Steel Fiber Reinforced Concrete Research Articles

Microscopic Modeling Techniques for Steel Fiber Reinforced Concrete: Applications and Future Directions

This article reviews micro-modeling methods for steel fiber-reinforced concrete (SFRC), including finite element, discrete element, and computational fluid dynamics approaches, to analyze its mechanical and structural behavior. The models offer predictive insights on stress response, crack propagation, and durability in SFRC, particularly in high-stress structural applications. This study discusses the limitations of current modeling practices, such as high computational demand and parameter determination challenges, and suggests future improvements to enhance simulation accuracy and scalability. This review aims to support the advancement of SFRC in structural engineering applications.

A cohesive fracture-enhanced phase-field approach for modeling the damage behavior of steel fiber-reinforced concrete

This study introduces a novel computational framework based on the phase-field fracture/damage model (PFM), providing rapid and accurate predictions of the damage behavior of Steel Fiber Reinforced Concrete (SFRC) material. The framework integrates the interfacial cohesive fracture model with the PFM to depict the interaction between the cementitious matrix and fibers, marking the first demonstration that this model adequately captures both local fracture phenomena between fibers and concrete (such as fiber bridging and interfacial debonding) and mesoscopic stress–strain behavior. Additionally, an enhanced geometric approximation is established based on the spatial distribution density function of fibers and particles, allowing for the transformation of the three-dimensions (3-D) fiber-reinforced material structure into an equivalent two-dimensions (2-D) material. This approach enables the reduction of computational time, a significant limitation of the phase-field method, thereby allowing an in-house code to perform various computations with complex material structures such as SFRC. The effectiveness of the approach is demonstrated through four examples involving variations in the microgeometry and material properties of SFRC structures, ranging from the simplest configurations to real experimental material structures. Extensive Monte Carlo simulations of the model are also employed to provide results closer to reality, which are then compared with recent experimental data and numerical models, demonstrating the efficacy of the proposed computational model.

Experimental study on acoustic emission of steel fiber broken pebble recycled concrete

This study investigates the mechanical properties and damage evolution of steel fiber-reinforced recycled concrete, using broken pebbles as coarse aggregates. The objective is to assess the compressive and flexural performance of the material under varying steel fiber content (0 %, 1 %, 2 %) and recycled aggregate replacement rates (0 %, 10 %, 20 %, 30 %, 40 %). The research employs Acoustic Emission (AE) technology to monitor real-time damage during compression and bending tests. The key findings reveal that increasing the steel fiber volume and aggregate replacement rate improves the interface bonding performance and enhances flexural strength, while also leading to higher initial damage. The AE signals captured during testing allowed the identification of critical damage stages, providing insight into crack initiation and propagation. This research offers valuable contributions to the understanding of recycled concrete behavior, with implications for sustainable construction and improved material design.

Optimized punching shear design in steel fiber-reinforced slabs: Machine learning vs. evolutionary prediction models

This research paper focuses on utilizing Artificial Neural Networks (ANN), Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR), and Gene Expression Programming (GEP) to predict the punching shear strength of Steel Fibre-Reinforced Concrete (SFRC) slabs.In order to formulate predictions, research and analysis were carried out making use of a dataset, this dataset included several parameters that impact on punching shear strength, including SFRC slabs longitudinally and transversely, using ANN, GEP, and MOGA-EPR methods. The developed models exhibited very good performance, as the soft computing techniques (GEP and MOGA-EPR) achieved R² values of 0.91 to 0.93, while the ANN technique was higher at 0.95. Furthermore, two case studies were incorporated to carry out cost analyses of the models in real-world applications. It was shown that the efficiency of the Machine Learning (ML) models in reducing the costs of materials is relatively high, as they were capable of better predictions than the standard methods employed by the codes.

