Shear Strength Of Beams Research Articles

A new model for predicting the shear strength of RC beams strengthened with externally bonded FRP sheets

Externally bonded fibre reinforced polymer (FRP) reinforcements have been widely used for upgrading the shear resistance of reinforced concrete (RC) structures. This technique has been widely studied and numerous analytical models currently exist to predict the contribution of FRP for the shear resistance of RC beams. In this study, some of the most well-known models recommended in guidelines (including fib bulletin 90, CNR-DT 200 R1/2013, and ACI 440.2R-17) are selected, and their predictive performance are assessed using a large database of 344 beams compiled from published works. In addition, this study introduces a novel model that accounts for a missing influential predictive component in the existing models, namely the ratio of existing steel stirrups. The comparison of the results obtained with this model and those from the current models demonstrates the best predictive performance of the new one.

Verification of Truss-Arch model unified for RC beams with stirrups using shear database

A shear strength analysis model called “Truss-Arch Model Unified (TAMU)” was proposed for shear-critical reinforced concrete (RC) beams with transverse reinforcement. The TAMU has its roots in the Compatibility Strut-and-Tie Model (C-STM) which is a computational modeling approach for the overall force–deformation analysis of deep beams. The TAMU simplified the C-STM so that a limited analysis can be executed by hand methods of analysis to predict the maximum load-carrying capacity of RC deep beams. While the tensile stress of concrete is generally taken into account to evaluate the shear strength of RC beams, the TAMU limit analysis focuses on the strength reduction of the principal diagonal concrete arch due to the transverse principal tensile strain. In the present study, a shear database is assembled from previous works reported in the literature to further substantiate the validity of the TAMU approach. The shear database consists of tested RC beams with shear reinforcement whose shear span-depth ratios (a/d) are between 0.5 and 4.5. Comparative TAMU analysis results show satisfactory agreement with the ultimate strengths observed in 460 RC beam tests regardless of D- and B-regions, demonstrating the method’s ability to predict the maximum load-carrying capacity for shear-critical beams. Additionally, the TAMU approach shows better accuracy and consistency against the database compared to AASHTO sectional shear design methods. The probability of reasonable prediction of the TAMU method is 92% in d-regions and 78% in B-regions, respectively, which is significantly higher than those of AASHTO sectional shear design methods, especially in d-regions. Thus, the TAMU method may be a viable improvement as an alternative strength analysis method for shear-critical RC beams with transverse reinforcement.

Mechanisms of plasticity development and energy dissipation on concrete-encased steel plate shear wall with boundary steel frame under cyclic loading

Steel frame infilled with composite plate shear wall (SF-CPSW) is an efficient lateral load resisting structure with significant ductility and energy dissipation capacity. In this paper, CPSW comprises the infill steel plate, encased concrete panel and headed studs. The steel frames (SFs) are expected to sustain the vertical load, while as the boundary members of the CPSW, they also influence the lateral behaviors of structure. However, the research on the interactions or plasticity development of SF-CPSW is limited. Thus, its energy dissipation mechanism is unclear to date. This paper conducts finite element analyses on the hysteresis performance and energy dissipation mechanism of SF-CPSW structure under cyclic loading. The hysteresis and skeleton of lateral load-drift ratio responses, distributions and developments of internal forces, stresses and strains and the energy assignments are studied to reveal the mechanisms of lateral load-resisting and plasticity development of SF-CPSW. The effects of steel plate thickness, beam cross-section, column cross-section and concrete panel thickness on the hysteresis performance and energy assignment are investigated to propose the energy dissipation mechanism and associated design methods. The results indicate that the energy dissipation of SF-CPSW is governed by the plastic shearing of infill steel plate and beam web due to the shear force transfers and bending restraints from CPSW to the steel frame. Besides, the columns resist portions of lateral load under such interactions. The structural energy dissipation should develop successively by the shear yielding of steel plate, the shear yielding of beam and column, the flexural yielding of beam, the plastic buckling of steel plate and the flexural yielding of column. As the major members for energy dissipation, the yield shear strength of beam should be higher than 27.5% yield shear strength of infill steel plate.

