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Steel fiber is the most widely used type of fiber in concrete because of its advanced and economical manufacturing facilities, reinforcing effect, and ability to cope with changing environmental conditions. The use of steel fiber in concrete can reduce the amount of reinforcement used and increase the crack resistance of concrete, which extends the service life of concrete structures, reduces the frequency of maintenance and repairs, and consequently lowers energy consumption and emissions. Many studies focus on the postcracking behavior of steel fiber–reinforced concrete (SFRC). However, it is also crucial to examine the precracking behavior of SFRC at early age. A temperature stress test machine (TSTM) was used to investigate the autogenous shrinkage (AS), tensile creep (TC), and cracking behavior of hooked-end SFRC (HSFRC) at early age under uniaxial constant restrained condition. Analysis and experimental findings demonstrated that (1) the addition of steel fiber increased the splitting tensile strength and modulus of elasticity of HSFRC. The 1-, 3-, and 7-day splitting tensile strength and modulus of elasticity increased with an increase in curing temperature. However, a decrease was observed at 28 days as the curing temperature increased; (2) increasing steel fiber content had a significant influence on reducing TC and AS of HSFRC. TC and AS of HSFRC increased with increasing curing temperature; and (3) early-age cracking potential of HSFRC decreased as steel fiber content increased. Increased curing temperature resulted in a concomitant elevation in the potential for early-age cracking in HSFRC. [ABSTRACT FROM AUTHOR]

This manuscript presents a comprehensive investigation into the tensile strength and flexural behavior of steel fiber‐reinforced concrete (SFRC), utilizing a robust dataset comprising 457 meticulously selected data points, including 54 instances of self‐tested experimental data. The study establishes an empirical relationship governing the tensile strength of steel fiber concrete (SFC) based on the fiber reinforcement index (FRI) and the grade of concrete. The experimental study has extended its scope by testing an additional 27 full‐scale SFRC beams (rectangular cross‐sectioned) under monotonic loading to quantify the impact of tensile strength on the overall structural performance. The beam specimens feature continuous longitudinal and transverse reinforcement throughout their span, with steel fibers strategically positioned below the neutral axis. The steel fiber content was systematically varied from 0% to 2% in intervals of 0.25%. This work introduces modifications to the design of SFRC flexural members, emphasizing the consideration of tensile contribution below the neutral axis. The study integrates both experimental and analytical methodologies, providing diverse insights into the flexural behavior of SFRC beams. The proposed design methodology considers the tensile strength contribution from SFC, effectively predicting flexural capacity, particularly in scenarios involving higher volume fractions. Its applicability extends to structural design, offering insights into evaluating structural capacity for both newly designed and existing structures. [ABSTRACT FROM AUTHOR]

Fiber 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. [ABSTRACT FROM AUTHOR]

In the context of reinforced concrete (RC) arch bridges, while the incorporation of full sections of steel fibers can enhance the bridge's toughness, cracking resilience, and bearing capacity, achieving an optimal balance between structural performance and economic viability in this manner remains challenging. This article introduces a novel computational approach—the distributed steel fiber concrete (LSFRC) arch—which considers the spatial distribution of damage in RC arches. The static performance of SFRC elements and LSFRC beams was compared and analyzed using the concrete plastic damage model (CDP) in ABAQUS software. This study validated the rationality of the model and investigated the impact of varying steel fiber volume ratios and steel fiber layer heights on the damage evolution of LSFRC arches. The results of this study demonstrate that the cracking load and bearing capacity of an RC arch can be effectively enhanced through the addition of steel fibers to a local area under static loading. Furthermore, the deflection and damage to the arch waist and arch roof can be significantly reduced. Furthermore, the incorporation of steel fibers at an increased volume rate and at a greater height within the doped section can effectively slow the rate of damage evolution within the section. This results in the inhibition of crack extensions and in an improvement in the ductility and reliability of the damage stage. The LSFRC arches offer superior economic and practical advantages over their full cross-section doped steel fiber (FRC) counterparts. This study offers novel insights and methodological guidance for the design and implementation of concrete arch bridges. [ABSTRACT FROM AUTHOR]

The purpose of the study is to examine the physical and mechanical characteristics of steel fiber-reinforced concrete under compression, including: modulus of elasticity, Poisson ratio, values of ultimate strains under compression, values of compressive strength with different percentages of dispersed reinforcement. An experimental investigation program, which included the production of cube samples measuring 100×100×100 mm, as well as a compression test under static loading, taking into account unloading from the region of inelastic deformations, was developed and carried out. Two types of steel fiber were chosen as dispersed reinforcement: hooked end and wave shape. The volume content of steel fiber in the cube samples was 0.5, 1.0, 1.5 and 2.0 %. As a result of the investigation, the strength and deformation characteristics of steel fiber reinforced concrete under compression were obtained. Based on the experimental data, actual strain diagrams of steel fiber reinforced concrete were constructed, taking into account the type of reinforcing fibers and the percentage of reinforcing fiber. Based on the obtained diagrams, a law of deformation of steel fiber reinforced concrete is proposed, which can be described by a polynomial function of the fourth order with constant coefficients that determine the shape of the stress-strain curve. The presented research results can be used in developing a methodology for physically nonlinear analysis of steel fiber reinforced concrete elements with a percentage of dispersed reinforcement from 0.5 to 2.0 %.

