Support Vector Machine Modelling for the Compressive Strength of Concrete

2012 ◽  
Author(s):  
A. Sriraam ◽  
S.K. Sekar ◽  
P. Samui
Keyword(s):  
Support Vector Machine ◽  
Compressive Strength ◽  
Support Vector ◽  
Compressive Strength Of Concrete

scholarly journals Prediction of Splitting Tensile Strength from Cylinder Compressive Strength of Concrete by Support Vector Machine

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Kezhen Yan ◽  
Hongbing Xu ◽  
Guanghui Shen ◽  
Pei Liu
Keyword(s):  
Experimental Data ◽  
Support Vector Machine ◽  
Tensile Strength ◽  
Compressive Strength ◽  
Splitting Tensile Strength ◽  
Support Vector ◽  
Svm Model ◽  
Concrete Mechanical Properties ◽  
Compressive Strength Of Concrete ◽  
Good Agreement

Compressive strength and splitting tensile strength are both important parameters that are utilized for characterization concrete mechanical properties. This paper aims to show a possible applicability of support vector machine (SVM) to predict the splitting tensile strength of concrete from compressive strength of concrete, a SVM model was built, trained, and tested using the available experimental data gathered from the literature. All of the results predicted by the SVM model are compared with results obtained from experimental data, and we found that the predicted splitting tensile strength of concrete is in good agreement with the experimental data. The splitting tensile strength results predicted by SVM are also compared to those obtained by using empirical results of the building codes and various models. These comparisons show that SVM has strong potential as a feasible tool for predicting splitting tensile strength from compressive strength.

scholarly journals Effect of Lithium-Slag in the Performance of Slag Cement Mortar Based on Least-Squares Support Vector Machine Prediction

Materials ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1652 ◽  
Author(s):  
Jianghu Lu ◽  
Zhexuan Yu ◽  
Yuanzhe Zhu ◽  
Shaowen Huang ◽  
Qi Luo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Compressive Strength ◽  
Least Squares ◽  
Cement Mortar ◽  
Chloride Ion ◽  
Industrial Wastes ◽  
Support Vector ◽  
Ion Permeability ◽  
Property Changes ◽  
Lithium Slag

There is a universally accepted view that environmental pollution should be controlled while improving cement mortar natural abilities. The purpose of this study is to develop a green cement mortar that has better compressive strength and anti-chloride ion permeability. Two industrial wastes, lithium-slag and slag, were added to cement mortar, and the role of lithium-slag was to activate slag. In addition, to save economic and time costs, this paper also used the least-squares support vector machine (LS-SVM) method to predict the property changes of cementitious-based materials. Then multiple natural abilities of samples, including compressive strength, anti-chloride ion permeability, and fluidity, were tested. In addition, LS-SVM and traditional support vector machine (SVM) were used to train and forecast the performance, including compressive strength. The results show that lithium-slag can activate slag to improve the compressive strength, anti-chloride ion permeability of mortar, and LS-SVM sharpens accuracy by 11% compared to SVM.

scholarly journals Using Support Vector Machine to Improve Ultrasonic Pulse Velocity Test for Concrete

2018 ◽  
Vol 207 ◽  
pp. 01001
Author(s):  
Tu Quynh Loan Ngo ◽  
Yu-Ren Wang
Keyword(s):  
Compressive Strength ◽  
Ultrasonic Pulse Velocity ◽  
Ultrasonic Pulse ◽  
Pulse Velocity ◽  
Support Vector ◽  
Non Destructive Testing ◽  
Testing Methods ◽  
Destructive Testing ◽  
Compressive Strength Of Concrete ◽  
Non Destructive

In the construction industry, to evaluate the compressive strength of concrete, destructive and non-destructive testing methods are used. Non-destructive testing methods are preferable due to the fact that those methods do not destroy concrete samples. However, they usually give larger percentage of error than using destructive tests. Among the non-destructive testing methods, the ultrasonic pulse velocity test is the popular one because it is economic and very simple in operation. Using the ultrasonic pulse velocity test gives 20% MAPE more than using destructive tests. This paper aims to improve the ultrasonic pulse velocity test results in estimating the compressive strength of concrete using the help of artificial intelligent. To establish a better prediction model for the ultrasonic pulse velocity test, data collected from 312 cylinder of concrete samples are used to develop and validate the model. The research results provide valuable information when using the ultrasonic pulse velocity tests to the inputs data in addition with support vector machine by learning algorithms, and the actual compressive strengths are set as the target output data to train the model. The results show that both MAPEs for the linear and nonlinear regression models are 11.17% and 17.66% respectively. The MAPE for the support vector machine models is 11.02%. These research results can provide valuable information when using the ultrasonic pulse velocity test to estimate the compressive strength of concrete.

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.

Modelling Concrete Strength Using Support Vector Machines

2013 ◽  
Vol 438-439 ◽  
pp. 170-173 ◽  
Author(s):  
Hai Ying Yang ◽  
Yi Feng Dong
Keyword(s):  
Compressive Strength ◽  
Support Vector Machines ◽  
Concrete Strength ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Data Sets ◽  
Risk Minimization ◽  
Minimization Principle ◽  
Vector Machines ◽  
Compressive Strength Of Concrete

Support vector machine (SVM) is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. A SVM model is presented to predict compressive strength of concrete at 28 days in this paper. A total of 20 data sets were used to train, whereas the remaining 10 data sets were used to test the created model. Radial basis function based on support vector machines was used to model the compressive strength and results were compared with a generalized regression neural network approach. The results of this study showed that the SVM approach has the potential to be a practical tool for predicting compressive strength of concrete at 28 days.

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