Shear Strength Prediction of Concrete Beams Reinforced with FRP Bars and Stirrups Using Gene Expression Programming

Existing reinforced concrete (RC) structures in humid regions suffer from deterioration due to the corrosion of ordinary reinforcement bars damaging the whole system. The deterioration of the transverse reinforcement leads to shear failure, which is one of the most dangerous failure modes. Therefore, researchers suggested using fiber-reinforced polymer (FRP) bars as a replacement for reinforcement bars in humid regions to integrate sustainability and improve their serviceability and durability. A simple model that can accurately estimate the shear strength of concrete beams designed with FRP longitudinal bars and stirrups is lacking. This research proposed a simplified Gene expression programming (GEP) based model to estimate the shear strength of FRP concrete beams. Seven parameters that principally dominate the shear behavior of FRP beams were utilized to create the GEP model. The parameters are the beam width, beam depth, concrete compressive strength, FRP tensile longitudinal reinforcement ratio, area of stirrups, spacing between the stirrups, and the ultimate FRP strength of stirrups. A comparison was made between the GEP and ACI-440 models; the R2 values of the total database were 92% and 54% for the GEP and ACI models, respectively. The R2 of the GEP model is considerably higher than that measured for the ACI model, and the errors of the GEP model are low, which affirms that the GEP is superior to the ACI model in estimating the shear strength of FRP beams. The trends of the GEP and ACI-440 models and the empirical results are similar, confirming the GEP model’s consistency. Using the GEP model to estimate the shear strength of concrete beams designed with FRP longitudinal bars and stirrups is recommended.

Machine learning-based models for predicting the shear strength of synthetic fiber reinforced concrete beams without stirrups

Stirrups can be added to reinforced concrete (RC) beams made of plain concrete to increase the shear strength providing better performance against embrittlement. If the need for shear stress is less than the strength of RC beams, reinforced with the minimum number of stirrups, then this choice might not be the most cost-effective. Synthetic Fiber Reinforced Concrete (SyFRC) in Reinforced Concrete (RC) structures is limited due to the lack of predicting models for this emerging material in current literature. However, recent advances in concrete technology have revealed more significant benefits of SyFRC, including high deformation capacity and less corrosive concrete. This study predicts shear strength of SyFRC beams without stirrups using the ACI 318-19 equation in addition to Machine Learning (ML) methods such as LightGBM, XGBoost, and Gene Expression (GEP). A database of 102 tested SyFRC specimens were compiled, processed, and evaluated. With R2 values of 98.91% and 97.22%, respectively, the LightGBM and XGBoost outperformed the other examined algorithms by having the least predictions discrepancy and highest accuracy values. As the ACI 318-19 equation does not account for the fibers volume ratio and shear span-to-depth ratio effects on the shear strength contribution, it projected shear strength with the lowest degree of accuracy, with R2 = 75.5%. The feature importance analysis revealed that these two factors, in addition to the effective beam depth, beam width, longitudinal steel reinforcement ratio, and concrete compressive strength should not be negligible in shear strength prediction. For forecasting the shear strength, a closed-form GEP-based model was suggested. The proposed GEP model has a little lower prediction accuracy with R2 = 88.4%. The performance of the four examined models was evaluated from various perspectives. The analysis shows that, apart from the ACI equation, all considered models effectively predict the effects of shear span-to-depth ratio. This is critical for investigating deep and slender beams, and size effect, which are critical for beams with high effective depths. The current study's findings should give practitioners a solid platform for making precise and straightforward assessments of shear strength.

Reliable machine learning for the shear strength of beams strengthened using externally bonded FRP jackets

All over the world, shear strengthening of reinforced concrete elements using external fiber-reinforced polymer jackets could be used to improve building sustainability. However, reports issued by the American Concrete Institute called for heavy scrutiny before actual field implementation. The very limited number of proposed shear equations lacks reliability and accuracy. Thus, further investigation in this area is needed. In addition, machine-learning techniques are being implemented successfully to develop strength models for complex problems including shear, flexure, and torsion. This study aims to provide a reliable machine-learning model for reinforced concrete beams strengthened in shear using externally reinforced fiber polymer sheets. The proposed model was developed and validated against the experimental database and the very limited models in existing literature. The model showed better agreement with the experimentally measured strength compared to the previous models, which accounted for the effect of various parameters including but not limited to: the element geometry, strengthening details, and configurations. The model could guide the further developments of design codes and mechanical models.