Application of high-strength steel fiber-reinforced concrete to reduce the use of conventional rebars in normal-strength reinforced concrete beams

This is an experimental study whose main objective was to evaluate the use of high-strength steel fiber-reinforced concrete (HSSFRC) to reduce the use of conventional reinforcements, longitudinal/transverse steel bars in normal-strength reinforced concrete beams (NSRC). To enable a detailed discussion on the theme, five prototype beams (150x300x2700 mm3) were built and load tested until failure. One of the beams contained no fibers and was used as control. It had a strength class of 30.0 MPa, and longitudinal reinforcement ratio, ρl,t, and transverse reinforcement ratio, ρw, of 0.91 and 0.11%, respectively, which are the values commonly used in practical applications. In addition to the control beam, four other HSSFRC beams, with a strength class of 60.0 MPa, were built with ρl,t = 0.39% (reduction of ≈ 60.0%, in comparison to the control beam), and ρw = 0.0% (reduction of 100.0%); fiber consumption ranged between 45.0, 60.0, 75.0 and 90.0 kg/m3. The behavior of the beams was interpreted on the basis of the load-displacement and moment-curvature relationships. These results, and the responses of the material characterization tests, showed the feasibility of designing HSSFRC beams with undoubtedly successful reductions, and mechanical behavior which was equivalent, in some cases, to that of NSRC beams.

Surrogated modelling for structural reliability and safety assessments of high-strength concrete beams with industrial and recycled steel fibers

As a secondary material recycled from tyres, Recycled Steel Fibre Reinforced Concrete (RSF) aims to replace industrial steel fibres (ISF) to improve concrete matrix‘s severability, fracture toughness, and residual tensile stress. Despite the increasing attention on the mechanical properties of this material, RSF concrete’s influence on structural component strength is still neglected due to a lack of sufficient and reliable experimental data. This study looks into existing Crack mouth opening displacement (CMOD) experiment results of both types of steel fibre reinforced concrete beams together with nonlinear finite element model (FEM) simulations using the RILEM TC 162-TDF crack damage model(CDP) proposed for ISF in ABAQUS. Meanwhile, an additional parameter analysis of the experimental data related to shear capacity also examines the shear span ratio, fibre content, and fibre type of the two types of fibre reinforced concrete beams. Moreover, Kriging surrogate models (KSM) and Sobol global sensitivity analysis have been conducted. For the reference concrete beams with a fibre content of 0.4 %, the prediction accuracy ratios of ultimate strength, corresponding displacement, and stiffness between the FEM and the experiment results are 98 %, 88 %, and 100 %, respectively. This paper has been proved that the CMOD-based CDP model effectively elucidates the shear contribution of different steel fibres, which can be used in structural analysis of ISF or RSF concrete for structure components. Utilizing FEMs, a series of modified flexural strength prediction formulas based on TR63 standards are proposed to enhance structural applications with ISF and RSF. Moreover, the prediction ability of the proposed formulas is validated using the results of 183 real flexural capacity experiments of ISF reinforced concrete beams, achieving an improved R² value of 0.97.

Characterisation of fibre distribution uniformity in steel fibre-reinforced concrete under microwave-induced heating during casting

Traditional active microwave thermography (AMT) tests for hardened steel fibre-reinforced concrete (SFRC). The surface temperature of SFRC derived from AMT cannot fully characterise the distribution of steel fibres in the SFRC. This study proposes a microwave-induced heating method combined with temperature sensors (MI-TS) to estimate the fibre dispersion of an SFRC mixture during casting. The principles and testing process are discussed, and a numerical model is developed to evaluate the temperature distribution of the SFRC mixture following microwave exposure. Through MI-TS and numerical models, the temperature in the SFRC mixture with uniform fibre distribution, fibre clustering and fibre sinking is investigated, and the results are compared with those obtained by AMT and destruction technology to evaluate the feasibility of the proposed method. The results show that in the same specimen, the temperature difference between areas with similar fibre content falls in a very narrow range (e.g., 3.5–4.5 °C for a 100 × 100 × 100 mm specimen exposed to 600 W of microwave irradiation for 180 s). When the fibre content in a given area is more than 0.5 vol%, a large temperature difference forms between this and other areas. When the equivalent fibre content of the specimen is ignored, the temperature difference between the two areas is directly proportional to the difference in fibre content. Therefore, the area that shows differences in fibre clustering and fibre content can be easily determined found based on the temperature difference derived from MI-TS.

