Steel Fiber Reinforced Concrete Research Articles

Assessing sampling interval-dependent roughness in fractured steel fiber reinforced concrete using a double-exponential decay model

This study introduces a double-exponential decay (DED) model to investigate the sampling interval (δ)-dependent behavior of crack morphology roughness in steel fiber reinforced concrete (SFRC). The root mean square (Z2-3D) of the directional derivatives in 3D digital morphology is employed to quantify the roughness and its anisotropic behavior. Surface morphologies of 24 fractured specimens are captured by using a laser scanner and the obtained results are re-meshed based on the Point-Kriging algorithm. The roughness is represented by the ratio of surface area to projected area (RS) and Z2-3D values along different directions at various δs (4–3000 μm). The outcomes suggest that the values of RS and Z2-3D both decline, but the anisotropy of surface roughness is enhanced at an increased δ. The DED model is able to properly reflect the decay behavior in which the morphology roughness is classified into three corrugated components based on their distinct attenuation laws. A satisfactory agreement with the literature data is observed, validating the presented methodology. The reported findings in this work are useful for enhancing the understanding of the formation of crack morphology roughness and its relationship to the damage process of SFRC.

Predicting the fire-induced structural performance of steel tube columns filled with SFRC-enhanced concrete: using artificial neural networks approach

Predicting the axial Shortening strength of concrete-filled steel tubular (CFST) columns is an important problem that this study attempts to solve for civil engineering projects. We suggest using a deep learning-based artificial neural network (ANN) model to address this issue, taking into account the intricate relationship between steel tube and core concrete. The model, called ANN-SFRC (Steel Fibre Reinforced Concrete), surpasses an R2 threshold of 0.90 and achieves impressive R2 values across different types of CFST columns. Compared to traditional linear regression methods, the ANN-SFRC model significantly improves accuracy, with an observed inaccuracy of less than 3% compared to actual values. With its reliable approach to forecasting the behavior of CFST columns under axial compression, this high-performance instrument enhances safety and accuracy during the design and planning stages of civil engineering.

Low-velocity impact behavior of two-way SFRC slabs strengthened with steel plate

Structural systems and structural elements can often suffer severe damage or even completely collapse under the effect of sudden dynamic impact loading, which is a different type of loading that is not considered during their design. Research on how structures behave under impact loading and how they can be strengthened to perform better against this type of loading has increased to avoid such undesirable severe damage. Within the scope of this study, it is aimed to improve the behavior and increase the performance of two-way steel fiber-reinforced concrete (SFRC) slabs, one of the leading structural elements that can be affected by impact loading, using steel fiber concrete (SFC) and placing steel plates on the surface of the RC slab. Within the scope of the study, the effects of placing FRC as layers in different positions within the slab and placing the steel plate on different surfaces of the slabs were examined. Impact loading was applied using a drop weight test setup designed by the authors, and the acceleration–time, displacement–time, and impact loading–time behaviors of the RC slabs were measured and interpreted. The use of fiber concrete in RC slabs and strengthened with steel plates increased the maximum acceleration values by an average of 3% and 113%, respectively. The use of fiber concrete in RC slabs reduced the maximum displacement and residual displacement values by an average of 2% and 25%, respectively. Placing steel plates on the slabs reduced the maximum displacement and residual displacement values by an average of 270% and 199%, respectively. In addition, the energy absorption capacities of RC slabs were calculated, and how they were affected by experimental variables was examined. Numerical analyses of the RC slabs tested in the study were also conducted using ABAQUS finite element software, and the results obtained were compared with the experimental ones.

