Comparative study of Bs8110 and Eurocode 2 standards for design of a factory reinforced concrete flat slab

2020 ◽  
Author(s):  
Fatimah De’nan ◽  
Md Azlin Md Said ◽  
Chong Hong Rui ◽  
Izwan Johari



2021 ◽  
Vol 239 ◽  
pp. 112292
Author(s):  
Stanislav Aidarov ◽  
Francisco Mena ◽  
Albert de la Fuente




2016 ◽  
Vol 38 (2) ◽  
pp. 37-46 ◽  
Author(s):  
Mateusz Kaczmarek ◽  
Agnieszka Szymańska

Abstract Nonlinear structural mechanics should be taken into account in the practical design of reinforced concrete structures. Cracking is one of the major sources of nonlinearity. Description of deflection of reinforced concrete elements is a computational problem, mainly because of the difficulties in modelling the nonlinear stress-strain relationship of concrete and steel. In design practise, in accordance with technical rules (e.g., Eurocode 2), a simplified approach for reinforced concrete is used, but the results of simplified calculations differ from the results of experimental studies. Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. This paper describes the creation and operation of a neural network for making predictions of deflections of reinforced concrete beams at different load levels. In order to obtain a database of results, that is necessary for training and testing the neural network, a research on measurement of deflections in reinforced concrete beams was conducted by the authors in the Certified Research Laboratory of the Building Engineering Institute at Wrocław University of Science and Technology. The use of artificial neural networks is an innovation and an alternative to traditional methods of solving the problem of calculating the deflections of reinforced concrete elements. The results show the effectiveness of using artificial neural network for predicting the deflection of reinforced concrete beams, compared with the results of calculations conducted in accordance with Eurocode 2. The neural network model presented in this paper can acquire new data and be used for further analysis, with availability of more research results.



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

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).



1914 ◽  
Vol 77 (1) ◽  
pp. 1682-1730
Author(s):  
L. J. Mensch ◽  
C. A. P. Turner ◽  
Edward Godfrey ◽  
H. T. Eddy ◽  
A. W. Buel ◽  
...  


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