Reinforced concrete slabs are widely used in building structures due to their economic, durable, and aesthetic advantages. The determination of their ultimate strength often hinges on punching shear strength. Presently, methods such as closed hoops, steel bending, and fiber reinforcement are employed to enhance punching shear strength, with fiber reinforcement gaining popularity due to its ease of implementation and efficacy in improving concrete durability. This study introduces a novel approach employing six machine learning algorithms rooted in decision trees and decision tree-based ensemble learning to predict punching shear strength in steel fiber-reinforced concrete slabs. To overcome experimental data limitations, a data augmentation approach based on the Gaussian mixture model is employed. The validation of the data augmentation is conducted through "synthetic training—real testing" and "real training—real testing". Additionally, the best machine learning model is analyzed for explainability using Shapley Additive exPlanation (SHAP). Results demonstrate that the proposed data augmentation method effectively captures the original data distribution, enhancing the robustness and accuracy of the machine learning model. Moreover, SHAP provides better insights into the features influencing punching shear strength. Thus, the proposed data enhancement model offers a reliable approach for modeling small experimental datasets in structural engineering. [ABSTRACT FROM AUTHOR]

In the context of reinforced concrete (RC) arch bridges, while the incorporation of full sections of steel fibers can enhance the bridge’s toughness, cracking resilience, and bearing capacity, achieving an optimal balance between structural performance and economic viability in this manner remains challenging. This article introduces a novel computational approach—the distributed steel fiber concrete (LSFRC) arch—which considers the spatial distribution of damage in RC arches. The static performance of SFRC elements and LSFRC beams was compared and analyzed using the concrete plastic damage model (CDP) in ABAQUS software. This study validated the rationality of the model and investigated the impact of varying steel fiber volume ratios and steel fiber layer heights on the damage evolution of LSFRC arches. The results of this study demonstrate that the cracking load and bearing capacity of an RC arch can be effectively enhanced through the addition of steel fibers to a local area under static loading. Furthermore, the deflection and damage to the arch waist and arch roof can be significantly reduced. Furthermore, the incorporation of steel fibers at an increased volume rate and at a greater height within the doped section can effectively slow the rate of damage evolution within the section. This results in the inhibition of crack extensions and in an improvement in the ductility and reliability of the damage stage. The LSFRC arches offer superior economic and practical advantages over their full cross-section doped steel fiber (FRC) counterparts. This study offers novel insights and methodological guidance for the design and implementation of concrete arch bridges.

In recent years, steel fiber-reinforced concrete (SFRC) single-layer linings have been used in tunnel engineering. Compared to plain concrete single-layer linings, SFRC single-layer linings demonstrate enhanced bearing capacity, durability, and sustainability. Existing studies primarily focused on the mechanical properties of SFRC; however, limited investigations have been conducted on the cracking pattern of SFRC linings. This study uses laboratory tests to examine the influence of steel fiber content and aspect ratio on the mechanical properties of concrete, such as compressive strength and elastic modulus. After the recommended content and aspect ratio of steel fiber are proposed through tests, the cracking pattern and safety performance of plain concrete and SFRC linings under surrounding rock pressure are studied using a similar model test. The test results indicate that the recommended steel fiber volume fraction and aspect ratio for CF35 SFRC are 0.58% and 70, respectively. Due to the effect of loose load, cracks initially develop on the inside of arch crowns in both plain concrete and SFRC single-layer linings. Subsequently, new cracks appear on the inside of the lining floor and the outside of the two wall feet. Numerous narrow cracks with rugged and winding expansion paths can be found on SFRC single-layer linings. Conversely, plain concrete single-layer linings exhibit fewer cracks with larger widths along a straighter path. The initial cracking load of a single-layer lining made of plain concrete is 0.027 MPa, whereas for a single-layer lining made of SFRC, it is 0.04 MPa. This indicates that SFRC can effectively enhance the initial cracking load of lining structures. In the event of damage to the lining, the most critical area for the plain concrete single-layer lining is at the two wall feet, where the minimum safety factor is 1.66. However, for the SFRC lining in the same location, the safety factor is 2.7, resulting in a 62.7% increase in safety. [ABSTRACT FROM AUTHOR]

In this study, a torsional strength model for SFRC beams with transverse reinforcement was developed based on a simplified softened truss model. The proposed model had two unique features. One feature was the introduction of multi-potential capacity criteria by considering two failure modes owing to aggregate interlock and concrete crushing. The stresses in the model were calculated straightforwardly using simple equations for the torsional effective thickness and principal stress angle. Another feature was the introduction of the effective longitudinal and transverse cross-sectional areas of the fibers to estimate their contributions to the torsional strength. Two possible failure types for the steel fibers, debonding and fracture, were considered. The proposed model was validated by comparing the calculated torsional strength with test data for SFRC beams obtained from the literature. This comparison showed that the proposed model could capture the torsional strengths of SFRC beams with wide ranges for the reinforcement ratios in both directions, properties and amount of steel fibers, concrete properties, and cross-sectional details. The average value of the ratio between the predicted ultimate torsional strength and experimental value was 1.11, with a COV of 0.267, and the proposed model was able to well estimate the failure mode of the specimens. As a practical application of the proposed model, the approach used to estimate the steel fiber contribution was used to extend the torsional strength design procedure in the SNiP code, a building design code used in the Commonwealth of Independent States countries, to cover SFRC beams. [ABSTRACT FROM AUTHOR]