Advanced monitoring of damage behavior and 3D visualization of fiber distribution in assessing crack resistance mechanisms in steel fiber-reinforced concrete

This study presents a novel approach to understanding the reinforcing mechanisms of steel fiber-reinforced concrete, particularly its crack resistance and toughening effects. By integrating dynamic surface strain monitoring with advanced three-dimensional visualization techniques, new insights are provided into how steel fibers enhance concrete's structural performance. X-ray computed tomography (CT) technology enabled detailed 3D visualization and analysis of steel fibers embedded within the concrete matrix, facilitating an in-depth examination of their distribution patterns, orientation, and positioning. Additionally, the Digital Image Correlation (DIC) three-dimensional full-field strain measurement system was utilized to dynamically monitor the loading process during three-point bending tests on semicircular concrete specimens. The findings indicate that the uniform distribution of steel fibers significantly reduces stress concentration and effectively delays crack initiation and propagation. Steel fibers bridge cracks, mitigate local stress concentrations, and substitute for the tensile components of concrete after macro-crack formation, thereby inhibiting further crack propagation. Quantitative analysis of fiber distribution through 3D image reconstruction and coordinate extraction confirmed a strong correlation between fiber orientation and crack resistance. This study contributes to the field by offering a comprehensive analysis of key parameters such as fracture process strain monitoring, 3D visualization, and quantification of fiber distribution, advancing the understanding of crack propagation mechanisms and failure processes in fiber-reinforced concrete.

Influence of Steel and Poly Vinyl Alcohol Fibers on the Development of High-Strength Geopolymer Concrete

The present study focuses on the mechanical performance of steel and polyvinyl alcohol fibers embedded in the geopolymer matrix. A high-strength geopolymer concrete with fly ash, slag and silica fume as precursors and sodium hydroxide and sodium silicate solutions as activators has been tested for its strength in compression and flexure. The influence of fibers on flowability, long-term shrinkage and sulphuric acid attack on the geopolymer concrete has also been studied. The dosage of fibers was maintained at 1%, 2% and 3% by volume, and fibers of length 13 mm have been used in the study. Results indicate that slag with 3% steel fibers by volume had a predominant influence on the strength development of steel fiber-reinforced geopolymer concrete, yielding a compressive strength of 107 MPa after 28 days. Blast furnace slag resulted in increasing the shrinkage of concrete due to rapid gel formation owing to the presence of calcium ions, although the fibers helped reduce the shrinkage to some extent. The strength of steel fiber geopolymer concrete was superior to PVA fiber geopolymer concrete; however, after an acid attack, the strength of steel fiber geopolymer concrete was reduced more than PVA fiber geopolymer concrete due to the enhanced corrosion resistance of PVA fibers.

3D mesoscale model of steel fiber reinforced concrete based on equivalent failure of steel fibers

