Strength Of Steel Fiber Reinforced Concrete Research Articles

Effects of amount and geometrical properties of steel fiber on shear behavior of high-strength concrete beams without shear reinforcement

This study aims to investigate the effects of the geometrical properties and volume content of steel fibers on the shear resistance of reinforced high-strength concrete (HSC) beams without stirrups. Hooked-end and twisted steel fibers with different aspect ratios ranging from 60 to 100 were used at volume fractions of 0.5%–1.0%. The shear strength of HSC beams without stirrups significantly increased due to the addition of steel fibers and by increasing their volume fraction by up to 1.0%, resulting in approximately 1.7 times higher shear resistance. A higher fiber aspect ratio was effective in improving the resistance to shear failure of HSC beams without stirrups and in enhancing beam deformability relative to the increase in shear strength. Using twisted steel fibers led to better shear resistance than hooked-end steel fibers, resulting in 1.11- and 3.34-times higher shear strength and ductility at the same fiber volume fraction on average. The shear strength of steel fiber-reinforced concrete (SFRC) beams was also predicted using eight different models. Narayanan's model most precisely predicted the shear strengths of reinforced SFRC beams without shear reinforcement, with an average vu,exp/vu,pred ratio of 1.16. The shear strength increased with the decrease in shear span-to-depth (a/d) ratio and the increase in the fiber factor. The contribution of steel fibers to shear strength was greater for deep beams than slender beams. The predicted shear strengths of deep beams and high-strength SFRC showed a significant deviation from the actual value and required careful consideration.

A comprehensive comparison of various machine learning algorithms used for predicting the splitting tensile strength of steel fiber-reinforced concrete

Incorporating steel fibers into concrete effectively enhances its tensile strength by managing crack formation, increasing toughness and ductility, and reducing brittleness, making it a valuable choice for various applications, including bridge decks. Thus, the objective of this research is to employ the machine learning (ML) technique to estimate the splitting tensile strength (STS) of steel fiber-reinforced concrete (SFRC) that incorporates hooked industrial steel fibers (ISF) using SFRC mixtures collected from various research papers in this field. The dataset comprises 116 data points from various research papers. Utilizing the recursive feature elimination with cross-validation and leave-one-out (RFECV-LOO) method, the most influential parameters, including ISF content, water-to-cement ratio (W/C), cement content (C), fine aggregate content (FA), superplasticizer (SP), and fly ash, were identified to train the ML models. Eight statistical metrics, including the root mean squared error (RMSE), mean squared error (MSE), mean absolute error (MAE), coefficient of determination (R2), mean absolute percentage error (MAPE), mean bias error (MBE), statistical test (Tstat), and scatter index (SI) were employed as metrics to assess model performance. Various ML models including multiple linear regression (MLR), ridge regression (Ridge), lasso regression (Lasso), elastic net regression (ElasticNet), k-nearest neighbor (KNN), support vector regression (SVR), categorical boosting (CatBoost), light gradient boosting model (LightGBM), random forest (RF), gradient boosting (GB), extreme gradient boosting (XGB), adaptive boosting (AdaBoost), and multilayer perceptron (MLP) were employed to predict the SFRC’s STS. The Python programming language, TensorFlow framework as well as the Scikit-learn packages, were used to run the models. Among the various ML methods, SVR demonstrated higher accuracy, achieving an R2 of 0.930, RMSE of 0.502, and MAE of 0.330. In contrast, linear regression models, especially Lasso with an R2 of 0.631, RMSE of 0.854, and MAE of 0.710, exhibited the least effective performance when it came to predicting the STS of SFRC. Moreover, the performance of the developed ML models was approved through sensitivity analysis as well as parametric analysis.

Analysis and prediction of compressive and split-tensile strength of secondary steel fiber reinforced concrete based on RBF fuzzy neural network model.