A survey of shear strength behavior of high content fly ash reinforced concrete beams

Fly ash (FA) is a significant industrial byproduct that causes environmental problems. It is utilized in a range of application fields, including as the filling of down regions, mine filling, bricks, and the partial replacement of cement in concrete building, in an effort to lessen the environmental concerns it poses. Numerous studies have been conducted to determine the best way to use FA as a substitute for cement and fine aggregate in concrete and how it affects the mechanical and structural performance of modified concrete. In this view, the current study provides an overview of prior experimental outcomes on the behavior of high content fly ash (HCFA) reinforced concrete (RC) beams under shear. The effect of partially replacing cement in concrete with HCFA ranging from 0% to 70% on several factors pertaining to beam resistance in shear, such as the ratio between shear span and depth of the beam, concrete mix proportions, and concrete reinforcement, has been evaluated. The results on the shear resistance capacity of RC beams under shear at first crack and failure load, as well as deflection at ultimate load, have been presented and discussed. According to the available test results, up to 50% FA can be employed efficiently in concrete construction with no substantial reduction in shear strength. A review of the literature reveals that there is limited data on the influence of HCFA on the shear strength of beams, and more experimental studies are required to understand the shear performance of reinforced concrete members containing FA at the ultimate load and the resulting failure criteria.

Shear Performance of GFRP Reinforced Concrete Beams with Seawater and Chopped Fiber

This paper reports an experimental study on the behavior and shear strength of concrete beams reinforced with longitudinal GFRP bars mixed with sea water. In order to evaluate how much concrete contributes to shear resistance, seven beams were tested in bending. Similar in size and concrete strength, the beams were longitudinally reinforced with glass fiber-reinforced polymer bars; however, they did not even have shear reinforcement. The beams, which measured 3,100 mm in length, 400 mm in depth, and 200 mm in width, were conducted and tested up to failure. The test variables were longitudinal reinforcement ratios (1.0, 1.4, and 2.0%), chopped fiber content (0, 0.5, 2, and 3 kg/m3), and mixing water type (freshwater and seawater). The test findings showed that increasing the reinforcement ratio increased the neutral-axis depth and allowed the formation of more closely spaced fractures while decreasing the loss of flexural stiffness after cracking. By increasing the area of concrete in compression, this in turn enhances the contribution of aggregate interlock as well as the contribution of uncracked concrete. Furthermore, increasing the reinforcement ratio improves the dowel action, which reduces the tensile stresses that are created in the concrete around it. Doi: 10.28991/CEJ-2023-09-04-05 Full Text: PDF

Effect of basalt minibars on the shear strength of BFRP-reinforced high-strength concrete beams

This research examined the effect of a novel type of stiff nonmetallic macro fibers, basalt macro fibers (BMF), on the shear strength and behavior of high-strength concrete (HSC) beams longitudinally reinforced with basalt fiber-reinforced polymer (BFRP) bars. Nine BFRP reinforced concrete beams (BFRP-RC) were cast and tested under two-point loads without BFRP stirrups. The volume fraction of BMF (Vf) and the BFRP longitudinal reinforcement ratio (ρf) were all evaluated; three volume fractions of BMF were used (0%; 0.75%; 1.5%), with three longitudinal reinforcement ratios (0.71%, 1.71%, and 2.54%). The results indicated that adding BMF to HSC-BFRP-RC beams enhanced the shear resistance. For concrete compressive strength of 90 MPa, the results show that adding BMF enhanced the shear resistance for all flexural reinforcement ratios; however, it is more effective in beams reinforced with a higher flexural reinforcement ratio. For beams reinforced with flexural reinforcement ratios of 0.71% and 2.54%, the shear resistance increased by 95% and 98%, respectively, due to adding 0.75% BMF, compared to 136% and 210% when 1.5% of BMF were added. And the post-cracking stiffness of all beams remarkably increased due to BMF addition. Furthermore, the effect of BMF on the mechanical properties of HSC was investigated. Finally, ACI 318–19, ACI 440.1R-15, and ACI 544.4R-18 were evaluated to predict the ultimate shear strength of HSC-BFRP-RC beams reinforced with BMF. The experimental results correspond well with ACI 544.4R-18 predictions of the shear strength of fiber-reinforced concrete (FRC) beams without shear reinforcement.