Steel fiber reinforced concrete (SFRC) is widely used in civil engineering. Investigating the mechanical properties and failure mechanisms of SFRC materials using mesoscopic models holds significant theoretical and practical importance. The bond-slip interaction between steel fibers and the cementitious matrix is crucial, yet current computational capabilities struggle to simulate the interactions among the numerous components within concrete containing a large number of steel fibers. This paper establishes an equivalent failure model to replace the bond-slip interaction. Based on this model, a multiphase mesoscopic model (including aggregates, the interfacial transition zone (ITZ), mortar, and steel fibers) is developed to predict the mechanical properties and failure modes of SFRC. The accuracy and validity of the model are verified by comparing the simulation results with the experimental results from four-point bending tests on SFRC beams, focusing on load-displacement curves, failure modes, and crack propagation. The results indicate that under load, the ITZ at the sharp edges of large aggregates in the SFRC beam is the first to be damaged, forming microcracks. Subsequently, these cracks bypass the large particles and propagate towards weaker regions with fewer steel fibers, forming macrocracks. At this stage, the steel fibers take over the load from the cementitious matrix and limit crack propagation. The simulated crack propagation trend and crack width during the loading process of the SFRC multiphase mesoscale model match well with the experimental observations. A higher fiber content can mitigate stress concentration within SFRC beams and enhance the fiber bridging effect, significantly improving the flexural load-bearing capacity and toughness of the beams. A smaller steel fiber inclination angle can effectively inhibit the propagation of vertical cracks, thereby increasing the load-bearing capacity and toughness of SFRC beams and extending the service life of the structure.

Comparative analysis of flexural strength prediction in SFRC using frequentist, Bayesian, and Machine Learning approaches

Steel fiber reinforcement significantly enhances the flexural strength of concrete, which is vital for structural integrity. Annex L of the new Eurocode 2 classifies steel fiber-reinforced concrete by its flexural performance, aiding engineers in designing resilient structures. This study investigates the flexural behavior of steel fiber-reinforced concrete (SFRC) using three data-driven methodologies: Frequentist Inference (FI), Bayesian Inference (BI), and Machine Learning (ML). A comprehensive database was constructed from three-point bending tests on SFRC specimens, encompassing various compressive strengths, fiber quantities, and geometric parameters, to identify key factors influencing material properties.The findings indicate that all three methodologies yield comparable predictive capabilities for flexural responses in SFRC. Notably, FI models emphasize the importance of compressive strength and fiber volume fraction, along with fiber properties such as non-dimensional length and tensile strength. BI models enhance predictive stability by integrating prior knowledge and quantifying uncertainty, demonstrating their advantage, particularly in data-scarce situations. Additionally, ML analysis reveals that linear regression (LR) models can achieve accuracy similar to or greater than that of more complex models. This research provides novel insights into the application of BI and ML in concrete technology, emphasizing their potential to enhance predictive modeling. Additionally, it offers practical guidelines for optimizing SFRC design through a case study that compares residual flexural strengths obtained via Bayesian analysis, classifying the material in accordance with Annex L of the new Eurocode 2.

Genetic programming-based algorithms application in modeling the compressive strength of steel fiber-reinforced concrete exposed to elevated temperatures

Steel-fiber-reinforced concrete (SFRC) has replaced traditional concrete in the construction sector, improving fracture resistance and post-cracking performance. However, extreme temperatures degrade concrete's material characteristics including stiffness and strength. The construction industry increasingly embraces machine learning (ML) to estimate concrete properties and optimize cost and time accurately. This study employs independent ML methods, gene expression programming (GEP), multi-expression programming (MEP), XGBoost, and Bayesian estimation model (BES) to predict SFRC compressive strength (CS) at high temperatures. 307 experimental data points from published studies were utilized to develop the models. The models were trained using 70 % of the dataset, with 15 % for validation and 15 % for testing. Iterative hyperparameter adjustment and trial-and-error refining achieved optimum predictions. All the models were evaluated using correlation (R) values for training, validation, and testing datasets. MEP showed slightly lower R-values of 0.923, 0.904, and 0.949 than GEP, which performed consistently with 0.963, 0.967, and 0.961. XGBoost had the greatest training R-value of 0.997 but dropped in validation (0.918) and testing (0.896). BES model exhibited commendable performance with scores of 0.986, 0.944, and 0.897. GEP and XGBoost exhibited great accuracy, with GEP sustaining constant accuracy across all datasets, highlighting its potency in predicting CS. Interpreting model predictions using SHapley Additive exPlanation (SHAP) highlighted temperature over heating rate. CS improved significantly as the steel fiber volume fraction (Vf) reached 1.5 %, plateauing thereafter. The proposed models are valid and accurate, providing designers and builders with a practical and adaptable method for estimating strength in SFRC structural applications, particularly under high-temperature conditions.

