Failure analysis of punching in reinforced concrete flat slabs with openings adjacent to the column

2019 ◽  
Vol 182 ◽  
pp. 331-343 ◽  
Author(s):  
Elyson A.P. Liberati ◽  
Marília G. Marques ◽  
Edson D. Leonel ◽  
Luiz C. Almeida ◽  
Leandro M. Trautwein
Keyword(s):  
Reinforced Concrete ◽  
Failure Analysis ◽  
Flat Slabs

scholarly journals Punching strength of reinforced concrete flat slabs without shear reinforcement

2012 ◽  
Vol 5 (5) ◽  
pp. 659-691 ◽  
Author(s):  
P. V. P. Sacramento ◽  
M. P. Ferreira ◽  
D. R. C. Oliveira ◽  
G. S. S. A. Melo
Keyword(s):  
Reinforced Concrete ◽  
Shear Crack ◽  
Theoretical Method ◽  
Shear Reinforcement ◽  
Model Code ◽  
Flat Slabs ◽  
Codes Of Practice ◽  
Eurocode 2 ◽  
Model Code 2010 ◽  
Theoretical Results

Punching strength is a critical point in the design of flat slabs and due to the lack of a theoretical method capable of explaining this phenomenon, empirical formulations presented by codes of practice are still the most used method to check the bearing capacity of slab-column connections. This paper discusses relevant aspects of the development of flat slabs, the factors that influence the punching resistance of slabs without shear reinforcement and makes comparisons between the experimental results organized in a database with 74 slabs carefully selected with theoretical results using the recommendations of ACI 318, EUROCODE 2 and NBR 6118 and also through the Critical Shear Crack Theory, presented by Muttoni (2008) and incorporated the new fib Model Code (2010).

scholarly journals Strength of reinforced concrete flat slabs for punching

2020 ◽  
Vol 890 ◽  
pp. 012076
Author(s):  
Mirsayapov Ilshat ◽  
Nikitin Georgy ◽  
Khanbekov Midkhat
Keyword(s):  
Reinforced Concrete ◽  
Flat 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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