Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence
Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational...
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| Format: | Article |
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MDPI AG
2022-07-01
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| Series: | Materials |
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| Online Access: | https://www.mdpi.com/1996-1944/15/15/5194 |
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| author | Dong Zheng Rongxing Wu Muhammad Sufian Nabil Ben Kahla Miniar Atig Ahmed Farouk Deifalla Oussama Accouche Marc Azab |
| author_facet | Dong Zheng Rongxing Wu Muhammad Sufian Nabil Ben Kahla Miniar Atig Ahmed Farouk Deifalla Oussama Accouche Marc Azab |
| author_sort | Dong Zheng |
| collection | DOAJ |
| description | Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R<sup>2</sup>), statistical evaluation, and k-fold cross-validation. A sensitivity approach was also used to analyze the impact of factors on predicting results. The analysis showed that the GB and RF models performed well, and the XGB approach was in the acceptable range. Gradient Boosting showed the highest precision with an R<sup>2</sup> of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R<sup>2</sup> values of 0.94 and 0.86, respectively. Moreover, statistical and k-fold cross-validation studies confirmed that Gradient Boosting was the best performer, followed by Random Forest (RF), based on reduced error levels. The Extreme Gradient Boosting model performance was satisfactory. These ensemble machine learning algorithms can benefit the construction sector by providing fast and better analysis of material properties, especially for fiber-reinforced concrete. |
| first_indexed | 2024-03-09T12:25:43Z |
| format | Article |
| id | doaj.art-5c0aabcf88e249038de2f1d1e2414177 |
| institution | Directory Open Access Journal |
| issn | 1996-1944 |
| language | English |
| last_indexed | 2024-03-09T12:25:43Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Materials |
| spelling | doaj.art-5c0aabcf88e249038de2f1d1e24141772023-11-30T22:35:21ZengMDPI AGMaterials1996-19442022-07-011515519410.3390/ma15155194Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial IntelligenceDong Zheng0Rongxing Wu1Muhammad Sufian2Nabil Ben Kahla3Miniar Atig4Ahmed Farouk Deifalla5Oussama Accouche6Marc Azab7School of Architectural Engineering, Ningbo Polytechnic, Ningbo 315800, ChinaSchool of Architectural Engineering, Ningbo Polytechnic, Ningbo 315800, ChinaSchool of Civil Engineering, Southeast University, Nanjing 210096, ChinaDepartment of Civil Engineering, College of Engineering, King Khalid University, Abha 61421, Saudi ArabiaLaboratory of Systems and Applied Mechanics, Tunisia Polytechnic School, University of Carthage, La Marsa, Tunis 2078, TunisiaStructural Engineering and Construction Management Department, Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, EgyptCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitResearch has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R<sup>2</sup>), statistical evaluation, and k-fold cross-validation. A sensitivity approach was also used to analyze the impact of factors on predicting results. The analysis showed that the GB and RF models performed well, and the XGB approach was in the acceptable range. Gradient Boosting showed the highest precision with an R<sup>2</sup> of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R<sup>2</sup> values of 0.94 and 0.86, respectively. Moreover, statistical and k-fold cross-validation studies confirmed that Gradient Boosting was the best performer, followed by Random Forest (RF), based on reduced error levels. The Extreme Gradient Boosting model performance was satisfactory. These ensemble machine learning algorithms can benefit the construction sector by providing fast and better analysis of material properties, especially for fiber-reinforced concrete.https://www.mdpi.com/1996-1944/15/15/5194concretesteel fibersteel fiber-reinforced concreteflexural strengthmechanical characteristicsconstruction materials |
| spellingShingle | Dong Zheng Rongxing Wu Muhammad Sufian Nabil Ben Kahla Miniar Atig Ahmed Farouk Deifalla Oussama Accouche Marc Azab Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence Materials concrete steel fiber steel fiber-reinforced concrete flexural strength mechanical characteristics construction materials |
| title | Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence |
| title_full | Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence |
| title_fullStr | Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence |
| title_full_unstemmed | Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence |
| title_short | Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence |
| title_sort | flexural strength prediction of steel fiber reinforced concrete using artificial intelligence |
| topic | concrete steel fiber steel fiber-reinforced concrete flexural strength mechanical characteristics construction materials |
| url | https://www.mdpi.com/1996-1944/15/15/5194 |
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