Utilizing incineration bottom ash-infused steel fiber-reinforced concrete: An analysis of compressive strength and carbonation durability

This study has developed concrete with moderate strength, and excellent carbonation resistance, by blending incineration bottom ash with steel fibers into the concrete mixture. Through single-factor experiments, investigate the effects of bottom ash content and steel fiber volume fraction on the mechanical properties and carbonation resistance of concrete. The results indicate that the addition of steel fibers effectively reduces the adverse effects of incineration bottom ash content on the compressive strength and carbonation level of concrete. When the bottom ash content is 20 % and the steel fiber content is 2 %, the compressive strength of concrete reaches 56.1 MPa, and the carbonation depth drops to 4.19 mm after 28 days. In addition, a carbonation depth prediction model was established based on the compressive performance of concrete, accurately predicting the carbonation depth at different carbonation stages.

Cyclic bond behavior and bond stress – slip model of steel strands in steel fiber reinforced concrete

To study the bond performance of steel strands in steel fiber reinforced concrete (SFRC), bonding tests under monotonic and cyclic loads were performed on the specimens with different steel fiber volume fractions (SFVFs), bond lengths, nominal diameters of steel strands and SFRC strengths. The results showed that the relative rotation between the steel strand and SFRC occurred when the steel strands were vertically pulled out from SFRC due to the smooth and continuous helical shape of the steel strands. For the specimens with the same parameters, the maximum bond stress under cyclic load decreased by 1.67–22.88 % than that under monotonic load. The maximum bond stress under cyclic load increased with the increasing SFVFs or SFRC strength. Notably, the maximum bond stress under cyclic load also increased with the increasing bond length or diameter of steel strands, significantly different from that of ordinary ribbed steel bars and FRP bars. The cumulative energy dissipation (CED) of bond specimens increased with the diameter of steel strands and SFRC strength. Besides, the SFVFs had a positive effect on the CED, but simultaneously increasing diameter of steel strands would weaken the positive effect of SFVFs, and simultaneously increasing bond length had no obvious on the CED. Finally, a bond stress – slip model of steel strands in SFRC was established, suitable for calculating bond stress – slip curves under random and complicated loading schemes. By comparing the calculation and test results of bond stress – slip curves, it was proved that the proposed model had an acceptable accuracy and could describe the influence laws of SFVF, bond length, nominal diameter of steel strand and SFRC strength well.

Study of Acoustic Emission Signal Noise Attenuation Based on Unsupervised Skip Neural Network.

Acoustic emission (AE) technology, as a non-destructive testing methodology, is extensively utilized to monitor various materials' structural integrity. However, AE signals captured during experimental processes are often tainted with assorted noise factors that degrade the signal clarity and integrity, complicating precise analytical evaluations of the experimental outcomes. In response to these challenges, this paper introduces an unsupervised deep learning-based denoising model tailored for AE signals. It juxtaposes its efficacy against established methods, such as wavelet packet denoising, Hilbert transform denoising, and complete ensemble empirical mode decomposition with adaptive noise denoising. The results demonstrate that the unsupervised skip autoencoder model exhibits substantial potential in noise reduction, marking a significant advancement in AE signal processing. Subsequently, the paper focuses on applying this advanced denoising technique to AE signals collected during the tensile testing of steel fiber-reinforced concrete (SFRC), the tensile testing of steel, and flexural experiments of reinforced concrete beam, and it meticulously discusses the variations in the waveform and the spectrogram of the original signal and the signal after noise reduction. The results show that the model can also remove the noise of AE signals.

