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.

scholarly journals Compressive Strength Evaluation of Concrete Confined With Spiral Stirrups by Using Adaptive Neuro-fuzzy Inference System (ANFIS)

2021 ◽  
Author(s):  
Wei Chang ◽  
Wenzhong Zheng
Keyword(s):  
Compressive Strength ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Triaxial Compression ◽  
Concrete Columns ◽  
Confined Concrete ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Compressive Strength Of Concrete

Abstract The compressive strength of concrete confined with spiral stirrups was an important parameter to evaluate the load-bearing capacity of concrete columns. The confinement provided by spiral stirrups let concrete under the triaxial compression state and improved the compressive strength of concrete. However, the relationships between concrete and stirrups were complex and the existing prediction models for evaluating the compressive strength of confined concrete were various. In this paper, an adaptive neural-fazzy inferenxe system (ANFIS) model was developed to evaluate the compressive strength of concrete confined with stirrups. A set of 231 experimental results of concrete confined with spiral stirrups were collected from the previous studies to establish a reliable database. The investigated parameters included the aspect ratio of specimens, the diameter, spacing, yield strength, and volumetric ratio of stirrups, the ratio of longitudinal reinforcement, and the compressive strength of concrete. The results showed that the ANFIS model predicted the compressive strength of confined concrete accurately. By comparing with existing models, the proposed ANFIS model had high applicable and reliability. The effects of the investigated parameters on the compressive strength of concrete were analyzed based on the proposed ANFIS model.

Adaptive Neuro-Fuzzy Inference System for Predicting Compressive Strength of Fibres Self Compacting Concrete

2019 ◽  
Vol 892 ◽  
pp. 46-54
Author(s):  
L.V. Prasad Meesaraganda ◽  
Prasenjit Saha
Keyword(s):  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Data Sets ◽  
Fine Aggregate ◽  
Self Compacting Concrete ◽  
Anfis Model ◽  
Inference System

This research focused on the applicability of Adaptive Network-Based Fuzzy Inference System (ANFIS) for predict the compressive strength of fibers self-compacting concrete. An ANFIS model combines the benefit of ANN and fuzzy logic. The data developed experimentally for fibers self-compacting concrete and the data sets of a total 99 concrete samples were used in this work. In this paper research is computational based for prediction of concrete compressive strength. A model was developed using ANFIS with five input nodes as w/p ratio, course aggregate, fine aggregate, fiber and superplastizers. In this model Feed-forward three-layer back-propagation neural networks with 10 hidden nodes were examined using learning algorithm. ANFIS model proposed analytically that gives more compatible results. Hence, the model is adopted to predict the strength of fibrous self-compacting concrete.

scholarly journals Effective Compressive Strengths of Corner and Edge Concrete Columns Based on an Adaptive Neuro-Fuzzy Inference System

2020 ◽  
Vol 10 (10) ◽  
pp. 3475
Author(s):  
Hae-Chang Cho ◽  
Seung-Ho Choi ◽  
Sun-Jin Han ◽  
Sang-Hoon Lee ◽  
Heung-Youl Kim ◽  
...  
Keyword(s):  
Compressive Strength ◽  
Fuzzy Inference System ◽  
Load Transfer ◽  
Fuzzy Inference ◽  
Learning System ◽  
Design Codes ◽  
Anfis Model ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Current Design

In the current design codes, the effective compressive strength can be used to reflect decrease in load-transfer performance when upper/lower columns and slabs have different concrete compressive strengths. In this regard, this study proposed a method that can accurately estimate the effective compressive strengths by using an adaptive neuro-fuzzy inference system (ANFIS). The ANFIS is an algorithm that introduces a learning system that corrects errors into a fuzzy theory and has widely been used to solve problems with complex mechanisms. In order to constitute the ANFIS algorithm, 50 data randomly extracted from 75 existing test datasets were used in training, and 25 were used for verification. It was found that analysis using the ANFIS model provides a more accurate evaluation of the effective compressive strengths of corner and edge columns than do the equations specified in the current design codes. In addition, parametric studies were performed using the ANFIS model, and a simplified equation for calculating the effective compressive strength was proposed, so that it can be easily used in practice.

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