Estimating the compressive strength of concrete using multiple linear regression and adaptive neuro-fuzzy inference system

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.

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Coparison of artificial intelligence methods for predicting compressive strength of concrete

Journal of the Croatian Association of Civil Engineers ◽

10.14256/jce.3066.2020 ◽

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.

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Multiple linear regression, multi-layer perceptron network and adaptive neuro-fuzzy inference system for forecasting precipitation based on large-scale climate signals

Hydrological Sciences Journal ◽

10.1080/02626667.2014.966721 ◽

2016 ◽

Vol 61 (6) ◽

pp. 1001-1009 ◽

Cited By ~ 51

Author(s):

Bahram Choubin ◽

Shahram Khalighi-Sigaroodi ◽

Arash Malekian ◽

Özgür Kişi

Keyword(s):

Linear Regression ◽

Multiple Linear Regression ◽

Fuzzy Inference System ◽

Large Scale ◽

Fuzzy Inference ◽

Multi Layer Perceptron ◽

Inference System ◽

Neuro Fuzzy ◽

Climate Signals

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