Fiber orientation and orientation factors in steel fiber‐reinforced concrete beams with hybrid fibers: A critical review

AbstractFiber orientation is of paramount importance for the design of fiber‐reinforced concrete (FRC) structural elements, because it markedly influences the postcracking properties of such material. For this reason, structural codes introduce orientation factors which aim to correlate the real mechanical properties of the structural element with the ones determined from standard beams. Although the need of considering fiber orientation in design codes is commonly accepted, the orientation factors are still based on a limited number of research studies, raising the need to better determine fiber orientation to improve the current standards and support the design process of FRC elements. In this research, a steel fiber‐reinforced concrete (SFRC) with a hybrid system of macro and microfibers is steered into a broad range of fiber orientations and cast into standard beams. Besides measuring the mechanical performance of these SFRC beams, three different methods for assessing fiber orientation are employed, namely electromagnetic induction, image analysis, and micro‐computed tomography. The comparison between the outcomes of the different methods provides detailed information about the accuracy and suitability of each method, considering the corresponding domain of applicability at structural level. Finally, a critical review of the most common 2D and 3D orientation parameters found in literature is performed, and the equations are adapted to account for the hybrid system of fibers.

Experimental investigation on post-installed lap splices in ordinary and steel fiber-reinforced concrete

The design of post-installed lap splices typically relies on provisions derived from cast-in-place reinforcing bars (rebars). However, some bond characteristics of post-installed rebars show significant differences compared to cast-in-place rebars, especially when using high-performance mortars. To quantify the bond strength for design purposes, a sound understanding of differences in bond stress distribution, splitting failure mode, and load transfer mechanisms is crucial. This study offers new experimental evidence on these aspects, focusing on the impact of different high-performance injection mortars, lap lengths, and the incorporation of hooked steel fibers in the concrete. Direct tension tests were conducted on spliced post-installed and cast-in-place rebars. Fiber-optic sensors were used to measure strains quasi-continuously on the rebars, minimizing interference on the bond. The findings reveal that post-installed lap splices yield slightly higher bond strength than their cast-in-place counterparts, mainly due to the higher bond stiffness of the mortars. However, this advantage is limited by the bond behavior of the cast-in-rebar within the post-installed lap splice, particularly in conditions of poor confinement. In ordinary strength concrete, cast-in-place rebars exhibit an approximately constant bond stress distribution, as typically assumed for design purposes; by contrast, post-installed rebars show a pronounced non-linear distribution. Furthermore, an addition of steel fibers alters the bond stress of the rebars, resulting in a non-linear distribution in all cases. The study reveals a 20% increase in bond strength of lap splices in concrete reinforced with 80 kg/m3\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\hbox {kg}/\\hbox {m}^3$$\\end{document} of steel fibers. Moreover, they improve the ductility of bond failure for post-installed lap splices.

Sustainable use of recycled fine aggregates in steel fiber-reinforced concrete: Fresh, flexural, mechanical and durability characteristics

Recycling construction waste is a viable tactic for advancing environmentally friendly building methods. With regard to concrete applications, the purpose of this research is to determine whether it is feasible to use recycled fine concrete aggregates (RFA) in lieu of natural fine aggregates (NFA) and to lessen the environmental impact of natural resource depletion and landfill space. A sustainable steel fiber-reinforced concrete was created by replacing NFA with RFA at the replacement ratio of 0 %, 50 %, and 100 %. Steel fibers (SF) were also included to mixes at three different contents of 0, 25 and 50 kg/m3 in order to further improve the qualities of the concretes. Thus, the aim of this paper is to appraise how the addition of FRA affects the mechanical, freeze-thaw, fresh, and non-destructive qualities of concrete. Nine concrete mixtures were cast, and tests were made to evaluate the following properties: flowability, fresh concrete unit weight, tensile and compressive strengths, elastic modulus, surface hardness, crack mouth opening displacement (CMOD), freeze and thaw performance, sulfate resistance and abrasion. Moreover, microstructure properties of concrete were also analyzed. The outcomes revealed that the mechanical, flexural, and durability performances of the concrete mixtures were enhanced by substituting RFA for NFA. The mixture with 50%RFA and 50 kg/m3 SF gained maximum compressive strength of 44.82 MPa which was 20.7 % greater than the reference mixture (RFA0F0). The mixture containing 100 % RFA and 25 kg/m3 SF had the highest elastic modulus and showed an approximately 33 % augmentation in elastic modulus as per the reference mixture. The mixture with 100%RFA and 50 kg/m3 SF exhibited the largest tensile strength indicating 60 % tensile strength enhancement as per the reference mixture. Combined use of RFA and 50 kg/m3 SF in concrete mixtures had the best abrasion and freeze-thaw resistance. SF incorporated concrete mixtures with RFA exhibited worse sulfate resistance. This study contributed significantly to global resource efficiency and environmental preservation by shedding light on the sustainable use of RFA and SF in the making of concrete. The results made important contributions to global research and promote environmentally friendly building methods all throughout the world.