Characterisation proposal of direct shear strength of steel fibre-reinforced concrete

Characterisation proposal of direct shear strength of steel fibre-reinforced concrete

Design and flexural behavior of steel fiber-reinforced concrete beams with regular oriented fibers and GFRP bars

In this study, steel fiber-reinforced concrete (SFRC) beam structures with regular oriented steel fibers are designed and prepared by using an assembled solenoid device. Fiber orientation is not arranged in a single direction but is similar to the direction of tensile stress of the beam in bending load conditions. With application of inductive test method and image processing technology, fiber orientation coefficient of SFRC was evaluated and increased from 0.5 to 0.7–0.8 after fiber alignment. To describe and analyze the flexural behavior of SFRC beams with regular oriented fibers and glass fiber-reinforced polymer (GFRP) rebars, 14 concrete beams with different parameters were fabricated and subjected to a four-point bending test. Specifically, the parameters included fiber content, fiber orientation, GFRP reinforcement ratio and different layer sections. The results showed that optimizing fiber orientations to the tension zone of the section led to a higher flexural strength and can well restrain the development of crack widths with comparison to randomly distributed fibers. The peak load and energy absorption increased by 7 % and 12.4 % after fiber alignment with a fiber volume fraction of 1.5 %. The combination of randomly and regularly distributed steel fibers in the form of two-layer composite members can be an efficient solution to improve the flexural performance for purpose of the optimum distribution and direction of steel fibers, and reduce the engineering cost.

Failure characteristics and seismic behavior of steel basalt hybrid fiber reinforced concrete lining for the tunnel in strong earthquake areas

The effect of a high-intensity earthquake on a tunnel can be catastrophic. To minimize the damage of the earthquake to the tunnel, the mechanical properties of tunnel lining need to be improved in strong earthquake areas. Thereby, this paper proposes a steel basalt hybrid fiber reinforced concrete (SBHFRC) as an alternative construction material for the lining. Then, the failure characteristics and seismic performance of SBHFRC are studied and compared with that of plain concrete (PC) and steel fiber reinforced concrete (SFRC). The results show that the toughness of SFRC is better than that of SBHFRC. Furthermore, the initial crack load and failure load of SBHFRC lining are significantly higher than that of PC lining. The numbers of cracks observed in the SBHFRC lining and SFRC lining are more than that in the PC lining. But the distribution of cracks in SBHFRC lining is relatively uniform. The bearing capacity of SFRC lining and SBHFRC lining is basically the same, although significantly higher than that of PC lining. The seismic performance of different fiber concrete linings is further explored through numerical simulation. Under strong earthquakes, the damage areas of the lining are mainly concentrated at the tunnel vault bottom and vault near the fault, and the SBHFRC damage degree and range are the smallest. Compared with PC lining, the minimum safety factors of SFRC lining and SBHFRC lining are increased by 7.5 times and 11.2 times, respectively, which shows that SBHFRC lining has good seismic performance.

Effect of steel fibre with different orientations on mechanical properties of 3D-printed steel-fibre reinforced concrete: Mesoscale finite element analysis

It is widely recognized that steel fibres exhibit directional distribution during the preparation of 3D-printed steel fibre reinforced concrete (SFRC). The degree of fibre orientation varies due to several factors, such as nozzle size and layer height. This variation in fibre orientation range may result in different mechanical performance exhibited by 3D-printed SFRC at hardened state. To explore the relationship between steel fibre orientation and the mechanical properties of 3D-printed SFRC at hardened state, this study firstly establishes three-dimensional mesoscale finite element models based on the distribution of fibres in 3D-printed SFRC. The steel fibre and matrix work together through a mechanism for fluid-structure interaction between models. Then, compares the simulation results with experimental data to verify the accuracy of the models. After that, different steel fibre distribution orientations were set, and parametric analysis was conducted for the compressive and tensile loading conditions to explore the effects of fibre orientation on the damage mode and strength of 3D-printed SFRC. The results indicate that 3D-printed SFRC exhibits different damage modes with varying steel fibre orientation ranges. Additionally, the strength of 3D-printed SFRC varies in steel fibre orientation range and exhibits anisotropic characteristics. This study provides a theoretical basis for improving and controlling the performance of 3D-printed SFRC in future engineering applications.

