Arching Action in Steel Fiber-Reinforced Concrete Flat Slabs

2018 ◽  
Vol 115 (6) ◽  
Author(s):  
Akshay Venkateshwaran ◽  
Kiang Hwee Tan
Materials ◽  
2020 ◽  
Vol 13 (17) ◽  
pp. 3902 ◽  
Author(s):  
Shasha Lu ◽  
Mohammadreza Koopialipoor ◽  
Panagiotis G. Asteris ◽  
Maziyar Bahri ◽  
Danial Jahed Armaghani

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.


2017 ◽  
Vol 59 (7-8) ◽  
pp. 653-660 ◽  
Author(s):  
Wang Yan ◽  
Ge Lu ◽  
Chen Shi Jie ◽  
Zhou Li ◽  
Zhang Ting Ting

2021 ◽  
pp. 136943322098165
Author(s):  
Hossein Saberi ◽  
Farzad Hatami ◽  
Alireza Rahai

In this study, the co-effects of steel fibers and FRP confinement on the concrete behavior under the axial compression load are investigated. Thus, the experimental tests were conducted on 18 steel fiber-reinforced concrete (SFRC) specimens confined by FRP. Moreover, 24 existing experimental test results of FRP-confined specimens tested under axial compression are gathered to compile a reliable database for developing a mathematical model. In the conducted experimental tests, the concrete strength was varied as 26 MPa and 32.5 MPa and the steel fiber content was varied as 0.0%, 1.5%, and 3%. The specimens were confined with one and two layers of glass fiber reinforced polymer (GFRP) sheet. The experimental test results show that simultaneously using the steel fibers and FRP confinement in concrete not only significantly increases the peak strength and ultimate strain of concrete but also solves the issue of sudden failure in the FRP-confined concrete. The simulations confirm that the results of the proposed model are in good agreement with those of experimental tests.


1984 ◽  
Vol 21 (3) ◽  
pp. 108-111
Author(s):  
V. S. Sterin ◽  
V. A. Golubenkov ◽  
G. S. Rodov ◽  
B. V. Leikin ◽  
L. G. Kurbatov

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