Failure mode of A-IV-WS1.5 beam. 

Failure mode of A-IV-WS1.5 beam. 

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Experimental tests were carried out to assess the failure model of steel fiber reinforced concrete beams. Experimental research was focused on observing changes in the behavior of the tested elements depending on the amount of shear reinforcement and the fiber. Model two-span beams with a cross-section of 80x180 mm and a length of 2000 mm were test...

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... The whole database comprises 488 tests of SFRC beams containing longitudinal rebar (mild steel only) and no shear reinforcement (screening rules to determine usable shear test results are detailed in [28]; they commonly include casting and curing conditions and loading and measuring methods). Almost all of the tests reported were carried out on simply supported beams under three-or four-point bending, with two exceptions: (i) two-span beams in [38] that generated a negative moment at the middle support and (ii) six short-span beams in [39] that used special setups to prevent the development of arching action. ...
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... Alnahhal et al. [34] observed that the flexural bearing capacity of the recycled aggregate BFRC beam after BMF inclusion was significantly increased compared with that of ordinary reinforced concrete. Kosior-Kazberuk et al. [35] found that the flexural toughness of concrete considerably increased when BFs were used and the addition of BFs leads to the improvement in fracture parameters. This paper presents a new composite beam, which is composed of BFRC and ordinary concrete. ...
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... This is due to the high complexity of the shear transfer mechanisms, the different types of failure and the internally interdependent forces in the beam. Kosior-Kazberuk [12] indicated that shear strength (Vc) consists of: the strength resulting from aggregate interlock, the shear strength of the concrete in the compression zone, the dowel action of the longitudinal reinforcement and residual tensile strength of concrete across the crack. The stiffness of composite rebars is much lower than that of steel reinforcement. ...
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... The influence of fibers on concrete properties was referred to the results obtained for the reference concrete without fibers. The content of both types of fibers was determined on the basis of the previous test results conducted on concrete specimens [33] and model reinforced concrete beams [34]. The analysis of the test results [33] showed a significant effect of 0.19% of basalt fibers addition on the fracture toughness of concrete, and further increase in the fiber content did not improve the properties of the concrete. ...
... The analysis of the test results [33] showed a significant effect of 0.19% of basalt fibers addition on the fracture toughness of concrete, and further increase in the fiber content did not improve the properties of the concrete. The volume fraction of steel fibers 1.0% ensured the significant effect of fibers in concrete on the shear behavior of beams and further increase in fiber content required special treatment to evenly distribute them in concrete mix [34]. Portland cement CEM I 42.5R was used to make concrete for structural elements. ...
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The presented study was conducted to assess the shear capacity and the mechanical behavior of fiber reinforced concrete two-span beams in a five-point bending test. Experimental research was focused on observing changes in the behavior of tested elements depending on the amount of shear reinforcement (stirrups) and the fiber type used. The beams had varied stirrup spacing and two sorts of fibers were used as dispersed reinforcement. The steel fiber content was 78.5 kg/m³ and the basalt fiber content was 5.0 kg/m³. Concrete beams without addition of fibers were also examined as reference ones. The effectiveness of both sorts of fibers as shear reinforcement was assessed on the basis of strain development and crack pattern analysis. The digital image correlation technique was used to monitor the development of cracks around the central support of beams. It was shown that fibers control the cracking process and deformations in reinforced concrete beams and they can be effectively used as additional or the only shear reinforcement. The results of shear capacity obtained in the experiment were also compared with the shear capacity calculated according to current design approaches. This analysis has shown that fibers enhance the ultimate shear strength of reinforced concrete beams.
... The notations used in this database are given in the "List of notations". For a number of references [42,44,50,52,54,59,67,[69][70][71][72][73][75][76][77][78]80,81,[83][84][85]88,89,94,[96][97][98][99][100][102][103][104]106,107,[109][110][111][112][115][116][117][118][119]123,124] information about the geometry of the support and loading plate was missing. These values were then approximated based on figures of the test setup in the original reference. ...
... Most specimens are rectangular beams, but the specimens in [73,81,94,99] are T-beams, in [89,90] I-beams, and in [114] non-prismatic beams. Almost all experiments are on simply supported beams in three-or four-point bending, with exception of the two-span beams in [117] and the special setup by [127] for short spans that does not allow for the development of arching action. ...
... The values from the drawings are used for the database. The ratio a v /d reported in [117] is 2.7. For the database entries, the size of the support plate measured from the technical drawings is used, and the effective depth is calculated assuming a cover of 10 mm. ...
