Shear Strength Of Beams Research Articles

Numerical Analysis of Concrete Deep Beams Reinforced with Glass Fiber-Reinforced Polymer Bars

This research aimed to generate data through a verified numerical model. The data were subsequently used to introduce a simplified analytical expression for the prediction of the shear strength of concrete deep beams reinforced with glass fiber-reinforced polymer (GFRP) reinforcing bars. A three-dimensional (3D) numerical simulation model for a large-scale GFRP-reinforced concrete deep beam (300 × 1200 × 5000 mm) was developed and validated against published experimental data. A parametric study was then conducted to examine the effects of key variables on the behavior and shear strength of GFRP-reinforced concrete deep beams. Eighteen 3D numerical models were developed to study the interaction between the concrete compressive strength (f’c), the shear span-to-depth ratio (a/h), the spacing between web reinforcement (s), and the shear strength. The a/h value was either 1.0 or 1.5. The values of f’c were 28, 37, and 50 MPa. The spacings between the web reinforcement, if present, were 100 and 200 mm. The results of the parametric study indicated that the shear strength of deep beam models increased almost linearly with an increase in f’c and a decrease in the stirrup spacing irrespective of the value of a/h. The strength reduction caused by increasing a/h was more pronounced for the beam models with the lower f’c and greater stirrup spacing. The simplified analytical expression introduced in the present study provided a reasonable prediction for the shear strength of GFRP-reinforced concrete deep beams.

Shear span behavior of reinforced concrete beams with circular and rectangular openings

In reinforced concrete (RC) beams, opening is frequently required for the passage of utility ducts and/or pipes. The presence of such web openings leads to a reduction of the strength and sitffeness of the beam. This paper aims to contribute to better understanding the shear behavior of RC beam with web opening in the shear span. The study is based on an experimental program carried out on three RC beams. All beam specimens were 850 mm long with a cross section of 150 × 250 mm and a shear span to beam depth ratio (a/d) of 1.48. One beam without any openings served as the control specimen, while the remaining two beams featured circular or rectangular openings within the shear span. The test results obtained from this experimental program offer insights into the shear behavior of beams with openings. Furthermore, a simplified calculation was conducted to determine the shear strength of the test specimens, considering the influence of openings. This calculation provided a deeper understanding of how openings contribute to the reduction in the shear strength of RC beams.

Experimental study on the effect of different shear reinforcement shapes and arrangement on 3D crack propagation and shear failure mechanism in RC beams

Surface crack patterns have been considered to determine the shear failure behavior of RC beams but the effect of three-dimensional crack propagation on shear crack development and shear strength of RC beams is still debatable. In this study, a three-dimensional crack evaluation was carried out to investigate the shear failure mechanism of RC beams with different shapes and arrangement of shear reinforcement (close shape shear reinforcement, rod shape reinforcement with mechanical anchorage, combination of close shape reinforcement and rod shape reinforcement, rod shape reinforcement and transverse distribution rebar) using three-point loading test by keeping the amount of shear reinforcement same in all beams. The load displacement relationships, 3D crack maps, stirrup strain and concrete strain relationships with displacement and shear resistance components were discussed in detail and these information’s are useful for designing and shear strengthening of existing beams. It was clarified that a clear difference in three-dimensional cracking pattern, shear resistance components of concrete and shear reinforcement contribution was observed in RC beams. This was due to the shear reinforcement shapes and arrangement of rod shape reinforcement, which influenced the shear strength of RC beams.

A Theoretical Analysis of Shear Failure of a Reinforced Self- Compacting Concrete Beam without Shear Reinforcement was Experimentally Validated

Abstract: Failures linked with reinforced concrete include axial, flexural, shear, torsion, and deflection (as well as cracking in the case of RC as a material) beyond specified allowed limitations. Failure due to shear (including shear related with torsion) is the most severe and can occur with little or no notice. To prevent the brittle character of shear failure with little or no forewarning, an examination was conducted to determine the size effect, and the diagonal shear failure of reinforced concrete beams without stirrups was analysed in terms of the effective depth of the section. The test beams are made of two types of concrete: conventionally vibrated concrete and self-consolidating concrete, both having nominal compressive strengths of 30, 60, and 70 at 28 days. Given the limitations of the laboratory, three beam sizes with overall depths of 200, 300, and 500 mm, a set shear span to effective depth ratio (a/ d) of 3.50, and a constant tensile reinforcement content of 3% are utilised as test specimens. The experimental results were compared to the acceptable shear stress requirements of IS 456 and ACI 318. The size, concrete type, and concrete strength were determined to have a substantial influence on beam shear strength

Experimental and Computational Research on the Shear Performance of Partially Filled Narrow-Width Steel Box-UHPC-Combined Girders under Negative Moment Action