The direct tensile test (DTT) is the most recommended test to determine the tensile behavior of fiber-reinforced concrete (FRC). However, this test is challenging to perform. Several studies have investigated inverse analysis to determine this behavior through simplified tests, such as the bending test. This study deals with developing a new approach to perform an inverse analysis for the three-point bending test (3PBT) involving FRC. A new proposed methodology concerns carrying out the inverse analysis procedure by parts. Initially, the parameters that influence the initial part of the stress–crack opening curve are adjusted. Progressively, the other parameters are adjusted considering the increment of the curve section. This procedure provides an implemented algorithm with more efficiency. A new strategy that deals with the establishment of criteria for parameters is proposed. Its results are compared with experimental data from other literature, whose steel fiber-reinforced concrete (SFRC) tested characteristics present different attributes such as fibers, shape, and length. The proposed methodology obtained the stress–crack opening curves in direct tension with reasonable accuracy, indicating that this methodology can be helpful in the characterization of the post-cracking FRC behavior. [ABSTRACT FROM AUTHOR]

Abstract Finite element analysis with nonlinear material behavior modeling can be used to design concrete structures. This study aimed to develop a computational model to represent the shear behavior of concrete beams without transverse reinforcement described in the literature, with or without steel fibers. Two different approaches of finite element analysis were investigated, namely smeared and discrete crack models. The results of the smeared crack model were compared with the results of double-notched push-through tests, and an empirical equation for the shear retention factor of plain concrete was suggested. The computational model using a discrete crack approach with representation of the aggregate interlocking mechanism was compared with the results of push-of test, and an accurate correlation was observed up to the maximum shear stress. It was concluded that the discrete crack approach provided the most accurate representation of the shear behavior of a non-reinforced beam with a shear span-to-depth ratio of 2.17. However, for a non-reinforced beam with a shear span-to-depth ratio of 2.66, the smeared crack approach accurately represents the shear strength and stiffness of the beam. The shear retention factor had little influence on the overall behavior of a steel fiber-reinforced concrete beam. Finally, it was concluded that a variable shear retention factor should be used in the smeared crack approach with fixed crack, as a constant shear retention factor tends to overestimate the shear strength of beams.

Summary: To address the problems faced with steel‐reinforced concrete (SRC) in construction, such as positional conflicts between steel and steel bars or difficulty in pouring concrete, a novel "Steel and Steel Fiber‐Reinforced Concrete" (SSFRC) composite structure was proposed. Push‐out tests of 34 SSFRC composite columns were carried out in this paper to study the interfacial bond performance from the perspective of energy dissipation. Based on loading‐displacement (P‐D) curves, the interfacial energy dissipation (Wb) and energy dissipation factor (λ) were introduced, and the influence of embedded length (Le), steel fiber volume rate (ρsf), thickness of concrete cover (Css), and section type on Wb and λ were analyzed. Test results indicated that circular column is better than square column in terms of Wb and λ. The increase of Le, Css, or ρsf is beneficial to the improvement of Wb, and λ is positively correlated with ρsf and Css but negatively correlated with Le. Additionally, the interfacial damage (Da) was defined by the relationship between elastic deformation energy (Wa) and Wb. It can be concluded that the ascent of Le and Css can effectively delay the appearance of Da and inhibit the development of Da, respectively, and Da develops slowly with the increase of ρsf at the later loading stage. [ABSTRACT FROM AUTHOR]

Experimental research on the direct shear behavior of fiber-reinforced concrete is often carried out using prisms molded with specific dimensions for a standardized test. However, the flow of fresh concrete in these molds can be different than in the case of a full-scale structural element. This is important considering that the flow direction highly influences the distribution and orientation of fibers. In addition, most of the studies did not relate their shear results to other mechanical properties. In contrast, this study attempted to deepen the experimental knowledge of the crack propagation of a steel fiber-reinforced concrete (SFRC) used in a full-scale prototype of a bridge box girder built in the laboratory. Prismatic specimens were sawn from webs and top flanges of this prototype. Serving as references, additional specimens were molded in wooden boxes. In a previous study of our research group, both had been tested under a three-point notched bending configuration maintaining test conditions proportional to the EN14651 specifications. From each of the previously flexurally tested specimens, two prismatic specimens suitable for the Fédération Internationale de la Précontrainte (FIP) shear test setup were extracted by adopting a cutting methodology that avoided the damage induced by the flexural tests to be part of the FIP specimens. These FIP specimens were tested in almost pure shear loading conditions for assessing the performance of SFRC. Computer tomography images and photos of the shear failure faces were used to determine the distribution and density of fibers. The results demonstrated that the peak loads were proportional to the fiber density at the shear failure section. Assuming that the SFRC conditions of the webs were representative of a common batching procedure in the construction industry, the results from the tests in specimens extracted from these webs were adopted to establish shear stress/flexural tensile stress ratios vs. crack mouth opening displacement curves. The curves belonging to cross-sections of a similar fiber density in the shear and flexural cases allowed for the proposal of a normalized crack-dilatancy relation composed of three stages of the crack propagation. In addition, a trilinear crack width–slip relation was established using the same set of specimens. The relevancy of this proposal is that the shear response can be estimated from a widely accepted standardized flexural test, which demands a simpler instrumentation and is also easier to execute than the shear setup. [ABSTRACT FROM AUTHOR]