Effect of bond surface area on the shear strength of carbon fibre reinforced polymer (CFRP) strengthened reinforced concrete beams

The use of carbon fiber reinforced polymer (CFRP) for shear strengthening of reinforced concrete (RC) elements has grown significantly over the last few decades. The effectiveness of a CFRP strengthened system depends primarily on the bond strength between the CFRP and RC substrate. Since cost of CFRP and bond materials (epoxy adhesive) is relatively high, it is important to reduce CFRP bond surface area to RC when carrying out structural strengthening. This paper reports the effect of bond surface area on the shear strength of RC beams externally bonded with CFRP. Seven RC beams were investigated. One of the beam specimens was not strengthened and was used as a reference. The remaining six beams were strengthened with 200 g/m2 and 300 g/m2 CFRP fabrics with three different bond surface areas, i.e., 0.15 m2, 0.2 m2 and 0.25 m2 in a U-wrap configuration. Static bending tests were performed on all the beams. Results show that the CFRP's contribution to shear strength increases as the bond surface area increases. Results also show that the shear strength of the RC beam was increased by 45% due to the presence of CFRP. Fundamentally, this work presents a parametric study to guide engineers on how shear strength and corresponding ductility of beams can be increased with an optimal configuration of the bond surface between CFRP and RC elements. An optimal and cost-effective configuration is proposed for carrying out nominal shear strengthening of RC elements after construction, especially in cases where the construction engineer has concerns about the shear stirrups provided before casting.

Predictive Modeling of Recycled Aggregate Concrete Beam Shear Strength Using Explainable Ensemble Learning Methods

Construction and demolition waste (CDW) together with the pollution caused by the production of new concrete are increasingly becoming a burden on the environment. An appealing strategy from both an ecological and a financial point of view is to use construction and demolition waste in the production of recycled aggregate concrete (RAC). However, past studies have shown that the currently available code provisions can be unconservative in their predictions of the shear strength of RAC beams. The current study develops accurate predictive models for the shear strength of RAC beams based on a dataset of experimental results collected from the literature. The experimental database used in this study consists of full-scale four-point flexural tests. The recycled coarse aggregate (RCA) percentage, compressive strength (fc′), effective depth (d), width of the cross-section (b), ratio of shear span to effective depth (a/d), and ratio of longitudinal reinforcement (ρw) are the input features used in the model training. It is demonstrated that the proposed machine learning models outperform the existing code equations in the prediction of shear strength. State-of-the-art metrics of accuracy, such as the coefficient of determination (R2), mean absolute error, and root mean squared error, have been utilized to quantify the performances of the ensemble machine learning models. The most accurate predictions could be obtained from the XGBoost model, with an R2 score of 0.94 on the test set. Moreover, the impact of different input features on the machine learning model predictions is explained using the SHAP algorithm. Using individual conditional expectation plots, the variation of the model predictions with respect to different input features has been visualized.

Nonlinear FE Prediction of Shear Strength of RC T-Beams with Flange in Compression Zone

This research predicts the shear strength of reinforced concrete T-beams with flanges under compression stresses using nonlinear finite element (FE) analysis. A FE model is developed and verified against specially designed experiments (beams with varying flange width and depth). Results of the FE models were found to be in excellent agreement with their corresponding experimental results. The average load capacity and deflections (FE/experiments) ratios were 1.03 and 0.87, respectively. We then conducted an extensive parametric study to investigate the structural performance of flanged beams under the effect of two concentrated point loads. This parametric study examined four parameters: flange dimensions, longitudinal reinforcement in flange, concrete compressive strength, and shear span to depth ratio. Our findings indicated that the presence of compressed flanges in T-beams increases the shear strength by up to 260% of the shear strength of the web alone. The shear strength of flanged beams increases with the increase in flange dimensions, where the effect of flange thickness is more pronounced than that of flange width. Moreover, the presence of longitudinal reinforcement in the flange enhanced the beam's shear strength by up to 40%, compared to similar beams without flange reinforcement. Additionally, the shear strength increased up to three-fold for various beam conditions when the shear span to depth ratio was reduced from 2.0 to 0.5. These findings provide valuable insights for designing and constructing reinforced concrete T-beams with flanges under compression stresses.