A Novel Repair Technique of Pre-damaged T-Beams Failing in Shear Using Eco-Friendly Steel Fibre-Reinforced Geopolymer Concrete

AbstractRepair of reinforced concrete structures is required to preserve the adequate performance of these structures throughout their service life. One of the credible techniques is using fibrous concrete as a repair material. In this paper, the performance of steel fibre-reinforced geopolymer concrete (SFRGPC) in the repair of pre-damaged reinforced concrete T-beams (pre-loaded up to 50% of their shear capacity) failing in shear was investigated. Five T-beam series and a four-point loading test were adopted: one reference beam, three beams were repaired with different fibrous ratios of 1, 2, and 3%, and one was repaired with 2% steel fibre and additional U-steel stirrups. The key test results include crack propagation, crack width, initial stiffness, load deflection, peak loads, and strain associated with web stirrups. A clear enhancement was noticed in the performance of the repaired T-beams; their shear capacity was boosted by as much as 45% compared to the control beam. It was also deduced that the beam went from a brittle to a ductile failure mode at 3% SFRGPC and at 2% SFRGPC with U-stirrups. Finally, an analytical model prediction was proposed to predict the shear capacity of repaired T-beams with the SFRGPC. The model showed a satisfactory correlation with experimental results, with an average ratio of 0.995 and a standard deviation of 0.035.

Experimental investigation on axial compressive and splitting tensile behaviour of reinforced concrete with a low content of hybrid steel fibres

This study intends to explore how the hybrid steel fibre reinforcement with a low content affects the mechanical characteristics of concrete. The axial compression properties of hybrid steel fibre-reinforced concrete (HSRC) were systematically investigated regarding stress-strain curves, failure modes, elastic modulus, and toughness. The crack propagation and fracture process of HSRC were analyzed based on the cumulative crack width and horizontal strain field. The results showed that the micro-straight steel fibre (mSF) demonstrated a more pronounced influence on post-peak stress retention in the axial compression test, whereas macro hooked-end steel fibre (MHF) positively affected the peak strain enhancement. The average peak stress of the HSRC increased by 8.6 % higher than that of H40 and 16.4 % higher than that of S40. It is noteworthy that the mixing of steel fibres at a low fibre content was detrimental to the compressive toughness. The splitting tensile test showed a transitional stage before the HSRC reached its peak load, during which the load steadily increased and crack propagation slowly occurred. Moreover, the digital image correlation (DIC) measurement results suggested that mSF can enhance the internal quality of concrete. The study contributes to a deep understanding of the synergistic reinforcement mechanism and the fracture processes in HSRC.

A numerical critical shear crack model and its application to post‐peak behavior assessment of RC and SFRC beams

AbstractIn this paper, a numerical critical shear crack model, in which the shear‐flexural coupling effect is considered and the full process of stress and strain is captured, is proposed to evaluate the post‐peak behavior of reinforced concrete (RC) and steel fiber reinforced concrete (SFRC) beams. The shape of critical shear crack is identified by combining the modified compression field theory considering the bridge effect of steel fibers and the section analysis method. Based on the shape of critical shear crack, the shear capacity of beam members is provided by the shear tension zone and the shear compression zone. The shear capacity of the shear tension zone is calculated by the modified compression field theory and that of the shear compression zone is determined by multiaxial strength criterion of concrete. The vertical displacements caused by the flexure deformation and shear deformation are deduced by the moment area method and integration of shear strain. To verify the proposed numerical approach, a test database of 486 RC beams and 313 SFRC beams was established to predict the shear strength, and the force‐displacement relationships of twelve beam members are used to validate the feasibility for full process analysis. The stochastic analysis of beams with different failure modes is conducted via GF‐discrepancy‐based point selection method and probability density evolution method. The limitation between different failure modes is defined according to the degradation percent of shear capacity and it is taken as threshold value of failure domain, and the failure probability analysis indicates that the designed flexure beam member suffered severe degradation of shear capacity, resulting in a significant decline in safety probability.

