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Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models
by
Mansouri, Iman
, Kisi, Ozgur
, Gholampour, Aliakbar
, Ozbakkaloglu, Togay
in
Admixtures
/ Artificial Intelligence
/ Compressive strength
/ Computational Biology/Bioinformatics
/ Computational Science and Engineering
/ Computer Science
/ Concrete aggregates
/ Data Mining and Knowledge Discovery
/ Flexural strength
/ Image Processing and Computer Vision
/ Least squares
/ Mechanical properties
/ Model accuracy
/ Modulus of elasticity
/ Multivariate analysis
/ Original Article
/ Probability and Statistics in Computer Science
/ Recycled materials
/ Regression models
/ Splines
/ Splitting
/ Support vector machines
/ Tensile strength
2020
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Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models
by
Mansouri, Iman
, Kisi, Ozgur
, Gholampour, Aliakbar
, Ozbakkaloglu, Togay
in
Admixtures
/ Artificial Intelligence
/ Compressive strength
/ Computational Biology/Bioinformatics
/ Computational Science and Engineering
/ Computer Science
/ Concrete aggregates
/ Data Mining and Knowledge Discovery
/ Flexural strength
/ Image Processing and Computer Vision
/ Least squares
/ Mechanical properties
/ Model accuracy
/ Modulus of elasticity
/ Multivariate analysis
/ Original Article
/ Probability and Statistics in Computer Science
/ Recycled materials
/ Regression models
/ Splines
/ Splitting
/ Support vector machines
/ Tensile strength
2020
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Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models
by
Mansouri, Iman
, Kisi, Ozgur
, Gholampour, Aliakbar
, Ozbakkaloglu, Togay
in
Admixtures
/ Artificial Intelligence
/ Compressive strength
/ Computational Biology/Bioinformatics
/ Computational Science and Engineering
/ Computer Science
/ Concrete aggregates
/ Data Mining and Knowledge Discovery
/ Flexural strength
/ Image Processing and Computer Vision
/ Least squares
/ Mechanical properties
/ Model accuracy
/ Modulus of elasticity
/ Multivariate analysis
/ Original Article
/ Probability and Statistics in Computer Science
/ Recycled materials
/ Regression models
/ Splines
/ Splitting
/ Support vector machines
/ Tensile strength
2020
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Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models
Journal Article
Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models
2020
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Overview
This paper investigates the application of three artificial intelligence methods, including multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) for the prediction of the mechanical behavior of recycled aggregate concrete (RAC). A large and reliable experimental test database containing the results of 650 compressive strength, 421 elastic modulus, 152 flexural strength, and 346 splitting tensile strength tests of RACs with no pozzolanic admixtures assembled from the published literature was used to train, test, and validate the three data-driven-based models. The results of the model assessment show that the LSSVR model provides improved accuracy over the existing models in the prediction of the compressive strength of RACs. The results also indicate that, although all three models provide higher accuracy than the existing models in the prediction of the splitting tensile strength of RACs, only the performance of the LSSVR model exceeds those of the best-performing existing models for the flexural strength of RACs. The results of this study indicate that MARS, M5Tree, and LSSVR models can provide close predictions of the mechanical properties of RACs by accurately capturing the influences of the key parameters. This points to the possibility of the application of these three models in the pre-design and modeling of structures manufactured with RACs.
Publisher
Springer London,Springer Nature B.V
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