Experimental Investigation on Shear Behavior of Dune Sand Reinforced Concrete Deep Beams

An experimental study on the shear behavior of dune sand reinforced concrete (DSRC) deep beams was conducted to determine the feasibility of using dune sand (DS) in engineering. Nine DSRC deep beams were designed and thoroughly analyzed for failure modes, diagonal cracks, and load–deflection curves in this study. The results showed that the shear strength and ductility of DSRC deep beams increased when the DS replacement rate was 30%, but the opposite effect occurred when the DS replacement rate was 50%. To analyze the differences in the effects of the DS replacement rate, shear span-to-depth ratio, concrete strength, and stirrup ratio on the shear strength of DSRC and normal reinforced concrete (NRC) deep beams, a total of 227 shear experimental tests of NRC deep beams were conducted. Furthermore, four national codes were evaluated and compared based on experimental data. The evaluation results showed that the four codes underestimated the shear strength of DSRC and NRC deep beams. Among them, ACI 318–11 provided more reliable predictions for both NRC and DSRC deep beams. It is in this regard that a new empirical model for predicting the shear strength of DSRC deep beams is proposed, in which a reduction coefficient of the DS replacement rate is incorporated. The verification results indicates that the predicted results of the proposed model are in good agreement with the experimental results.

Sustainability of Using Steel Fibers in Reinforced Concrete Deep Beams without Stirrups

Reinforced Concrete (RC) deep beams perform better structurally when steel fibers are added, as this reduces the need for web steel reinforcements, boosts shear strength, and helps to bridge cracks. The current ACI 318-19 code does not include predicting shear strength models to account for the added steel fibers in Steel Fibers Reinforced Concrete (SFRC) deep beams without stirrups; therefore, structural engineers are less motivated to use them. To fill this gap, the databases of 281 RC and 172 SFRC deep beams were compiled, and the preliminary investigation of the collected databases revealed that (1) Longitudinal steel reinforcement significantly increases the shear strength of SFRC specimens, as the steel fibers make deep beams better at carrying loads by assisting them in bridging cracks; and (2) Although shear stress and span-to-depth ratio are inversely related, SFRC deep beams encounter larger shear loads than RC deep beams because when the span-to-depth ratio of beams increases, the failure mode switches from crushing struts to diagonal shear failure. To help structural engineers adopt SFRC deep beams, a nonlinear regression-based model was developed to estimate the shear strength of SFRC deep beams using the experimental database of SFRC beams. Three factors—feature selection, data preprocessing, and model development—were considered. Additionally, the model’s effectiveness was evaluated and compared with other models found in the literature. The proposed shear strength model of SFRC performed better than the other models in the literature, providing the lowest Root Mean Square Error (RMSE) of 1.58 MPa. The results of this study give practitioners a strong platform for establishing precise and useful estimations of shear strength in SFRC deep beams without stirrups.

Finite element simulation on the effect of steel bars in concrete beams with & without web openings

Usage of service ducts in high rise buildings proves to be most reliable and these require an opening through the beams to manage the floor height of a structure. In the present study, a comparison is done between the beams with and without web openings. As web reinforcement and shear reinforcement are the major parameters influencing the shear strength of beams, these were analyzed using finite element modelling with the help of Abaqus software. The web openings provided at the center between the application of load and supports caused approximately about 40 % reduction in the beams capacity and so it can be avoided by provided additional wrapping with CFRP like materials around the openings. The capacity of beam is retained same at the places of beams without openings in both the cases. As the percentage of web reinforcement is increased, the ability to take load also increased along with the vertical displacement.

