Unconfined Compressive Strength Prediction from Petrophysical Properties and Elemental Spectroscopy Using Support-Vector Regression

2017 ◽  
Author(s):  
Ardiansyah Negara ◽  
Syed Ali ◽  
Ali AlDhamen ◽  
Hasan Kesserwan ◽  
Guodong Jin
Keyword(s):  
Compressive Strength ◽  
Support Vector Regression ◽  
Unconfined Compressive Strength ◽  
Strength Prediction ◽  
Support Vector ◽  
Petrophysical Properties

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 Application of Artificial Intelligence to Determined Unconfined Compressive Strength of Cement-Stabilized Soil in Vietnam

2021 ◽  
Vol 11 (4) ◽  
pp. 1949
Author(s):  
Huong Thi Thanh Ngo ◽  
Tuan Anh Pham ◽  
Huong Lan Thi Vu ◽  
Loi Van Giap
Keyword(s):  
Compressive Strength ◽  
Unconfined Compressive Strength ◽  
Performance Indicator ◽  
Training Data ◽  
Superior Performance ◽  
Gradient Boosting ◽  
Support Vector ◽  
Ann Model ◽  
Data Set ◽  
Stabilized Soil

Cement stabilized soil is one of the commonly used as ground reinforcement solutions in geotechnical engineering. In this study, the main object was to apply three machine learning (ML) methods namely gradient boosting (GB), artificial neural network (ANN) and support vector machine (SVM) to predict unconfined compressive strength (UCS) of cement stabilized soil. Soil samples were collected at Hai Duong city, Vietnam. A total of 216 soil–cement samples were mixed in the laboratory and compressed to determine the UCS. This data set is divided into two parts of the training data set (80%) and testing set (20%) to build and test the model, respectively. To verify the performance of ML model, various criteria named correlation coefficient (R), mean absolute error (MAE) and root mean square error (RMSE) were used. The results show that all three ML models were effective methods to predict the UCS of cement-stabilized soil. Amongst three model used in this study, optimized ANN model provided superior performance compare to two others models with performance indicator R = 0.925, RMSE = 419.82 and MAE = 292.2 for testing part. This study can provide an effective tool to quickly predict the UCS of cement stabilized soil with high accuracy.

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