The durability of concrete structures that have been in service for a long time under the coupled action of stray current and salt-brine environment has become increasingly important. Because of the excellent mechanical properties of steel fiber–reinforced concrete (SFRC), erosion experiments of SFRC were carried out to study issues such as the erosion depth, strength loss, ion migration, and microscopic morphology changes of SFRC under the action of stray current in a salt-brine environment. The color test and compressive strength were used to characterize the penetration depth of chloride ions and the strength loss of concrete, respectively. The titration method was used to detect the chloride and sulfate ion content. Scanning electron microscopy (SEM) and energy dispersive X-ray spectroscopy (EDS) were used to characterize the microscopic morphology and composition; the finite-element method and discrete-element method were used to explain further and verify the mechanism of SFRC corrosion. This research showed under the action of an electric field, chloride ions transport faster than sulfate ions. The strength loss of SFRC is caused mainly by the erosion and expansion of steel fibers. Products such as gypsum and ettringite are generated only in the cathode area, which also reduces the strength of SFRC to a certain extent. [ABSTRACT FROM AUTHOR]

Geopolymer concrete-filled glass fiber-reinforced polymer (GFRP) tube columns show significant potential in marine engineering due to their excellent durability and mechanical properties. This study investigated the bias performance of steel fiber-reinforced geopolymer recycled aggregate concrete-filled GFRP tube (SGRC-CFFT) columns, considering variables such as load eccentricity, GFRP tube thickness, fiber winding angle, and recycled coarse aggregate (RCA) replacement rate. The failure modes, load-displacement behaviors, and strain distribution were analyzed and discussed. Furthermore, a high-precision finite element model was established based on the test data. The test results indicated that increasing load eccentricity significantly reduced both the load-bearing and axial deformation capacity of CFFT columns. When the load eccentricity reached 0.40, the peak load of the specimen was only 39 % of that of the corresponding axial compression specimen. A higher RCA replacement rate would cause a more pronounced reduction in bias-bearing capacity, with a maximum decrease of 24.6 %. Before 80 % of the peak load, the axial deformation of the column mid-span section accorded to the plane section assumption. Due to the eccentric load, the hoop deformation of the CFFT column was concentrated on the compression side. The fiber winding angle was the critical factor influencing both the peak load and the peak moment of the biased SGRC-CFFT column. • Eccentric load severely diminished the bearing capacity of the SGRC-CFFT columns. • Fiber winding angle was a key factor affecting the bias properties of the CFFTs. • RCA caused a maximum 24.6 % reduction in the bearing capacity of bias CFFTs. • Deformation of the CFFTs adhered to the plane section assumption before 0.8 N p. • Sand-coating enhanced the deformation coordination between concrete and GFRP tube. [ABSTRACT FROM AUTHOR]

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. • The model provides more accurate representation of the material's behavior under load. • Microcracks initiate at the ITZ of large aggregates and propagate towards weaker regions with fewer steel fibers. • A higher fiber content can mitigate stress concentration and improve the toughness of the SFRC beams. • A smaller steel fiber inclination angle can inhibit the propagation of vertical cracks. [ABSTRACT FROM AUTHOR]

Estimating concrete properties using soft computing techniques has been shown to be a time and cost-efficient method in the construction industry. Thus, for the prediction of steel fiber-reinforced concrete (SFRC) strength under compressive and flexural loads, the current research employed advanced and effective soft computing techniques. In the current study, a single machine learning method known as multiple-layer perceptron neural network (MLPNN) and ensembled machine learning models known as MLPNN-adaptive boosting and MLPNN-bagging are used for this purpose. Water; cement; fine aggregate (FA); coarse aggregate (CA); super-plasticizer (SP); silica fume; and steel fiber volume percent (Vf SF), length (mm), and diameter were the factors considered (mm). This study also employed statistical analysis such as determination coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) to assess the performance of the algorithms. It was determined that the MLPNN-AdaBoost method is suitable for forecasting SFRC compressive and flexural strengths. The MLPNN technique's higher R2, i.e., 0.94 and 0.95 for flexural and compressive strength, respectively, and lower error values result in more precision than other methods with lower R2 values. SHAP analysis demonstrated that the volume of cement and steel fibers have the greatest feature values for SFRC's compressive and flexural strengths, respectively. [ABSTRACT FROM AUTHOR]

Serious compression failure of reinforced concrete shear walls under high axial load ratios can be effectively prevented by adding steel fibers at the bottom, combined with an imbedded steel profile in the boundary element. However, the existing axial load ratio limit for conventional reinforced concrete shear walls is not suitable for performance-based design of such composite structural member. The seismic behavior of three composite walls with different fiber volume fractions (0–2.0%) was investigated through experimental and numerical studies under the preliminarily determined axial load ratio limit. The influence of the axial load ratio and boundary element requirements on the displacement ductility was numerically quantified. Addition of fibers assisted in mitigating the crushing of bottom concrete, improving the displacement ductility, and maintaining the bearing capacity under large deformations. Subsequently, an axial load ratio limit relax of 0.1 to 0.2 was adopted for such walls with fiber volume fraction of 1.0% to 2.0% to obtain a displacement ductility consistent with traditional walls. Finally, the axial load ratio limit and corresponding requirements on boundary elements for such walls with separate ductility demand have been recommended, which can provide guidance for performance-based design of such composite walls with flexure-controlled failure mode, however, shear-controlled walls should also be investigated in further studies. [ABSTRACT FROM AUTHOR]

