Prediction of the Axial Compressive Strength of Circular Concrete-Filled Steel Tube Columns using Sine Cosine Algorithm-Support Vector Regression

2021 ◽  
pp. 114282
Author(s):  
Fei Lyu ◽  
Xinyu Fan ◽  
Faxing Ding ◽  
Zhiwen Chen
Keyword(s):  
Compressive Strength ◽  
Support Vector Regression ◽  
Steel Tube ◽  
Support Vector ◽  
Concrete Filled Steel Tube ◽  
Axial Compressive Strength ◽  
Sine Cosine Algorithm

Experimental Study on Compressive Bearing Capacity of High Strength Concrete-Filled Steel Tube Composite Columns

2011 ◽  
Vol 368-373 ◽  
pp. 410-414 ◽  
Author(s):  
Hong Zhen Kang ◽  
Lei Yao ◽  
Xi Min Song ◽  
Ying Hua Ye
Keyword(s):  
Compressive Strength ◽  
Reinforced Concrete ◽  
High Strength Concrete ◽  
High Strength ◽  
Steel Tube ◽  
Test Results ◽  
Concrete Filled Steel Tube ◽  
Composite Columns ◽  
Strength Concrete ◽  
Axial Compressive Strength

To study axial compressive strength of high strength concrete-filled steel tube composite columns, tests of 18 specimens were carried out. Parameters of the specimens were the confinement index of concrete-filled steel tube, the cubic strength and the stirrup characteristic value of concrete outer of steel tube. Test results show that the concrete-filled steel tube and the reinforced concrete deformed simultaneously in the axial direction before and at the peak value of axial compressive force; after failure of the reinforced concrete, the concrete-filled steel tube can still bear the axial load and deformation; the main influential factors of axial compressive capacity are confinement index, the cubic strength and the stirrup characteristic value of concrete outer of steel tube. The accuracy of the formula of axial compressive strength of composite columns provided by CECS 188:2005 is proved by the test results of this paper.

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 Response of RPC-Filled Circular Steel Tube Columns under Monotonic and Cyclic Axial Loading

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Qin Rong ◽  
Yusheng Zeng ◽  
Lanhui Guo ◽  
Xiaomeng Hou ◽  
Wenzhong Zheng
Keyword(s):  
Compressive Strength ◽  
Cyclic Loading ◽  
Axial Loading ◽  
Steel Tube ◽  
Test Results ◽  
Confining Effect ◽  
Circular Steel Tube ◽  
Axial Compressive Strength ◽  
Reactive Powder ◽  
Elastic Stage

Results from mechanical tests on thirteen reactive powder concrete- (RPC-) filled circular steel tube (RFCT) columns under monotonic and cyclic axial loading are presented in this paper. The test variables include monotonic and cyclic loadings, confinement coefficient, and diameter of the steel tube. The test results show that the envelope curves of specimens under cyclic loading were similar to the load-deformation curves of the specimens under monotonic loading. Confinement coefficient had a significant influence on the failure modes of RFCT columns. With an increase in confinement coefficient of 0.53 to 0.98, the failure mode transformed from shear failure to compressive failure for specimens under monotonic and cyclic loading. In the elastic stage, no confining effect was provided by the steel tube to the RPC since Poisson’s ratio of steel was larger than the transverse deformation coefficient of RPC. Beyond the elastic stage, the axial compressive strength and ultimate strain of RPC increased significantly due to the confining effect when compared to unconfined RPC. Stress of the steel tube and RPC was investigated by using an elastic-plastic analytical model. Before yielding of the steel tube, stress development in the tube was faster in the longitudinal direction than in the hoop direction. The results of the experiment indicate that the compressive strength of RPC could be predicted by Mander’s model for confined concrete. Based on Mander’s model, an equation is extended to calculate the axial compressive strength of RFCT columns, and the predicted results are in good agreement with the test results. Based on comparative analysis of 180 RFCT columns axial compressive tests, the equation given by EC4 considering the confinement effect can be applied to predict the compressive strength of RFCT columns.

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.

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