Punching Shear Strength of Steel Fiber Reinforced Concrete Flat Plates

10.14359/4145 ◽  
1994 ◽  
Vol 91 (4) ◽  
Keyword(s):  
Shear Strength ◽  
Reinforced Concrete ◽  
Steel Fiber ◽  
Fiber Reinforced Concrete ◽  
Punching Shear ◽  
Fiber Reinforced ◽  
Steel Fiber Reinforced Concrete ◽  
Flat Plates

scholarly journals Consideration on punching shear strength of steel-fiber-reinforced concrete slabs

2015 ◽  
Vol 7 (5) ◽  
pp. 168781401558425 ◽  
Author(s):  
Hyunjin Ju ◽  
Na-Rae Cheon ◽  
Deuck Hang Lee ◽  
Jae-Yuel Oh ◽  
Jin-Ha Hwang ◽  
...  
Keyword(s):  
Shear Strength ◽  
Reinforced Concrete ◽  
Steel Fiber ◽  
Fiber Reinforced Concrete ◽  
Punching Shear ◽  
Concrete Slabs ◽  
Fiber Reinforced ◽  
Steel Fiber Reinforced Concrete ◽  
Reinforced Concrete Slabs

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

Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3902 ◽  
Author(s):  
Shasha Lu ◽  
Mohammadreza Koopialipoor ◽  
Panagiotis G. Asteris ◽  
Maziyar Bahri ◽  
Danial Jahed Armaghani
Keyword(s):  
Feature Selection ◽  
Reinforced Concrete ◽  
Predictive Models ◽  
Steel Fiber ◽  
Shear Capacity ◽  
Fiber Reinforced Concrete ◽  
Punching Shear ◽  
Fiber Reinforced ◽  
Steel Fiber Reinforced Concrete ◽  
Flat Slabs

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.

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