Design and economic implications of using steel fibers in elevated slabs of multi‐story buildings

AbstractThis paper presents a comprehensive comparison between conventional reinforced concrete (RC) and steel fiber‐reinforced concrete (SFRC) slabs. Twenty‐two‐way rectangular RC and SFRC slabs are designed with varying dimensions, aspect ratios, and boundary conditions, following verified international standards to compare the steel quantities in a unique study. SFRC slabs with larger aspect ratios and discontinuous edges required significantly higher steel percentages than square slabs, as the random three‐dimensional distribution of steel fibers suited the load distribution pattern of square slabs. To further conduct a detailed design and economic feasibility case study on a realistic building model, two 12‐story buildings are computationally designed with RC and SFRC alternatives. The results indicate that SFRC slabs required approximately 25% more steel than RC slabs due to the uniform distribution of steel fibers throughout the section. Project cost and time estimates were conducted based on standard productivity manuals and cost databases in two distinct economies, India and the USA. Despite the higher steel requirements, the 12‐story SFRC building showed time savings of 34.1 days (5.44%) and cost savings of 4.88% and 1.27% when constructed in the USA and India, respectively. The results are compared with limited available data in literature. In conclusion, the study suggests that SFRC slabs in multi‐story buildings could be more economically favorable in countries with higher labor costs.

Application of metaheuristic algorithms for compressive strength prediction of steel fiber reinforced concrete exposed to high temperatures

The demand for steel fiber-reinforced concrete (SFRC) in construction has surged, particularly due to its enhanced fire resistance, leading to extensive research on its residual properties after elevated temperature exposure. However, conducting experimental tests can be a time-consuming process, demanding significant resources in terms of time, cost, and human resources. In contrast, machine learning (ML) models can rapidly simulate outcomes, enabling researchers to explore a wide range of scenarios efficiently. The previous literature studies used either ensemble or individual models; however, this study made a unique approach to utilize hybrid models, which are more precise compared to the other types of ML models. Accordingly, a data-rich framework containing 304 data samples was utilized in this study to develop an efficient and robust predictive model for the compressive strength (CS) of SFRC at higher temperatures. Support vector regression (SVR) in combination with three distinct optimization algorithms, specifically, particle swarm optimization (PSO), the firefly algorithm (FFA), and grey wolf optimization (GWO) were utilized to develop the hybrid models. In addition, typical ML techniques such as random forest (RF) and decision tree (DT) were used for comparative analysis. Among the five models, the SVR-FFA hybrid model showcased superior predictive accuracy, with the SVR-GWO model following closely. The correlation coefficients (R) for both models exceeded 0.99. The SVR-FFA model demonstrated relative root mean square error (RMSE) and mean absolute error (MAE) values of 0.0375 and 0.0248, respectively, while the SVR-GWO model exhibited corresponding values of 6.0560 and 5.1326. The SHapley Additive exPlanation (SHAP) technique revealed that the influence of temperature is more significant compared to the heating rate. Moreover, a considerable enhancement was observed in CS as the steel fiber volume fraction (Vf) reached 1.5%; on the other hand, no further improvement was noticed when Vf exceeded the 1.5% threshold. The proposed hybrid models are robust and accurate methods to estimate the CS of SFRC in field applications.

Structural Behavior of Circular Concrete Columns Reinforced with Longitudinal GFRP Rebars under Axial Load

This paper presents experimental and theoretical assessments of the structural behavior of circular steel fiber-reinforced concrete (SFRC) columns reinforced with glass fiber-reinforced polymer (GFRP) bars subjected to a concentric axial compressive load. Laboratory experiments were planned to evaluate and compare the effect of different design parameters on the structural behavior of column specimens based on experiments and finite element (FE) analysis. The experimental variables were (i) concrete types, i.e., conventional concrete (CC) and fiber-reinforced concrete (FC), (ii) longitudinal reinforcement types, i.e., steel and GFRP bars, and (iii) transverse rebar configurations, i.e., tied and spiral with different pitches. Sixteen column specimens were fabricated and categorized into four groups with respect to rebar configurations and concrete types. The results showed that the failure modes and cracking patterns of those four column groups were comparable, particularly in the pre-peak branches of load-deflection curves. Even though the average ultimate load of the columns with longitudinal GFRP bars was 17.9% less than that with longitudinal steel bars, the ductility index (DI) was 10.2% greater than their counterpart on average. The addition of steel fibers (SF) to concrete increased the axial peak load by up to 3.1% and the DI by up to 6.6% compared to their counterpart CC columns without SFs. The DI of specimens was increased by higher volumetric ratios (up to 12%) and spiral types (up to 5.5%). The concrete damage plastic (CDP) model for FC columns was updated in the finite element software ABAQUS 6.14. Finally, a new simple equation was theoretically proposed to predict the axial capacity of specimens by considering the inclusion of longitudinal GFRP rebars, volumetric ratio, and steel spiral/hoop ties. Good agreement between the proposed model predictions and the experimental/numerical results was observed.

