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Open AccessArticle

A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs

1
Civil Engineering College, Liaoning Technical University, Fuxin 123000, China
2
Faculty of Civil and Environmental Engineering, Amirkabir University of Technology, Tehran 15914, Iran
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Computational Mechanics Laboratory, School of Pedagogical and Technological Education, 14121 Heraklion, Greece
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Department of Building Structures and Geotechnical Engineering, Higher Technical School of Architecture, Universidad de Sevilla, 41012 Sevilla, Spain
5
Institute of Research and Development, Duy Tan University, Da Nang 550000, Vietnam
*
Authors to whom correspondence should be addressed.
Materials 2020, 13(17), 3902; https://doi.org/10.3390/ma13173902
Received: 2 August 2020 / Revised: 26 August 2020 / Accepted: 31 August 2020 / Published: 3 September 2020
When designing flat slabs made of steel fiber-reinforced concrete (SFRC), it is very important to predict their punching shear capacity accurately. The use of machine learning seems to be a great way to improve the accuracy of empirical equations currently used in this field. Accordingly, this study utilized tree predictive models (i.e., random forest (RF), random tree (RT), and classification and regression trees (CART)) as well as a novel feature selection (FS) technique to introduce a new model capable of estimating the punching shear capacity of the SFRC flat slabs. Furthermore, to automatically create the structure of the predictive models, the current study employed a sequential algorithm of the FS model. In order to perform the training stage for the proposed models, a dataset consisting of 140 samples with six influential components (i.e., the depth of the slab, the effective depth of the slab, the length of the column, the compressive strength of the concrete, the reinforcement ratio, and the fiber volume) were collected from the relevant literature. Afterward, the sequential FS models were trained and verified using the above-mentioned database. To evaluate the accuracy of the proposed models for both testing and training datasets, various statistical indices, including the coefficient of determination (R2) and root mean square error (RMSE), were utilized. The results obtained from the experiments indicated that the FS-RT model outperformed FS-RF and FS-CART models in terms of prediction accuracy. The range of R2 and RMSE values were obtained as 0.9476–0.9831 and 14.4965–24.9310, respectively; in this regard, the FS-RT hybrid technique demonstrated the best performance. It was concluded that the three hybrid techniques proposed in this paper, i.e., FS-RT, FS-RF, and FS-CART, could be applied to predicting SFRC flat slabs. View Full-Text
Keywords: fiber-reinforced concrete; punching shear capacity; tree model; feature selection; hybrid predictive models fiber-reinforced concrete; punching shear capacity; tree model; feature selection; hybrid predictive models
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MDPI and ACS Style

Lu, S.; Koopialipoor, M.; Asteris, P.G.; Bahri, M.; Armaghani, D.J. A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs. Materials 2020, 13, 3902.

AMA Style

Lu S, Koopialipoor M, Asteris PG, Bahri M, Armaghani DJ. A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs. Materials. 2020; 13(17):3902.

Chicago/Turabian Style

Lu, Shasha; Koopialipoor, Mohammadreza; Asteris, Panagiotis G.; Bahri, Maziyar; Armaghani, Danial J. 2020. "A Novel Feature Selection Approach Based on Tree Models for Evaluating the Punching Shear Capacity of Steel Fiber-Reinforced Concrete Flat Slabs" Materials 13, no. 17: 3902.

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