Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars
The performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and strain) should be determined in order to evaluate t...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/12/10/4851 |
| _version_ | 1797501941237940224 |
|---|---|
| author | Hamed Dabiri Visar Farhangi Mohammad Javad Moradi Mehdi Zadehmohamad Moses Karakouzian |
| author_facet | Hamed Dabiri Visar Farhangi Mohammad Javad Moradi Mehdi Zadehmohamad Moses Karakouzian |
| author_sort | Hamed Dabiri |
| collection | DOAJ |
| description | The performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and strain) should be determined in order to evaluate their reliability prior to the construction procedure. In this study, the application of Tree-Based machine learning techniques is implemented to analyze the ultimate strain of non-spliced and spliced steel reinforcements. In this regard, a database containing the results of 225 experimental tests was collected based on the research investigations available in peer-reviewed international publications. The database included the mechanical properties of both non-spliced and mechanically spliced bars. For better accuracy, the databases of other splicing methods such as lap and welded-spliced methods were excluded from this research. The database was categorized as two sub-databases: training (85%) and testing (15%) of the developed models. Various effective parameters such as splice technique, steel grade of the bar, diameter of the steel bar, coupler geometry—including length and outer diameter along with the testing temperatures—were defined as the input variables for analyzing the ultimate strain using tree-based approaches including Decision Trees and Random Forest. The predicted outcomes were compared to the actual values and the precision of the prediction models was assessed via performance metrics, along with a Taylor diagram. Based on the reported results, the reliability of the proposed ML-based methods was acceptable (with an R<sup>2</sup> ≥ 85%) and they were time-saving and cost-effective compared to more complicated, time-consuming, and expensive experimental examinations. More importantly, the models proposed in this study can be further considered as a part of a comprehensive prediction model for estimating the stress-strain behavior of steel bars. |
| first_indexed | 2024-03-10T03:25:47Z |
| format | Article |
| id | doaj.art-d3085e1d666a4c648d4b3400abb31a5b |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T03:25:47Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d3085e1d666a4c648d4b3400abb31a5b2023-11-23T09:54:09ZengMDPI AGApplied Sciences2076-34172022-05-011210485110.3390/app12104851Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement BarsHamed Dabiri0Visar Farhangi1Mohammad Javad Moradi2Mehdi Zadehmohamad3Moses Karakouzian4School of Science and Technology, University of Camerino, 62032 Camerino, ItalyDepartment of Civil Engineering, Construction Management, and Environmental Engineering, Northern Arizona University, Flagstaff, AZ 86011, USADepartment of Civil and Environmental Engineering, Carleton University, Ottawa, ON K1S 5B6, CanadaDepartment of Civil and Environmental Engineering, Louisiana State University, Baton Rouge, LA 70803, USADepartment of Civil and Environmental Engineering and Construction, University of Nevada, Las Vegas, NV 89154, USAThe performance of both non-spliced and spliced steel bars significantly affects the overall performance of structural reinforced concrete elements. In this context, the mechanical properties of reinforcement bars (i.e., their ultimate strength and strain) should be determined in order to evaluate their reliability prior to the construction procedure. In this study, the application of Tree-Based machine learning techniques is implemented to analyze the ultimate strain of non-spliced and spliced steel reinforcements. In this regard, a database containing the results of 225 experimental tests was collected based on the research investigations available in peer-reviewed international publications. The database included the mechanical properties of both non-spliced and mechanically spliced bars. For better accuracy, the databases of other splicing methods such as lap and welded-spliced methods were excluded from this research. The database was categorized as two sub-databases: training (85%) and testing (15%) of the developed models. Various effective parameters such as splice technique, steel grade of the bar, diameter of the steel bar, coupler geometry—including length and outer diameter along with the testing temperatures—were defined as the input variables for analyzing the ultimate strain using tree-based approaches including Decision Trees and Random Forest. The predicted outcomes were compared to the actual values and the precision of the prediction models was assessed via performance metrics, along with a Taylor diagram. Based on the reported results, the reliability of the proposed ML-based methods was acceptable (with an R<sup>2</sup> ≥ 85%) and they were time-saving and cost-effective compared to more complicated, time-consuming, and expensive experimental examinations. More importantly, the models proposed in this study can be further considered as a part of a comprehensive prediction model for estimating the stress-strain behavior of steel bars.https://www.mdpi.com/2076-3417/12/10/4851machine learningreinforcement systemspliced barultimate strainDecision TreeRandom Forest |
| spellingShingle | Hamed Dabiri Visar Farhangi Mohammad Javad Moradi Mehdi Zadehmohamad Moses Karakouzian Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars Applied Sciences machine learning reinforcement system spliced bar ultimate strain Decision Tree Random Forest |
| title | Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars |
| title_full | Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars |
| title_fullStr | Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars |
| title_full_unstemmed | Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars |
| title_short | Applications of Decision Tree and Random Forest as Tree-Based Machine Learning Techniques for Analyzing the Ultimate Strain of Spliced and Non-Spliced Reinforcement Bars |
| title_sort | applications of decision tree and random forest as tree based machine learning techniques for analyzing the ultimate strain of spliced and non spliced reinforcement bars |
| topic | machine learning reinforcement system spliced bar ultimate strain Decision Tree Random Forest |
| url | https://www.mdpi.com/2076-3417/12/10/4851 |
| work_keys_str_mv | AT hameddabiri applicationsofdecisiontreeandrandomforestastreebasedmachinelearningtechniquesforanalyzingtheultimatestrainofsplicedandnonsplicedreinforcementbars AT visarfarhangi applicationsofdecisiontreeandrandomforestastreebasedmachinelearningtechniquesforanalyzingtheultimatestrainofsplicedandnonsplicedreinforcementbars AT mohammadjavadmoradi applicationsofdecisiontreeandrandomforestastreebasedmachinelearningtechniquesforanalyzingtheultimatestrainofsplicedandnonsplicedreinforcementbars AT mehdizadehmohamad applicationsofdecisiontreeandrandomforestastreebasedmachinelearningtechniquesforanalyzingtheultimatestrainofsplicedandnonsplicedreinforcementbars AT moseskarakouzian applicationsofdecisiontreeandrandomforestastreebasedmachinelearningtechniquesforanalyzingtheultimatestrainofsplicedandnonsplicedreinforcementbars |