Accurate analysis of the strength of steel-fiber-reinforced concrete (SFRC) is important for ensuring construction quality and safety. Cube compression and splitting tensile tests of steel fiber with different varieties, lengths, and dosages were performed, and the effects of different varieties, lengths, and dosages on the compressive and splitting properties of secondary concrete were obtained. It was determined that the compression and splitting strengths of concrete could be effectively improved by the addition of end-hooked and milled steel fibers. The compressive and splitting strengths of concrete can be enhanced by increasing the fiber length and content. However, concrete also exhibits obvious uncertainty owing to the comprehensive influence of steel fiber variety, fiber length, and fiber content. In order to solve this engineering uncertainty, the traditional RBF neural network is improved by using central value and weight learning strategy especially. On this basis, the RBF fuzzy neural network prediction model of the strength of secondary steel fiber-reinforced concrete was innovatively established with the type, length and content of steel fiber as input information and the compressive strength and splitting tensile strength as output information. In order to further verify the engineering reliability of the prediction model, the compressive strength and splitting tensile strength of steel fiber reinforced concrete with rock anchor beams are predicted by the prediction model. The results show that the convergence rate of the prediction model is increased by 15%, and the error between the predicted value and the measured value is less than 10%, which is more efficient and accurate than the traditional one. Additionally, the improved model algorithm is efficient and reasonable, providing technical support for the safe construction of large-volume steel fiber concrete projects, such as rock anchor beams. The fuzzy random method can also be applied to similar engineering fields.

Experimental and MCFT-Based Study on Steel Fiber-Reinforced Concrete Subjected to In-Plane Shear Forces

Testing of concrete panels subjected to pure in-plane shear loading is necessary to elucidate the shear behavior of concrete. However, available data for predicting the shear capacity and behavior of steel fiber-reinforced concrete are rather limited. This study aims to evaluate the shear capacity and behavior of fiber-reinforced concrete made of highly flowable strain hardening fiber-reinforced concrete (HF-SHFRC) experimentally and analytically, respectively, using a panel tester loaded under pure shear and modified compression field theory (MCFT). The test was conducted using a panel test machine at the University of Toronto. The test results of the HF-SHFRC demonstrated strain hardening behavior at tension after the first crack, as indicated by the increase in the shear stress after the first crack in the HF-SHFRC panel. An analysis procedure is proposed for predicting the shear strength of steel fiber-reinforced concrete (SFRC) based on experimental data of the SFRC panels to obtain reliable results. A comparison of results obtained from the proposed analysis procedure and experiments show that it accurately predicted the response of the HF-SHFRC. The proposed MCFT-based analysis procedure can provide valuable insight for understanding the behavior of the SFRC panels under shear loading.

Modeling the flexural strength of steel fibre reinforced concrete

<abstract> <p>Industrial applications of fibre-reinforced concrete (FRC) in structures require extensive experimental and analytical investigations of the FRC material properties. For design purposes and applications involving the flexural loading of the member, it is essential to have a predictive model for the flexural strength of the FRC material. In the present paper, a fracture mechanics approach based on Bridged Crack Model (BCM) is used to predict the flexural strength of steel fibre-reinforced concrete (SFRC) beams. The model assumes a quadratic tension-softening relationship (σ-δ) governing the bridging action of the steel fibres and a linear profile of the propagating crack. The proposed tension-softening relationship is considered valid for a wide range of fibre-reinforced concrete materials based on the knowledge of either the material micromechanical parameters (such as fibre volume fraction, fibre/matrix bond strength, fibre length, and fibre tensile strength) or an actual experimentally-measured σ-δ relationship. The flexural strength model thus obtained allows the prediction of the flexural strength of SFRC and study the variation of the latter as a function of the micromechanical parameters. An experimental program involving the flexural testing of 13 SFRC prism series was carried out to verify the prediction of the proposed model. The SFRC mixes incorporated two types of steel fibres (straight-end and hooked-end), four different concrete compressive strengths (40, 50, 60, and 70 MPa), three different fibre volume fractions (1, 1.5, and 2%), and three specimen depths (100, 150, and 200 mm). The experimental results were compared to the predictions of the proposed flexural strength model, and a reasonable agreement between the two has been observed. The model provided a useful physical explanation for the observed variation of flexural strength as a function of the test variables investigated in this study.</p> </abstract>