This study performed a scientometric-based examination of the literature on steel fiber-reinforced concrete (SFRC) to identify its key elements. Typical review papers are limited in their capacity to link distinct segments of the literature in an organized and systematic method. The most challenging aspects of current research are knowledge mapping, co-occurrence, and co-citation. The Scopus search engine was used to search for and obtain the data required to meet the goals of the study. During the data evaluation, the relevant publication sources, keyword assessment, productive authors based on publications and citations, top papers based on citations received, and areas vigorously involved in SFRC studies were recognized. The VOSviewer software tool was used to evaluate the literature data from 9562 relevant papers, which included citation, abstract, bibliographic, keywords, funding, and other information. Furthermore, the applications and constraints related to the usage of SFRC in the construction sector were examined, as well as potential solutions to these constraints. It was determined that only 17 publication sources (journals/conferences) had published at least 100 articles on SFRC up to June 2022. Additionally, the mostly employed keywords by authors in SFRC research include steel fibers, fiber-reinforced concrete, concrete, steel fiber-reinforced concrete, and reinforced concrete. The assessment of authors revealed that 39 authors had published at least 30 articles. Moreover, China, the United States, and India were found to be the most active and participating countries based on publications on SFRC research. This study can assist academics in building collaborative initiatives and communicating new ideas and techniques because of the quantitative and graphical depiction of participating nations and researchers. [ABSTRACT FROM AUTHOR]

By improving the ZWT model, a principal structure model applicable to both soft and hard materials under dynamic loading conditions was obtained. Dynamic mechanical experiments were conducted using SHPB to obtain stress–strain curves for coal rock and foam concrete. The ZWT intrinsic model was simplified according to the dynamic impact characteristics of concrete, and the intrinsic model was established by introducing macroscopic damage quantity D and correction factor δ. The stress–strain curves of coal rock, foamed concrete, steel fiber concrete, granite, lightweight foamed concrete, and EPS concrete at different strain rates were used to validate the present constitutive model and prove the correctness of the model. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The interface performance of steel fiber-reinforced concrete (SFRC) is a critical factor in determining mechanical properties and durability. The degradation of the concrete matrix and micro-structure interface is caused by environmental erosion, which shortens the service life of the structure design. Considering different volume contents of steel fiber (0%, 1%, 2%), the failure mechanism of SFRC under different environmental erosion conditions was studied through a laboratory test scheme. A total of six environmental factors are selected, including water, sodium chloride solution, sodium sulfate solution, dilute sulfuric acid solution, sodium hydroxide solution, and a freeze-thaw cycle. When subjected to different erosion concentrations and periods, micro-structure and axial bearing capacity deterioration laws are compared and analyzed. A durability equation related to fiber mixture ratio and strength is presented based on the experimental data and the numerical simulation method. The influence of different environments on steel fiber-reinforced concrete is analyzed, and the grey correlation degree of axial compressive strength is analyzed. The experimental results show that steel fiber can effectively improve the concrete axial bearing capacity, but different responses are observed under the various erosion conditions. A freeze-thaw cycle environment has the most significant impact on the axial compressive strength of concrete, followed by the sulfuric acid environment, and other environments have a weaker impact. The research results will provide a theoretical basis for predicting the performance deterioration of SFRC concerning other erosion conditions and periods. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study aims at investigating the performance of Portland cement concrete incorporating construction and demolition waste aggregate in conjunction with rubber and steel fibers from post-consumer tires. Eight concrete mixes were prepared and tested. In these, natural coarse aggregate (NCA) was replaced by 100 % recycled concrete aggregate (RCA) and/or 20 % and 40 % rubber coarse aggregate (RuCA). The contribution of post-consumer tire steel fibers (PCTSF) to the behavior of the various recycled concrete mixes was also examined. The axial compressive stress–strain, flexural stress–deflection, and splitting tensile strength behaviors together with the dry density, porosity and rapid chloride penetration for all concrete mixes were investigated and compared. The results indicated that increasing the replacement levels of RuCA resulted in a decrease in concrete strength, although the decrease was less pronounced in the concrete mixes with RCA than in mixes incorporating NCA. Specifically, the reductions in compressive, tensile and flexural strengths were respectively as much as 6.6 %, 12.9 % and 14 % less in the mixes containing RCA. Also, the addition of PCTSF substantially mitigated the reductions in strength and, more significantly, the deflection at peak stress was increased by up to 664 %. Furthermore, incorporation of these fibers maintained up to 47 % of the flexural peak strength of the concrete mix at 4 mm deflection in the post-peak region. Additionally, the porosity of the concrete mixes ranged from 6 % to 12 %, placing them in the high-durability concrete category. The dry density exhibited an inverse relationship with the rubber aggregate content, resulting in a reduction of up to 10.2 % compared to the control mix. Hence, utilizing a substantial volume of post-consumer tire materials in recycled-aggregate concrete mixes is envisaged as an ideal choice in construction applications where lower concrete rigidity is desired, for example in seismic or vibrated zones. The approach also aligns with the increasing emphasis on sustainable construction materials. • Mechanical and durability properties of concrete mixes were investigated. • Elastic modulus values were compared with the values predicted by various codes. • PCTSF retained adequate concrete strength and enhanced its post-peak behavior. • High-durability concrete mixes with porosity 9–12 % were developed. • Lightweight concrete mixes with densities less than 2200 kg/m3 were produced. [ABSTRACT FROM AUTHOR]

