scholarly journals Probabilistic Studies on the Shear Strength of Slender Steel Fiber Reinforced Concrete Structures

2020 ◽  
Vol 10 (19) ◽  
pp. 6955
Author(s):  
Oladimeji B. Olalusi ◽  
Panagiotis Spyridis
Keyword(s):  
Reinforced Concrete ◽  
Steel Fiber ◽  
Concrete Structures ◽  
Shear Resistance ◽  
Shear Capacity ◽  
Fiber Reinforced Concrete ◽  
Reinforced Concrete Structures ◽  
Fiber Reinforced ◽  
Steel Fiber Reinforced Concrete ◽  
Model Uncertainties

Shear failure is a brittle and undesirable mode of failure in reinforced concrete structures. Many of the existing shear design equations for steel fiber reinforced concrete (SFRC) beams include significant uncertainty due to the failure in accurately predicting the true shear capacity. Given these, adequate quantification and description of model uncertainties considering the systematic variation in the model prediction and measured shear capacity is crucial for reliability-based investigation. Reliability analysis must account for model uncertainties in order to predict the probability of failure under prescribed limit states. This study focuses on the quantification and description of model uncertainty related to the current shear resistance predictive models for SFRC beams without shear reinforcement. The German (DAfStB) model displayed the lowest bias and dispersion, whereas the fib Model 2010 and the Bernat et al., model displayed the highest bias and dispersion. The inconsistencies observed in the resistance model uncertainties at the variation of shear span to effective depth ratio are a major cause for concern, and differentiation with respect to this parameter is advised. Finally, in line with the EN 1990 semi-probabilistic approach for reliability-based design, the global partial safety factors related to model uncertainties in the shear resistance prediction of SFRC beams are proposed.

scholarly journals Steel fiber concrete. Constructions. Terminology

2020 ◽  
Vol 12 (4) ◽  
Author(s):  
Klara Talantova
Keyword(s):  
Reinforced Concrete ◽  
Composite Materials ◽  
Steel Fiber ◽  
Concrete Structures ◽  
Fiber Reinforced Concrete ◽  
Reinforced Concrete Structures ◽  
Fiber Reinforced ◽  
Steel Fiber Reinforced Concrete ◽  
Steel Fiber Concrete ◽  
Composite Steel

The article discusses the terms and definitions that are increasingly appearing in the open press and on the Internet, confusing well-established concepts. The purpose of considering terminology issues is an attempt to draw the attention of the community of builders – scientists and practitioners to the inadmissibility of inaccuracy and incorrect choice of terms and definitions in the technical, scientific and even regulatory literature. The article discusses the transformation (changes) of terminology in the field of dense concretes, reinforced concrete structures, as well as structures based on a building composite – steel fiber reinforced concrete. In particular, concrete is a composite, reinforced concrete is not a collective name for reinforced concrete structures and products, but material, moreover, is composite. At the same time, steel fiber concrete is a hardened concrete. This approach is an important reason for restraining widespread use in the practice of building structures based on steel fiber reinforced concrete. These terms are often used in the open press, depriving the discussed topics of physical meaning, and, as a result, the impossibility of obtaining the expected result. At the same time, the open press provides information on composite materials, the peculiarities of creating and managing their properties. The article presents the established generally accepted terms and definitions that correspond to the physical nature of concrete, reinforced concrete structures, composite materials and structures based on composite – steel fiber reinforced concrete. The definition of composite materials, the principles of their creation, as well as the features of the composite – steel fiber reinforced concrete and methods of forming its structure are presented. A method is shown for obtaining the properties of steel fiber reinforced concrete in accordance with the stress-strain state of the developed structural element and creating structures based on it with properties specified in accordance with the conditions of their operation and having technical and economic indicators that exceed those of standard structures – analogues.

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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