EXPERIMENTAL INVESTIGATION OF STEEL FIBERS’ EFFECT ON THE IMPROVEMENT OF MECHANICAL PROPERTIES OF CONCRETE SEGMENTAL LINING IN MECHANIZED TUNNELING

The lining system, used in tunnel boring machines, is made of precast concrete segments. The use of steel fibers in this kind of support system can not only decrease production time and costs but also can improve compressive, tensile as well as flexural behaviors. A study has been carried out to examine the mechanical properties of segments in the Tabriz metro line-2 project using three types of steel fibers: 3D, 4D, and 5D. Various experiments were investigated to monitor the workability of Steel Fiber Reinforced Concrete (SFRC) under various loads. The results confirmed an improvement in the specimens’ mechanical properties because of these types of steel fibers. 5D, 4D, and 3D fibers provided the greatest effect on the tensile and flexural strength of steel fiber-reinforced concrete, respectively. The 28-day compressive, tensile, and flexural strengths of the specimens are incremented by 13%, 68%, and 154%, respectively. In addition, by conducting tests on the orientation of steel fibers, placing the fibers perpendicular to the load can improve the compressive strength of concrete by almost 18%.

Application of steel fiber-reinforced concrete for slab lagging at underground mines in Quang Ninh

Steel Fiber-Reinforced Concrete (SFRC) is an advanced material studied and used in some developed countries in the world in recent years. The characteristics of this concrete are intensive compression, high tensile and tolerance strength, higher repeated loads, and long-term durable stability. Steel fibers were used to manufacture SFRC because of their outstanding characteristics. The durability of SFRC should be improved and the disadvantages of traditional concrete should be reduced. To produce inserts in the support structure, SFRC must be applied. Because 60÷70% of tunnels are being constructed for underground coal mines of Vietnam National Coal and Mineral Group (TKV) currently use. SPV steel frame support in combination with reinforced concrete inserts steel. To increase the plaque's capacity and durability in the future, a study must be done to create new, very intense concrete materials. This study investigated how the ratio and compressive strength of steel fibers affected the flexural characteristics of SRFC. To achieve this, 30 MPa strength SFRC with 0.5%, 1.0%, and 1.5% fiber fractions were made and evaluated. This study’s experimental results can be summarized as follows: according to the compression experimental results, the compressive strength and elastic modulus of SFRC were not considerably impacted by the steel fiber volume ratio; The experimental results from the bending tests show that for concrete with a strength of 30 MPa, the proportion of steel fibers of 1.0% has higher flexural strength and toughness than the proportion of steel fibers of 0.5% and 1.5%; A comparison of the test at 28 days of age with the (ACI 211.1-91, 1991) was completely satisfied.

Flexural Strength Prediction of Steel Fiber-Reinforced Concrete Using Artificial Intelligence.

Research has focused on creating new methodologies such as supervised machine learning algorithms that can easily calculate the mechanical properties of fiber-reinforced concrete. This research aims to forecast the flexural strength (FS) of steel fiber-reinforced concrete (SFRC) using computational approaches essential for quick and cost-effective analysis. For this purpose, the SFRC flexural data were collected from literature reviews to create a database. Three ensembled models, i.e., Gradient Boosting (GB), Random Forest (RF), and Extreme Gradient Boosting (XGB) of machine learning techniques, were considered to predict the 28-day flexural strength of steel fiber-reinforced concrete. The efficiency of each method was assessed using the coefficient of determination (R2), statistical evaluation, and k-fold cross-validation. A sensitivity approach was also used to analyze the impact of factors on predicting results. The analysis showed that the GB and RF models performed well, and the XGB approach was in the acceptable range. Gradient Boosting showed the highest precision with an R2 of 0.96, compared to Random Forest (RF) and Extreme Gradient Boosting (XGB), which had R2 values of 0.94 and 0.86, respectively. Moreover, statistical and k-fold cross-validation studies confirmed that Gradient Boosting was the best performer, followed by Random Forest (RF), based on reduced error levels. The Extreme Gradient Boosting model performance was satisfactory. These ensemble machine learning algorithms can benefit the construction sector by providing fast and better analysis of material properties, especially for fiber-reinforced concrete.