Through experiments and mesoscale numerical simulations, we investigated the dynamic compression mechanical properties of steel fiber-reinforced basic magnesium sulfate cement concrete (BMSCC) containing coarse aggregates. First, the static mechanical properties of BMSCC reinforced with different amounts of steel fibers were prepared and tested. In addition, the dynamic compression properties of BMSCC under different strain-rate conditions were tested using a split-Hopkinson pressure bar (SHPB), and the dynamic increase factor (DIF) model applicable to BMSCC was determined. Then, based on the four-component characteristics of BMSCC (coarse aggregates, mortar, steel fibers, and interface transition zone ITZ), a meso-scale model considering random distribution of coarse aggregates and steel fibers was developed. Finally, the dynamic compression properties of BMSCC were numerically simulated by the numerical simulation method. The results showed that the mesoscopic model developed in this study can well predict and characterize the dynamic mechanical behavior of BMSCC. • Whisker self-toughening cement was used to prepare high toughness basic magnesium sulfate cement concrete (BMSCC). • BMSCC exhibits excellent crack resistance and impact resistance under the combined action of 5-1-7 whiskers and fibers. • A 3D random packing model of a mixed structure containing coarse aggregates and steel fibers was developed. [ABSTRACT FROM AUTHOR]

The direct tensile test (DTT) is the most recommended test to determine the tensile behavior of fiber-reinforced concrete (FRC). However, this test is challenging to perform. Several studies have investigated inverse analysis to determine this behavior through simplified tests, such as the bending test. This study deals with developing a new approach to perform an inverse analysis for the three-point bending test (3PBT) involving FRC. A new proposed methodology concerns carrying out the inverse analysis procedure by parts. Initially, the parameters that influence the initial part of the stress–crack opening curve are adjusted. Progressively, the other parameters are adjusted considering the increment of the curve section. This procedure provides an implemented algorithm with more efficiency. A new strategy that deals with the establishment of criteria for parameters is proposed. Its results are compared with experimental data from other literature, whose steel fiber-reinforced concrete (SFRC) tested characteristics present different attributes such as fibers, shape, and length. The proposed methodology obtained the stress–crack opening curves in direct tension with reasonable accuracy, indicating that this methodology can be helpful in the characterization of the post-cracking FRC behavior.

The behavior of steel fiber-reinforced concrete (SFRC) exposed to simulated marine environments was investigated in this work. The cubic compressive strength as well as axial compressive and flexural properties of SFRC subjected to chloride attack was monitored over 36 months. Meanwhile, the chloride transport behavior in SFRC was also investigated. Furthermore, the effect of water-to-binder ratio (W/B), mineral admixtures, and exposure conditions on the mechanical properties and chloride transport of SFRC was analyzed. Results showed that many rust spots appeared on SFRC surfaces after 36 months of chloride attack, while the internal steel fibers remained intact despite chloride penetration depths over 20 mm. The SFRC specimens immersed in 3% NaCl solution for 36 months showed no decreases in their cubic compressive strengths, axial compressive strengths, flexural strengths and toughness as compared to unexposed specimens. However, the SFRC specimens exposed to drying-wetting cycles condition showed some slight degradations. In addition, the SFRC specimens with 20% fly ash showed higher chloride resistance than the specimens with 20% fly ash and 40% slag. This study provides more experimental data and evidences for the application of SFRC in a marine environment. [ABSTRACT FROM AUTHOR]

The performance of Steel Fiber-reinforced Concrete (SFRC) is superior to that of conventional concrete. Due to its intricacy and limited available data, the development of a strength prediction model for SFRC is very difficult. To prevail over this constraint, research was carried out to build a deep-learning algorithm for the prediction of flexural, split tensile and compressive strengths of SFRC. To accomplish this, a dataset was created by accumulating SFRC strengths through an extensive literature survey. Initially, the deep features of fine aggregate-cement ratio, coarse aggregate-cement ratio, water-cement ratio, fly ash-cement ratio, super plasticizer-cement ratio, length, diameter and dosage of fiber are learned through a convolutional neural network. Then, softmax regression was used to develop a prediction model. The prediction model is trained and tested using 89 datasets with various mix ratios. From the results, we can conclude that the deep-learning-based prediction model exhibits greater accuracy, greater efficiency and greater generalization capacity compared to those of the conventional neural network model. [ABSTRACT FROM AUTHOR]

Steel fiber–reinforced concrete (SFRC) is extensively used in coupling beams due to its superior mechanical properties. In this study, the two-parameter kinematic theory is modified by introducing the consideration of the critical crack angle and longitudinal reinforcement strain. The modified method is validated by 64 sets of experimental coupling beams in the literatures, and the predicted shear capacities are found to have good agreement with the test results. Moreover, the decomposition of the shear capacity provided by steel fibers, stirrups, the critical loading zone, aggregate interlock, and the dowel action of the longitudinal reinforcement can be evaluated separately. Thus, the effects of the span-to-depth ratio, volume fractions of fibers, and stirrup ratios on the shear capacity are investigated. Additionally, the equivalent substitution relationship between stirrups and steel fibers is quantified, which provides a solution to the reduction of the density of stirrups in coupling beams and similar components. [ABSTRACT FROM AUTHOR]