Effectiveness evaluation of different steel fibers on the shear strength of reinforced SFRC slender beams without web rebars

Current studies have mainly focused on the effect of specific steel fibers on the shear performance of steel fiber-reinforced concrete (SFRC) slender beams. However, there has been a lack of in-depth research evaluating the effectiveness of different steel fibers through a statistically comparative analysis of experimental data from various researchers. Existing design methods do not fully account for the impact of all types of steel fibers on the shear capacity of SFRC slender beams, providing very limited guidance on selecting appropriate steel fibers. This highlights the need for research to verify the strengthening effectiveness of different steel fibers. This paper establishes databases comprising 232 shear-failed reinforced SFRC beams with four other types of steel fibers straight wire, deformed wire, deformed cut-sheet and ingot mill, based on a comprehensive review of published literature. These databases complement an existing database of 280 reinforced SFRC beams using hook-end wire steel fibers as shear reinforcement. The databases are used to evaluate the validity of several well-known existing formulas for predicting the shear capacity of beams and to determine the fiber bond factor values that reflect the diverse strengthening effects of different steel fibers. Utilizing a simi-empirical synergetic prediction model for the shear strength of reinforced SFRC slender beams with hook-end wire steel fibers, the shear resistances of test beams in databases with the other four types of steel fiber are analyzed. The primary contributors to shear capacity are identified as the uncracked shear-compression SFRC and the dowel action of longitudinal tensile steel bars. The contribution of steel fibers is linked to the shear resistance of uncracked shear-compression SFRC. From a practical design perspective, a conservative prediction formula is verified, aligning with the lower boundary of the tested shear strength obtained from the database of beams. Finally, suitable steel fibers for s enhancing the shear strength of reinforced SFRC beams without web rebars are suggested based on their effectiveness.

Experimental investigation on influence of embedment length, bar diameter and concrete cover on bond between reinforced bars and steel fiber reinforced concrete (SFRC)

The bond between reinforced bars and concrete has a significant impact on reinforced concrete structures. Incorporating steel fibers in concrete not only enhances the performance of the concrete but also influences the bond strength of embedded reinforcing bars. In this paper, experiments have been carried out to investigate the influences of different bar diameters, concrete cover and embedment lengths on bond-slip responses between reinforcing bars and steel fiber reinforced concrete (SFRC). Furthermore, pull-out tests with normal concrete were also executed as a comparative group. The bond-slip results of specimens were collected. The impact of embedment length, reinforcing bar diameter, and the existence of steel fibers on the bond behavior of reinforcing bars was comprehensively analyzed. Meanwhile, the results indicate that variations in the ratio of concrete cover to reinforcing bar diameter lead to different effects on the bond behavior of SFRC compared with normal concrete. Different failure modes of specimens were exhibited and discussed. Additionally, a mathematical model for bond strength of bars in SFRC was proposed based on the correction of the bond strength model with normal concrete, which shows good agreement with experimental results.