Compressive Strength of Steel Fiber-Reinforced Concrete Employing Supervised Machine Learning Techniques.

Recently, research has centered on developing new approaches, such as supervised machine learning techniques, that can compute the mechanical characteristics of materials without investing much effort, time, or money in experimentation. To predict the 28-day compressive strength of steel fiber–reinforced concrete (SFRC), machine learning techniques, i.e., individual and ensemble models, were considered. For this study, two ensemble approaches (SVR AdaBoost and SVR bagging) and one individual technique (support vector regression (SVR)) were used. Coefficient of determination (R2), statistical assessment, and k-fold cross validation were carried out to scrutinize the efficiency of each approach used. In addition, a sensitivity technique was used to assess the influence of parameters on the prediction results. It was discovered that all of the approaches used performed better in terms of forecasting the outcomes. The SVR AdaBoost method was the most precise, with R2 = 0.96, as opposed to SVR bagging and support vector regression, which had R2 values of 0.87 and 0.81, respectively. Furthermore, based on the lowered error values (MAE = 4.4 MPa, RMSE = 8 MPa), statistical and k-fold cross validation tests verified the optimum performance of SVR AdaBoost. The forecast performance of the SVR bagging models, on the other hand, was equally satisfactory. In order to predict the mechanical characteristics of other construction materials, these ensemble machine learning approaches can be applied.

An evolutionary approach for formulation of ultimate shear strength of steel fiber-reinforced concrete beams using gene expression programming

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.

Development of prediction model of steel fiber-reinforced concrete compressive strength using random forest algorithm combined with hyperparameter tuning and k-fold cross-validation

Because of the incorporation of discontinuous fibers, steel fiber-reinforced concrete (SFRC) outperforms regular concrete. However, due to its complexity and limited available data, the development of SFRC strength prediction techniques is still in its infancy when compared to that of standard concrete. In this paper, the compressive strength of steel fiber-reinforced concrete was predicted from different variables using the Random forest model. Case studies of 133 samples were used for this aim. To design and validate the models, we generated training and testing datasets. The proposed models were developed using ten important material parameters for steel fiber-reinforced concrete characterization. To minimize training and testing split bias, the approach used in this study was validated using the 10-fold Cross-Validation procedure. To determine the optimal hyperparameters for the Random Forest algorithm, the Grid Search Cross-Validation approach was utilized. The root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) between measured and estimated values were used to validate and compare the models. The prediction performance with RMSE=5.66, R2=0.88 and MAE=3.80 for the Random forest model. Compared with the traditional linear regression model, the outcomes showed that the Random forest model is able to produce enhanced predictive results of the compressive strength of steel fiber-reinforced concrete. The findings show that hyperparameter tuning with grid search and cross-validation is an efficient way to find the optimal parameters for the RF method. Also, RF produces good results and gives an alternate way for anticipating the compressive strength of SFRC

Mechanical properties of steel fiber reinforced concrete material in construction of road bridge deck

In order to improve the application effect of steel fiber reinforced concrete (SFRC) in road bridge construction, the mechanical properties of SFRC with different fiber content were analyzed. The SFRC specimens with 0%, 0.5%, 1%, 1.5% and 2% fiber content were designed, and the mechanical properties were tested. The results showed that the compressive strength first increased and then decreased with the increase of fiber content, and the maximum compressive strength of SFRC1.5 reached 40.86 MPa, increasing by 7.19%; the increase amplitude of tensile strength of SFRC1.5 was 73.04%, which was the most obvious; the flexural strength of SFRC increased with the increase of fiber content, and the flexural strength of SFRC2 was 9.78 MPa, increasing by 94.43%. It is concluded from the experimental results of a case study that SFRC1.5 presents the optimal overall mechanical properties and is more suitable for road bridge construction.