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Experimental research on the direct shear behavior of fiber-reinforced concrete is often carried out using prisms molded with specific dimensions for a standardized test. However, the flow of fresh concrete in these molds can be different than in the case of a full-scale structural element. This is important considering that the flow direction highly influences the distribution and orientation of fibers. In addition, most of the studies did not relate their shear results to other mechanical properties. In contrast, this study attempted to deepen the experimental knowledge of the crack propagation of a steel fiber-reinforced concrete (SFRC) used in a full-scale prototype of a bridge box girder built in the laboratory. Prismatic specimens were sawn from webs and top flanges of this prototype. Serving as references, additional specimens were molded in wooden boxes. In a previous study of our research group, both had been tested under a three-point notched bending configuration maintaining test conditions proportional to the EN14651 specifications. From each of the previously flexurally tested specimens, two prismatic specimens suitable for the Fédération Internationale de la Précontrainte (FIP) shear test setup were extracted by adopting a cutting methodology that avoided the damage induced by the flexural tests to be part of the FIP specimens. These FIP specimens were tested in almost pure shear loading conditions for assessing the performance of SFRC. Computer tomography images and photos of the shear failure faces were used to determine the distribution and density of fibers. The results demonstrated that the peak loads were proportional to the fiber density at the shear failure section. Assuming that the SFRC conditions of the webs were representative of a common batching procedure in the construction industry, the results from the tests in specimens extracted from these webs were adopted to establish shear stress/flexural tensile stress ratios vs. crack mouth opening displacement curves. The curves belonging to cross-sections of a similar fiber density in the shear and flexural cases allowed for the proposal of a normalized crack-dilatancy relation composed of three stages of the crack propagation. In addition, a trilinear crack width–slip relation was established using the same set of specimens. The relevancy of this proposal is that the shear response can be estimated from a widely accepted standardized flexural test, which demands a simpler instrumentation and is also easier to execute than the shear setup.

Estimating concrete properties using soft computing techniques has been shown to be a time and cost-efficient method in the construction industry. Thus, for the prediction of steel fiber-reinforced concrete (SFRC) strength under compressive and flexural loads, the current research employed advanced and effective soft computing techniques. In the current study, a single machine learning method known as multiple-layer perceptron neural network (MLPNN) and ensembled machine learning models known as MLPNN-adaptive boosting and MLPNN-bagging are used for this purpose. Water; cement; fine aggregate (FA); coarse aggregate (CA); super-plasticizer (SP); silica fume; and steel fiber volume percent (Vf SF), length (mm), and diameter were the factors considered (mm). This study also employed statistical analysis such as determination coefficient (R2), root mean square error (RMSE), and mean absolute error (MAE) to assess the performance of the algorithms. It was determined that the MLPNN-AdaBoost method is suitable for forecasting SFRC compressive and flexural strengths. The MLPNN technique’s higher R2, i.e., 0.94 and 0.95 for flexural and compressive strength, respectively, and lower error values result in more precision than other methods with lower R2 values. SHAP analysis demonstrated that the volume of cement and steel fibers have the greatest feature values for SFRC’s compressive and flexural strengths, respectively.

This study performed a scientometric-based examination of the literature on steel fiber-reinforced concrete (SFRC) to identify its key elements. Typical review papers are limited in their capacity to link distinct segments of the literature in an organized and systematic method. The most challenging aspects of current research are knowledge mapping, co-occurrence, and co-citation. The Scopus search engine was used to search for and obtain the data required to meet the goals of the study. During the data evaluation, the relevant publication sources, keyword assessment, productive authors based on publications and citations, top papers based on citations received, and areas vigorously involved in SFRC studies were recognized. The VOSviewer software tool was used to evaluate the literature data from 9562 relevant papers, which included citation, abstract, bibliographic, keywords, funding, and other information. Furthermore, the applications and constraints related to the usage of SFRC in the construction sector were examined, as well as potential solutions to these constraints. It was determined that only 17 publication sources (journals/conferences) had published at least 100 articles on SFRC up to June 2022. Additionally, the mostly employed keywords by authors in SFRC research include steel fibers, fiber-reinforced concrete, concrete, steel fiber-reinforced concrete, and reinforced concrete. The assessment of authors revealed that 39 authors had published at least 30 articles. Moreover, China, the United States, and India were found to be the most active and participating countries based on publications on SFRC research. This study can assist academics in building collaborative initiatives and communicating new ideas and techniques because of the quantitative and graphical depiction of participating nations and researchers.

The volumetric changes of concrete, including drying shrinkage, are effectively controlled in steel fiber-reinforced concrete (SFRC) mixtures due to the action of the included steel fiber. The current code provision in ANSI/SDI C-2017 allows a minimum steel fiber content of 0.2% of the volume fraction of concrete to control drying shrinkage and to manage cracking in the slab. Limited research has addressed replacing the shear reinforcement in concrete beams with steel fiber. In this study, we used newly developed arched steel fiber to evaluate shrinkage characteristics, including free-drying shrinkage and restrained drying shrinkage, of SFRC and scaled-down deck slab elements. We compared the measured drying shrinkage test results to predicted results obtained from models found in the literature. We confirmed that, overall, the number, width, and length of cracks were reduced significantly at the surface of SFRC slabs when arched steel fiber at 0.2% volume fraction was included in the mixture. [ABSTRACT FROM AUTHOR]