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.

Experimental Study on the Mechanical Properties of Recycled Spiral Steel Fiber-Reinforced Rubber Concrete

Recycled rubber (RR) and recycled spiral steel fiber (RSSF) were added to plain concrete (PC) to prepare recycled spiral steel fiber rubber concrete (SSFRC) with matrix strengths of C30, C40, and C50. Strength tests on the PC, rubber concrete (RC), and SSFRC were carried out, including the cube compressive strength, splitting tensile strength, and flexural strength. The effects of RSSF and RR on the mechanical properties of concrete were analyzed. Simultaneously, the stress–strain curve of the SSFRC was obtained through axial compressive testing, and the toughness of SSFRC was evaluated by three indexes: the tensile compression ratio, bending compression ratio, and toughness index. The results show that adding RR to PC results in a decrease in the mechanical properties of concrete with different matrix strengths, and the addition of RSSF can make up for the strength loss of the rubber. The mechanical strength of SSFRC with different matrix strengths increased first and then decreased with the increase in RSSF content. The cubic compressive strength reached its peak value when the content of RSSF was 1%, and the splitting tensile strength and flexural strength reach their peak values when the content of RSSF was 1.5%. RSSF works best with rubber particles at the right dosage to further increase the toughness of the concrete. When the rubber content is 10%, and the RSSF content is 1.5%, the mechanical strength enhancement effect of SSFRC is at its best, and the toughness is also at its best.

Selected Mechanical Properties of Concrete with Regard to the Type of Steel Fibers

Not only in the construction industry, but also in other technical areas, efforts are being made to reduce the costs or difficulty of producing a certain product and at the same time to improve some of its properties. With the development of modern technologies come new possibilities in the development and production of such products. Fiber reinforced concrete is one of them. Fiber reinforced concrete does not fully replace reinforced concrete, but even with a reduction in price and production time, it has a certain part of the properties of reinforced concrete, which can be used for structural elements with specifically required properties. The subject of the presented paper is the testing and comparison of compressive and split tensile strength of steel fiber reinforced concrete (SFRC) with steel fibers MasterFiber 482 with dosage 60 kg/m3 and 90 kg/m3 and SFRC with 5 different types of steel fibers with dosage 50 kg/m3, where dosage 0 kg/m3 represents ordinary portland concrete (OPC). Submitted paper is also focused on specific test methods of concrete, such as measurement of resistance to frost and defrosting chemicals and pressure water seepage.

Numerical investigation of shear behavior of steel fiber reinforced concrete beams without stirrups subjected to an asymmetric point load

Steel fibers have been shown in numerous studies to enhance the shear performance of steel fiber reinforced concrete (SFRC) beams effectively, and to evaluate the shear behavior of these beams, a nonlinear finite element (NLFE) analysis is required. In the present study, from recent experimental results of asymmetrical loading tests on SFRC beams without stirrups, three NLFE models have been validated and further developed to evaluate the impact that the ratio of shear span-to-depth (a/d) and the compressive strength of steel fiber concrete (SFC) have on the shear behavior and shear strength of SFRC beams without stirrups. The validation shows a good agreement between the chosen NLFE model and the reported experimental results. Applying the NLFE model to five different values of the a/d ratio (e.g., 1.0, 1.5, 2.0, 2.54, and 3.1) reveals a gradual decline in shear strength, while the ductility of the beam increases with the increase in ratio. Meanwhile, with increasing SFC compressive strength, the shear strength and ductility of SFRC beams without stirrups significantly increase for the same ratio of shear span-to-depth. Moreover, the failure mode of SFRC beams without stirrups dominantly depends on the a/d ratio.