Modelling and experimental validation of flexural tensile properties of steel fiber reinforced concrete

In this paper, a four-linear model for tensile σ-ε relation is proposed to analyze and evaluate the residual bending performance of steel fiber reinforced concrete (SFRC) in flexure. The four-linear model for tension is categorized into pre-cracked stage, cracking moment, crack propagation stage and failure stage. The strength of SFRC after cracking has been fully considered at the cracking moment and crack propagation stage in this model. Using axial force and bending moment equilibrium principles, the expression of each parameter in the σ-ε relationship is established. The proposed σ-ε relation is verified experimentally. It showed that the proposed σ-ε relation fairly predicts the load–deflection behavior of SFRC prisms. Further, the proposed σ-ε relation of SFRC is optimized by a simplified analytical approach in which the value of each key point is expressed by residual flexural tensile strength, and could be applied to predict the mechanical properties of SFRC structures.

Effect of Surface Shape and Content of Steel Fiber on Mechanical Properties of Concrete

Steel fiber reinforced concrete (SFRC) has gained popularity in the last decades attributed to the improvement of brittleness and low tensile strength of concrete. This study investigates the effect of three shapes of steel fibers (straight, hooked end, and corrugated) with four contents (0.5%, 1%, 1.5%, and 2%) on the mechanical properties (compression, splitting tension, shear, and flexure) of concrete. Thirteen groups of concrete were prepared and investigated experimentally. Test results indicated that steel fiber had significant reinforcement on mechanical properties of concrete. When the steel fiber content increases from 0.5% to 2.0%, the compressive strengths increase about 4–24%, splitting tensile strengths increase about 33–122%, shear strengths increase about 31–79%, and flexural strengths increase about 25–111%. Corrugated steel fiber has the best reinforced effect on strength of SFRC, hooked end steel fiber takes the second place, and straight steel fiber is the least. Calculated formulas of compressive, splitting tensile, shear, and flexural strengths were established with consideration of the bonding properties between concrete and steel fiber. Influence factors of steel fiber αf and concrete matrix strength αc were put forward and determined by regression analysis of experimental data. Calculated results agree well with the experimental results.

Shear Strength Prediction Equations and Experimental Study of High Strength Steel Fiber-Reinforced Concrete Beams with Different Shear Span-to-Depth Ratios

Conducting research on steel fiber-reinforced concrete (SFRC) beams without stirrups, particularly the SFRC beams with high-strength concrete (HSC) and high-strength steel (HSS) reinforcing bars is essential due to the limitation of test results of high strength SFRC beams with high strength steel reinforcing bars. Eight shear strength prediction equations for analysis and design of the SFRC beam derived by different researchers are summarized. A database was constructed from 236 beams. Accordingly, the previous shear strength equations can be evaluated. Ten high-strength SFRC beams subjected to monotonic loading were prepared to verify the existing shear strength prediction equations. The equations for predicting shear strength of the SFRC beam are proposed on the basis of observations from the test results and evaluation results of the previous shear strength equations. The proposed shear strength equation possesses a reasonable result. For alternative analysis and design of the SFRC beams, ACI 318-19 shear strength equation is modified to consider steel fiber parameters.

Vibration Based Damaged Detection of Steel Fiber Reinforced concrete

Abstract In this present study, static and dynamic properties of steel fiber reinforced concrete (SFRC) were experimentally investigated. The mechanical properties have been carried out by conducting compression, split tensile and flexural strength of SFRCs. In addition, dynamic properties such as damping ratio, mode shape and natural frequency of SFRCs in free-free constraints were also determined with the addition of total fiber volume fraction as 0%, 0.5%, 0.75%, 1% and 1.25% of concrete. Static results emphasize that the addition of steel fiber to concrete significantly increases the performance of mechanical properties. Dynamic test results show that the addition of steel fibers decreases the natural frequency and increases the damping ratio. The damage was induced to concrete specimens by cutting with concrete cutter machine as a depth of 10mm, 20mm and 30mm which has been referred as D1, D2 and D3, respectively. Damping of SFRC beams increases with the increase of damage whilst the fundamental natural frequency decreases with an increase of damage. Numerical modeling was also developed for control and SFRC specimens using ANSYS software to compare the experimental results with the analytical model prediction. It is also observed that the SFRC with a fiber content of 1.25% has the best damping property.