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Adding steel fibers to concrete improves the capacity in tension-driven failure modes. An example is the shear capacity in steel fiber reinforced concrete (SFRC) beams with longitudinal reinforcement and without shear reinforcement. Since no mechanical models exist that can fully describe the behavior of SFRC beams without shear reinforcement failing in shear, a number of empirical equations have been suggested in the past. This paper compiles the existing empirical equations and code provisions for the prediction of the shear capacity of SFRC beams failing in shear as well as a database of 488 experiments reported in the literature. The experimental shear capacities from the database are then compared to the prediction equations. This comparison shows a large scatter on the ratio of experimental to predicted values. The practice of defining the tensile strength of SFRC based on different experiments internationally makes the comparison difficult. For design purposes, the code prediction methods based on the Eurocode shear expression provide reasonable results (with coefficients of variation on the ratio tested/predicted shear capacities of 27–29%). None of the currently available methods properly describe the behavior of SFRC beams failing in shear. As such, this work shows the need for studies that address the different shear-carrying mechanisms in SFRC and its crack kinematics.
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In this study, an artificial intelligence tool called gene expression programming (GEP) has been successfully applied to develop an empirical model that can predict the shear strength of steel fiber reinforced concrete beams. The proposed genetic model incorporates all the influencing parameters such as the geometric properties of the beam, the concrete compressive strength, the shear span-to-depth ratio, and the mechanical and material properties of steel fiber. Existing empirical models ignore the tensile strength of steel fibers, which exercise a strong influence on the crack propagation of concrete matrix, thereby affecting the beam shear strength. To overcome this limitation, an improved and robust empirical model is proposed herein that incorporates the fiber tensile strength along with the other influencing factors. For this purpose, an extensive experimental database subjected to four-point loading is constructed comprising results of 488 tests drawn from the literature. The data are divided based on different shapes (hooked or straight fiber) and the tensile strength of steel fiber. The empirical model is developed using this experimental database and statistically compared with previously established empirical equations. This comparison indicates that the proposed model shows significant improvement in predicting the shear strength of steel fiber reinforced concrete beams, thus substantiating the important role of fiber tensile strength.
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This paper presents a semi-empirical model to estimate the ultimate shear strength of steel fiber-reinforced concrete (SFRC) beams without shear reinforcement using the gene expression programming (GEP) technique. An extensive, reliable dataset consisting of the data of 266 SFRC beams with no stirrups was established for the development of this model. The most effective variables including the compressive strength (fc'), ratio of shear span-to-effective depth (a/d), ratio of flexural reinforcement (ρ), and fiber factor (F) were employed as input parameters in the GEP-based modeling. The accuracy of the proposed model was verified by its ability to predict a portion of data that had not been used in the training phase. Moreover, the performance of the model was evaluated using different statistical criteria. Sensitivity analysis and parametric studies were conducted on the proposed model to determine its ability to correctly consider the effect of input parameters for predicting the ultimate shear strength. In addition, the proposed model was compared with some other models proposed in the literature, and it was found that the proposed model demonstrates the best performance and accuracy among the considered shear strength prediction models.
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The incorporation of steel fibers in a concrete mix enhances the shear capacity of reinforced concrete beams and a comprehensive understanding of this phenomenon is imperative to have an accurate estimation in engineering designs. Although significant studies have been carried out on shear capacity estimation, mechanics-based models are not yet available due to the complex underlying phenomenon. This paper presents a data-driven approach to the shear strength of SFRC beams and incorporates the largest database compilation of 507 experimental data. Input features considered in this study are the ratio of shear span to effective depth, concrete compressive strength, longitudinal reinforcement ratio, volume fraction, aspect ratio, and type of fiber. Eleven machine learning (ML) models, namely linear regression, ridge regression, lasso regression, decision tree, random forest, support vector machine, k-nearest neighbors, artificial neural network, XGBoost, AdaBoost, and CatBoost, are evaluated to examine their shear strength estimation of SFRC beams. The XGBoost is resulting in the most accurate predictions (85%) with the lowest root mean squared error and low mean absolute error. A study on the importance of the input parameters reveals that shear span to effective depth ratio, longitudinal reinforcement ratio, concrete strength, and volume fraction of fiber are the most influential parameters of shear strength of SFRC.