In this paper, novel unified equations for predicting the residual flexural tensile strength of normal‐weight, lightweight, and ultra‐lightweight steel fiber‐reinforced concrete (SFRC) are proposed. The unified equations were obtained by conducting multiple nonlinear regression analysis on the test results from 60 notched prism tests conducted according to EN 14651. The proposed unified equations account for fiber geometry, fiber content, concrete compressive strength, and concrete density. Unlike existing prediction equations, which can only predict the flexural tensile strength of normal‐weight SFRC, the proposed equations are applicable for SFRC with varying densities. Test results from the literature verified the accuracy of the proposed equations to within 10% of observed values, on average. Compared to the existing equations, the proposed unified equations were better at predicting residual flexural tensile strength of both normal‐weight and lightweight steel fiber reinforced concrete. [ABSTRACT FROM AUTHOR]

By improving the ZWT model, a principal structure model applicable to both soft and hard materials under dynamic loading conditions was obtained. Dynamic mechanical experiments were conducted using SHPB to obtain stress–strain curves for coal rock and foam concrete. The ZWT intrinsic model was simplified according to the dynamic impact characteristics of concrete, and the intrinsic model was established by introducing macroscopic damage quantity D and correction factor δ. The stress–strain curves of coal rock, foamed concrete, steel fiber concrete, granite, lightweight foamed concrete, and EPS concrete at different strain rates were used to validate the present constitutive model and prove the correctness of the model.

The interface performance of steel fiber-reinforced concrete (SFRC) is a critical factor in determining mechanical properties and durability. The degradation of the concrete matrix and micro-structure interface is caused by environmental erosion, which shortens the service life of the structure design. Considering different volume contents of steel fiber (0%, 1%, 2%), the failure mechanism of SFRC under different environmental erosion conditions was studied through a laboratory test scheme. A total of six environmental factors are selected, including water, sodium chloride solution, sodium sulfate solution, dilute sulfuric acid solution, sodium hydroxide solution, and a freeze-thaw cycle. When subjected to different erosion concentrations and periods, micro-structure and axial bearing capacity deterioration laws are compared and analyzed. A durability equation related to fiber mixture ratio and strength is presented based on the experimental data and the numerical simulation method. The influence of different environments on steel fiber-reinforced concrete is analyzed, and the grey correlation degree of axial compressive strength is analyzed. The experimental results show that steel fiber can effectively improve the concrete axial bearing capacity, but different responses are observed under the various erosion conditions. A freeze-thaw cycle environment has the most significant impact on the axial compressive strength of concrete, followed by the sulfuric acid environment, and other environments have a weaker impact. The research results will provide a theoretical basis for predicting the performance deterioration of SFRC concerning other erosion conditions and periods.

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.

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.

The determination of the postcrack tensile properties of steel fiber–reinforced concrete (SFRC) after exposure to elevated temperatures is a current methodological challenge. The objective of this research is to evaluate the applicability of the double edge wedge splitting (DEWS) test to characterize the postcrack tensile properties of SFRC after exposure to elevated temperatures. Results show that the DEWS test has reduced scatter and facilitates the interpretation of results with diminished frictional interaction between the apparatus and the specimen. The results in terms of coefficient of mechanical degradation were comparable to those obtained by bending tests in literature, which highlights the suitability of the DEWS test to be adopted in future researches and guidelines. The postcrack tensile properties were not significantly affected up to ∼300°C and a linear reduction ratio was verified with the increase in temperature. Additionally, the constitutive model proposed may reproduce the effect of temperature on the tensile stress-strain behavior of the SFRC and is a valuable input for hygro-thermo-mechanical numerical models oriented to simulate the mechanical behavior of structures made with SFRC. [ABSTRACT FROM AUTHOR]

Concrete being one of the most extensively used material worldwide, the ecological influence of the manufacture of the raw materials for concrete is substantial. The scale of the problem makes it essential to examine other sources of raw materials to minimize the consumption of available natural resources. Since more than 70% of the bulk of conventional concrete consists of aggregates, maximizing the use of alternate/waste materials brings remarkable benefits such as a decrease in the demand for natural aggregates and saving energy costs related to natural aggregate production and conveyance. India, the second-largest consumer of crushed aggregates in the world, is making great strides in using waste/recycled materials as aggregates in pavement construction. Every year, millions of tons of reclaimed asphalt pavement (RAP) are being produced from the repair and reconstruction of existing roads. If this waste material can be reused without much processing in the construction of new pavements, it could be a great alternative to natural aggregates. This paper is intended to examine the flexural fatigue behavior of RAP-based concrete with and without steel fibers. The study considers varied proportions of RAP content (0% to 30%), fiber content (0% and 1%), and stress levels (0.9, 0.8, and 0.7). A total of 96 prismatic samples were examined under static and flexure fatigue load. The frequency for the fatigue loading was kept at 2 Hz, maintaining a stress ratio at 0.1. The results indicate a decrease in flexural fatigue life with an increase in the RAP content at all the stress levels. However, there is a substantial enhancement in fatigue life when steel fibers are added to RAP-based concrete. The results show that two-parameter Weibull distribution is the best fit for modeling fatigue life variation. The fatigue strength (at a survival probability of 0.9, corresponding to two million load repetitions) decreased by 9%, 18%, and 23% for RAP contents of 10%, 20%, and 30%, respectively. The addition of fibers enhanced the fatigue strength of RAP-based concrete by 50%–65%. [ABSTRACT FROM AUTHOR]