Evaluation of flexural strength prediction of reinforced concrete beams with steel fibres

This paper presents experimental programme to investigate the effects of steel fibres on flexural strength of steel fibre-reinforced concrete (SFRC) beams and proposes a prediction model to evaluate flexural strength of beams which is their main contribution to literature. Four point bending test was carried out on the 11 SFRC beams which are 6 normal-strength SFRC beams and 5 high-strength SFRC beams. The variables studied in the investigation were steel fibre volume fractions (0, 0.75 and 1.50%), flexural reinforcement ratios (1.0 and 2.0%), and concrete compressive strengths (28 and 56 MPa). The test results confirmed that ultimate flexural strength increased with increasing fibre volume contents. The constitutive model for SFRC was proposed, which incorporated strength of fibre-reinforced concrete and strain-hardening of tensile reinforcement. Predictions of moment–curvature relationship based on the model matched well with the experimental data. The results of previously reported tests were combined with the results of new tests to document observed flexural failures of 129 SFRC beams. A simplified, empirical design equation to determine the flexural strength of SFRC beam was proposed and compared with existing equations. The analysis indicated that the proposed equation provided the most accurate estimates of flexural strength.

Compressive Strength Effects on Flexural Behavior of Steel Fiber Reinforced Concrete

This paper presents results of experimental investigation to evaluation the effects of compressive strength on flexural behavior of steel fiber-reinforced concrete (SFRC). For this purpose, normal and high strength SFRCs with two different fiber volume fractions of 0.5 and 1.0% were prepared. Compressive strength, modulus of elasticity, flexural strength and toughness were measured with tests on SFRC cylinders and prisms. Test results indicated that steel fiber volume fraction significantly affects the flexural strength and toughness of SFRC. However, the high strength SFRC showed reduction in flexural toughness compared with the normal strength SFRC. It can be concluded that flexural behavior of SFRC depends on both compressive strength and fiber volume fraction.

Shear strength prediction for steel fiber reinforced concrete beams without stirrups

This study proposes a theoretical approach based on modified compression field theory to predict the shear strength of steel fiber-reinforced concrete (SFRC) beams without stirrups. The tensile contribution of the steel fibers is considered in the tensile stress–strain constitutive equations, which take into account the distribution of the fibers. The proposed strength model is verified by comparing it with 139 shear failure tests previously conducted on SFRC and reinforced concrete beams without stirrups. The influences of concrete strength, fiber volume, shear span-to-depth ratio, and longitudinal steel ratio on predicted shear strength are also discussed. Comparisons between the predicted and experimental results show that the proposed model can estimate shear strength accurately.

Modeling shear strength of medium- to ultra-high-strength steel fiber-reinforced concrete beams using artificial neural network

This paper presents an approach for the prediction of the shear strength of steel fiber-reinforced concrete (SFRC) beams using artificial neural network (ANN) model developed based on existing experimental results. Experimental database containing 173 SFRC beams without stirrups (having concrete compressive strength ranging from 20.6 to 175 MPa, classified as medium-strength, high-strength and ultra-high-strength concrete beams) and steel fiber of various shapes (hooked, crimped and straight/plain) is used to develop an ANN model. The influence of various factors affecting the shear strength of SFRC beams is also analyzed based on experimental data. The developed ANN model is validated and tested with additional data from 36 experimental beams. The shear strengths predicted based on ANN model are found to be in good agreement with the experimental values. The results show that ANN model has strong potential as a feasible design tool for predicting the shear strength of SFRC beams without transverse reinforcement/stirrups within the range of input parameters considered in this study. The developed ANN model is also found to be more accurate in predicting the shear strength compared with existing empirical equations especially for high- to ultra-high-strength SFRC beams.