strength. According to the expert experience, the relationship between the rebound value and concrete strength tends to power function. So the common logarithms of rebound value and strength value are used as the inputs and outputs of the ANFIS. System parameter sets are iteratively adjusted according to input and output data samples by a hybrid-learning algorithm. In the system, in order to improve of the ANFIS, condition parameter sets can be determined by the back propagation gradient descent method and conclusion parameter sets can be determined by the least squares method. As a result, the concrete strength can be inferred by the fuzzy inference. The method takes full advantage of the characteristics of the abilities of Fuzzy Neural Networks (FNN) including automatic learning, generation and fuzzy logic inference. The experiment shows that the average relative error of the predicted results is 10.316% and relative standard error is 12.895% over all the 508 samples, which are satisfied with the requirements of practical engineering. The ANFIS-based model is very efficient for prediction the compressive strength of in-service concrete.

Application of adaptive neuro-fuzzy inference system for strength prediction of rubberized concrete containing silica fume and zeolite

2019 ◽  
Vol 234 (3) ◽  
pp. 438-451 ◽  
Author(s):  
Mostafa Jalal ◽  
Poura Arabali ◽  
Zachary Grasley ◽  
Jeffrey W Bullard
Keyword(s):  
Compressive Strength ◽  
Relative Humidity ◽  
Silica Fume ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Performance Criteria ◽  
System Model ◽  
Rubberized Concrete ◽  
Inference System ◽  
Neuro Fuzzy

Rubberized concrete containing waste tire rubber, silica fume, and zeolite cured in different curing conditions has been investigated in this paper. For this purpose, coarse aggregates were partially replaced by different percentages of waste rubber chips, namely 10% and 15%, and silica fume and zeolite were incorporated into the binder to replace 10% of cement mass. Different mixes were made and cured in two different conditions, namely in water and air with relative humidity of 100% and 50%, respectively. Compressive strengths of mixes were measured at different ages as 3, 7, 28, and 42 days. In order to simulate and predict the compressive strength of the rubberized cement composite, the influencing parameters were considered as cement content, silica fume, zeolite, rubber percentage, relative humidity, and age of the samples. Then, adaptive neuro-fuzzy inference system was employed to develop a prediction model for compressive strength of the concrete. Six variables were introduced into the adaptive neuro-fuzzy inference system model as inputs and the compressive strength was considered as the output. Prediction results and performance criteria were determined for various datasets including training, validation, testing, and all data. Parametric study of the adaptive neuro-fuzzy inference system models was also conducted to investigate the effect of each variable on the compressive strength of the rubberized concrete. Based on the correlations and errors obtained from the model, it was found that the proposed adaptive neuro-fuzzy inference system model can be a robust tool for predicting the behavior of complex composite materials such as rubberized concrete.

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