Evolutionary Fuzzy Function with Support Vector Regression for the Prediction of Concrete Compressive Strength

2011 ◽  
Author(s):  
Siamak Safarzadegan Gilan ◽  
Alireza Mashhadi Ali ◽  
Ali Akbar Ramezanianpour
Keyword(s):  
Compressive Strength ◽  
Support Vector Regression ◽  
Support Vector ◽  
Concrete Compressive Strength ◽  
Fuzzy Function

scholarly journals Prediction of Compressive Strength Using Support Vector Regression

MENDEL ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 51-56
Author(s):  
Goutham J Sai ◽  
Vijay Pal Singh
Keyword(s):  
Compressive Strength ◽  
Support Vector Regression ◽  
Design Stage ◽  
Support Vector ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Study Support ◽  
Compressive Strength Of Concrete ◽  
Non Destructive

At the design stage of a structure, the members of adequate dimension and strength is provided. But with passage of time, the strength of the members reduces gradually due to exposure to environmental conditions and unexpected loadings other than for which the structure is designed. Non Destructive Testing (NDT) method provides a convenient and rapid method of determination of existing strength of concrete without subjecting the member to any damage.  In the present study, Support Vector Regression (SVR) in Python has been used for the prediction of compressive strength of concrete. Three different NDT techniques have been used as input for the SVR model. A good co-relation between predicted strength and strength determined after crushing the concrete cubes has been achieved. It has also been observed that accuracy in the predicted strength is more in case of inputs from more than one NDT technique is used.

Estimating the compressive strength of rectangular fiber reinforced polymer–confined columns using multilayer perceptron, radial basis function, and support vector regression methods

2021 ◽  
pp. 073168442110501
Author(s):  
Yaser Moodi ◽  
Mohammad Ghasemi ◽  
Seyed Roohollah Mousavi
Keyword(s):  
Machine Learning ◽  
Compressive Strength ◽  
Support Vector Regression ◽  
Basis Function ◽  
Concrete Columns ◽  
Fiber Reinforced Polymer ◽  
Support Vector ◽  
Reinforced Polymer ◽  
Compressive Strength Of Concrete ◽  
Rectangular Columns

Recently, there has been a tendency to use machine learning (ML)–based methods, such as artificial neural networks (ANNs), for more accurate estimates. This paper investigates the effectiveness of three different machine learning methods including radial basis function neural network (RBNN), multi-layer perceptron (MLP), and support vector regression (SVR), for predicting the ultimate strength of square and rectangular columns confined by various FRP sheets. So far, in the previous study, several experiments have been conducted on concrete columns confined by fiber reinforced polymer (FRP) sheets with the results suggesting that the use of FRP sheets enhances the compressive strength of concrete columns effectively. Also, a wide range of experimental data (including 463 specimens) has been collected in this study for square and rectangular columns, confined by various FRP sheets. The comparison of ML-derived results with the experimental findings, which were in a very good agreement, demonstrated the ability of ML to estimate the compressive strength of concrete confined by FRP; the correlation coefficient (R2) for MLP, RBFNN, and SVR methods was equal to 0.97, 0.97, and 0.90, respectively. Similar accuracy was obtained by MLP and RBFNN, and they provided better estimates for determining the compressive strength of concrete confined by FRP. Also, the results showed that the difference between statistical indicators for training and testing specimens in the RBFNN method was greater than the MLP method, and this difference indicated the poor performance of RBFNN.

scholarly journals Experimental and Modelling of Alkali-Activated Mortar Compressive Strength Using Hybrid Support Vector Regression and Genetic Algorithm

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 3049
Author(s):  
Khaled A. Alawi Al-Sodani ◽  
Adeshina Adewale Adewumi ◽  
Mohd Azreen Mohd Ariffin ◽  
Mohammed Maslehuddin ◽  
Mohammad Ismail ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Compressive Strength ◽  
Support Vector Regression ◽  
Model Development ◽  
Hybrid Genetic Algorithm ◽  
Limestone Powder ◽  
Support Vector ◽  
Natural Pozzolan ◽  
The One ◽  
Alkali Activated

This paper presents the outcome of work conducted to develop models for the prediction of compressive strength (CS) of alkali-activated limestone powder and natural pozzolan mortar (AALNM) using hybrid genetic algorithm (GA) and support vector regression (SVR) algorithm, for the first time. The developed hybrid GA-SVR-CS1, GA-SVR-CS3, and GA-SVR-CS14 models are capable of estimating the one-day, three-day, and 14-day compressive strength, respectively, of AALNM up to 96.64%, 90.84%, and 93.40% degree of accuracy as measured on the basis of correlation coefficient between the measured and estimated values for a set of data that is excluded from training and testing phase of the model development. The developed hybrid GA-SVR-CS28E model estimates the 28-days compressive strength of AALNM using the 14-days strength, it performs better than hybrid GA-SVR-CS28C model, hybrid GA-SVR-CS28B model, hybrid GA-SVR-CS28A model, and hybrid GA-SVR-CS28D model that respectively estimates the 28-day compressive strength using three-day strength, one day-strength, all the descriptors and seven day-strength with performance improvement of 103.51%, 124.47%, 149.94%, and 262.08% on the basis of root mean square error. The outcome of this work will promote the use of environment-friendly concrete with excellent strength and provide effective as well as efficient ways of modeling the compressive strength of concrete.

Applying Support Vector Machines in Rebound Hammer Test

2013 ◽  
Vol 853 ◽  
pp. 600-604 ◽  
Author(s):  
Yu Ren Wang ◽  
Wen Ten Kuo ◽  
Shian Shien Lu ◽  
Yi Fan Shih ◽  
Shih Shian Wei
Keyword(s):  
Artificial Intelligence ◽  
Compressive Strength ◽  
Support Vector Machines ◽  
Prediction Model ◽  
Prediction Models ◽  
Percentage Error ◽  
Support Vector ◽  
Concrete Compressive Strength ◽  
Rebound Hammer ◽  
Vector Machines

There are several nondestructive testing techniques available to test the compressive strength of the concrete and the Rebound Hammer Test is among one of the fast and economical methods. Nevertheless, it is found that the prediction results from Rebound Hammer Test are not satisfying (over 20% mean absolute percentage error). In view of this, this research intends to develop a concrete compressive strength prediction model for the SilverSchmidt test hammer, using data collected from 838 lab tests. The Q-values yield from the concrete test hammer SilverSchmidt is set as the input variable and the concrete compressive strength is set as the output variable for the prediction model. For the non-linear relationships, artificial intelligence technique, Support Vector Machines (SVMs), are adopted to develop the prediction models. The results show that the mean absolute percentage errors for SVMs prediction model, 6.76%, improves a lot when comparing to SilverSchmidt predictions. It is recommended that the artificial intelligence prediction models can be applied in the SilverSchmidt tests to improve the prediction accuracy.

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