Ply level strain measurement in composites using digital image correlation

Application of digital image correlation technique for full field strain characterization has gained widespread interest. However, characterization of the strain and damage at very small length scales using this technique is still difficult and development in this regard would add a great value in understanding the material behavior, particularly for in-homogeneous materials such as composites. This article describes the work done primarily on continuous fiber reinforced polymer composites and ceramic matrix composites, towards applying the digital image correlation (DIC) technique at very small length scale, of the order of 100–200 μm. A novel technique was developed to generate a fine and randomly distributed speckle pattern. Actual examples of the application of digital image correlation at small length scales for strain and damage characterization are briefly described such as ply level strain measurement near ply drop in a fiber reinforced composite, local carbon-glass fiber joint strain measurement in hybrid composites, and short beam shear strength validation using ply level strain measurement in carbon glass fiber joints in hybrid composites. Limitations of digital image correlation technique for small scale strain measurement are also discussed.

Firefly Algorithm-Based Artificial Neural Network to Predict the Shear Strength in FRP-Reinforced Concrete Beams

The shear strength of fiber-reinforced polymer (FRP) reinforced concrete beams is often given a large safety margin by current construction requirements. Six characteristics are utilized as inputs to compute the shear strength of FRP-reinforced concrete beams. This study uses 198 samples from the literature to predict the shear strength of 139 training samples and 59 testing samples. Additionally, the ANN structure is optimized with the firefly algorithm. The FA-ANN model is also compared to ACI-440, CSA-S806, and BISE-99 codes, and the optimized model by Nehdi et al. Findings show that regarding the shear strength of FRP-reinforced concrete beams, the firefly algorithm-optimized model performs better than the other four models. Concerning accuracy, the coefficient of correlation, R2, was calculated as 0.961, while the average absolute error (AAE) is 0.22 for the shear strength of FRP-reinforced beams.

Shear strength of reinforced fly-ash-based geopolymer concrete beams with and without shear reinforcement

Geopolymer concrete (GPC) has attracted significant attention as an eco-friendly material and various studies have been conducted at the material level. However, studies on their structural properties are limited. In this study, shear tests were conducted on 10 fly-ash-based GPC beams subjected to double-curvature bending to understand the shear properties of GPC beams. The experimental parameters were the shear span ratio, concrete strength, and transverse shear reinforcement ratio. The shear cracking and ultimate shear strengths of GPC beams increase as the shear span ratio decreases. Existing design equations for shear cracking and shear ultimate strengths can be applied to GPC beams. The applicability and accuracy of existing shear ultimate strength evaluation equations, including existing experimental results, were verified, and the Architectural Institute of Japan design equation was found to be the most applicable, regardless of the loading arrangement and shear span ratio.

Experimental and numerical study on the effect of fibers on shear strength of basalt FRP-reinforced concrete beams without stirrup

Experimental and numerical study on the effect of fibers on shear strength of basalt FRP-reinforced concrete beams without stirrup

Size effect of large high strength concrete beams with or without shear reinforcement

This paper presents a research programme conducted on a total of seven large high strength concrete (HSC) beams, up to 1800 mm in depth and 13400 mm in span, with and without shear reinforcement in the web. This study investigates experimentally the effects of aggregate size, shear reinforcement ratio, and effective depth on shear strength of large HSC beams. In addition, the test data are adopted for verification of design provisions in ACI 318–14, ACI 318–19 and EN 1992–1-1. It is experimentally shown that reduction in ultimate shear strength was observed with increasing beam depth, regardless of the maximum aggregate size and web reinforcement ratio. A larger beam depth led to a more extensive development of flexural and shear cracks, as well as a greater beam stiffness. Besides, the use of smaller aggregates results in a higher ultimate strength (in MPa) for the beams. Shear equations from ACI 318–14 overestimated shear strengths of the large HSC beams without web reinforcement, while ACI 318–19 and EN 1992–1-1 predictions were more conservative by accounting for the size effect term. It is also shown that the existing codes of practice could not account for the effect of aggregate size when determining shear strength. Additionally, predictions by all the three codes for the HSC beams with minimum shear reinforcement were generally conservative.