A modeling strategy for the shear and flexural performance prediction of SFRC beams without stirrups accounting for the variability of properties

AbstractThe behavior of steel fiber reinforced concrete (SFRC) is highly dependent on mix design, where the properties of its components, such as cementitious matrix and fibers, impact the physical and mechanical properties of the composite. Incorporating steel fibers into concrete modifies its physical properties, potentially leading to fiber segregation and resulting in a nonuniform distribution of the fibers within the material. In experimental studies, an increase in fiber volume has been found to change the failure mode of SFRC beams from shear to flexure, and numerical investigations have shown that randomness in fiber distribution can significantly influence the behavior of the composite. Therefore, this study aims to develop a numerical model that considers variations in material properties to simulate the change in the failure mode of SFRC beams due to variations in fiber volume. The failure and yielding parameters were initially established and integrated into the concrete damaged plasticity (CDP). Subsequently, the response of both steel and concrete to tension or compression stresses was ascertained. Finally, the appropriate meshes and finite elements were chosen for the simulation. In the final approach, two methods were employed to introduce randomness into the beam. One divided the beam into different vertical segments: 30, 45, and 90, and the other combined vertical and horizontal segments: 30 vertical with 4 horizontal, 45 vertical with 5 horizontal, and 90 vertical with 10 horizontal. A script was developed in Python to automatically insert the properties. The numerical model successfully captured the change in failure mode from shear to flexure resulting from fiber volume increase. Vertical and horizontal segments increased toughness due to crack deviation, better predicting cracking pattern and loading capacity for the highest fiber contents.

A novel mesoscale modelling method for steel fibre-reinforced concrete with the combined finite-discrete element method

This paper proposes a novel mesoscale modelling method to study the effect of steel fibres on the fracture properties of steel fibre reinforced concrete (SFRC) based on an integrated finite-discrete element method (FDEM) framework. The present method models the steel fibre of SFRC as multiple bar elements, while simulating the cement matrix using triangle elements. A comprehensive bond-slip model is also introduced between the triangle element and bar element, enabling accurate reproduction of the interaction behaviour between the concrete matrix and steel fibre. Additionally, the progressive failure process of cohesive elements in FDEM is considered as crack propagation, confirming the superiority of FDEM from continuum to discontinuum. The results of the Brazilian split test and uniaxial compression test validate the capability of the FDEM framework for mode Ι and mode II fractures, while single fibre pull-out tests and 3-point-bending beam tests provide validation of the effectiveness of the integrated FDEM framework for the mesoscale modelling of SFRC. The discussion also covers employing the mesoscale modelling method to investigate the effect of steel fibre on the fracture properties of SFRC. Owing to its merits in quantitative analysis, the present method can serve as a feasible potential tool for mesoscale modelling analysis and SFRC design.

MIXED MODE FRACTURE OF THE GEOPOLYMER COMPOSITES REINFORCED WITH RECYCLED STEEL FIBERS

For the fiber-reinforced composites, strength-based criteria alone may fail to evaluate the bending response due to the long tail of the load-displacement curve. Hence, the fracture characterization of fibered composites has gained great attention worldwide. In this study, the mixed-mode fracture performance of the recycled steel fiber-reinforced geopolymer concrete was examined experimentally. The main test parameters were the amount of steel fibers (0 and 2% by mass) and the offset ratios of the edge notch (β = 0, 0.2, and 0.4). Several notched prisms were produced and tested under a deformation-controlled three-point bending test. Deformation maps on the surface of the specimens were derived through the digital image correlation method. Experimental results were discussed concerning the first cracking load, ultimate load, critical crack mouth opening displacement, critical crack mouth sliding displacement, and fracture energy. Based on the experimental findings, it can be stated that the peak flexural loads were increased by 666%, 1327%, and 400%, respectively for the 0, 0.2, and 0.4 notch offset ratios due to the use of recycled steel fiber. The fracture energies of the plain specimens were proportional to the notch offset ratio, but they fluctuated for the fiber-reinforced specimens because of the uneven distribution of fibers.