To examine the vertical shear behavior of narrow-width steel box-UHPC (Ultra-High Performance Concrete) composite beams with partial filling under negative bending moments, a total of six test beams were created and constructed in this study. The variables considered during the design and fabrication process included the flange thickness of UHPC, the amount of steel fibers in UHPC, and the height of the concrete filling. Reverse static concentration loading was applied to the beams. Compared to the C40 concrete flange, the cracking load of the 1/2 plate thickness UHPC flange and pure UHPC flange increased by 55.6% and 66.7%, respectively. The yield load witnessed a rise of 17.3% and 22.7%, while the ultimate load experienced an increase of 7% and 13.1%. This suggests that incorporating steel fibers can regulate the formation of cracks, enhance the flexibility of the flanges, and improve the overall shear capacity of the composite beams. When steel fibers were used in the amount of 2% of the concrete volume, the cracking resistance of the flange plate was increased by 16.7%. Partially filled and fully filled composite beams exhibited a 7.7% and 30.8% augmentation in cracking load, a 35.3% and 49.9% increase in yield load, and a 41% and 83.2% elevation in ultimate load when contrasted with composite beams devoid of concrete infusion within the steel box. The above observation implies that the incorporation of concrete within the steel box significantly improves the yield strength and ultimate shear capacity of the composite beams. The shear strength of the narrow-width steel box-UHPC composite beams, which are partially filled, shows a significant relationship with the experimental results when applying the principle of component superposition.

Shear strength and behavior of recycled strap fiber reinforced concrete haunched beams

Haunched beams also known as tapered beams, are structural elements characterized by varying depths along their span. In light of the growing concern over plastic accumulation on a global scale which provides a threat to the environment, and the challenges associated with employing traditional stirrups in the construction of tapered elements, this research paper has emerged.The approach of this study is divided into two main parts: an experimental phase and an analytical phase. In the initial stage, eight beams were subjected to testing to investigate the influence of tapered angles and the presence of recycled strap fiber on the shear behavior of tapered beams. The outcomes of this experimentation revealed a significant trend: as the tapered angles and fiber contents increased, the shear strength of haunched beams also increased.The subsequent phase of the study focused on an analytical examination, incorporating models proposed in various literature sources. A comprehensive database of 91 beams was assembled to facilitate the selection of the most suitable shear model for modification. Building upon the analysis of this extensive dataset, an equation was proposed. Additionally, a smaller dataset featuring tapered beams with fiber was collected to apply further analysis and demonstrate the models' performance. The predicted results by the proposed model showed good agreement with experimental test results.

Shear capacity prediction for FRCM-strengthened RC beams using Hybrid ReLU-Activated BPNN model

This study presents a robust Hybrid ReLU-Activated Backpropagation Neural Network (BPNN) model for predicting shear strength (VFRCM) in RC beams reinforced with Fiber-Reinforced Cementitious Matrix (FRCM) composites. The model demonstrates exceptional accuracy, boasting a coefficient of determination (R2) of 0.924 and a mean root mean squared error (RMSE) of 10.611 kN on the test set. Leveraging k-fold cross-validation, the model's performance is rigorously assessed across ten data splits, emphasizing its reliable predictive capabilities. Hyperparameter optimization, guided by Particle Swarm Optimization (PSO), reveals an optimal value of α = 0.5757, minimizing the Mean Squared Error (MSE) to 74.53 for the dataset. Feature importance analysis underscores the significant role of critical parameters in shear strength prediction, with the longitudinal reinforcement ratio (ρl) emerging as the most influential feature, contributing 37.55 % to the model's predictive power. Additionally, the effective beam depth (d) is identified as the second most influential feature, providing approximately 16 % to the accurate prediction of VFRCM in RC beams strengthened with FRCM composites.

A Simplified Approach to Determine Shear Strength for Corroded RC Beams

Corrosion damage, which can be considered as a construction or usage defect during the life of the structure, is an important parameter for the structural elements. Strength loss is observed in reinforced concrete (RC) elements due to corrosion, which is an important parameter affecting the performance of the building. Determining the shear strength of beams with corroded reinforcement is important in terms of strength loss, design, and reinforcement criteria in the structural element. In this context, the data of the corroded RC beam experimental tests carried out in the literature were collected and the ultimate shear strength values of the beams were determined depending on the test parameters. Strength estimation was performed with machine learning regression algorithms XGBoost and AdaBoost. The results obtained were evaluated with R2, RMSE and MAE performance metrics and high estimation success was achieved. The study shows that with these systems, which can perform learning based on experimental data, it is possible to estimate the shear strength values of corroded beams with known production parameters without the need for experimental measurements.

Effects of Concrete Strength and CFRP Cloth Ratio on the Shear Performance of CFRP Cloth Strengthened RC Beams

A three-dimensional meso-scale numerical model, which considers (1) the concrete meso-structures, (2) the bond slip between concrete internal structures and steel bars, and (3) the stripping behavior of carbon fiber reinforced polymer (CFRP) cloth from the surface of the concrete component, is established to investigate the effects of concrete strength and the CFRP cloth ratio on the shear performance of reinforced concrete (RC) beams. On the basis of verifying the rationality of the shear failure model and the feasibility of the CFRP reinforcement simulation method, 30 orthogonally designed numerical models of six kinds of concrete strength and five kinds of CFRP cloth ratios were designed and established. Based on the numerical simulation results, this paper analyzes the damage evolution of the CFRP–concrete interface, the variation trend of CFRP strain along the height direction of beam, the failure mode, and the load-displacement curve. Results show the following: (1) With the increase of concrete strength grade and CFRP cloth ratio, the shear strength of RC beams strengthened with increases in CFRP cloth ratio, the influence range of concrete strength grade is 24–50%, and the influence range of the CFRP cloth ratio is 10–25%; (2) The improvement range decreases, and the improvement range of the concrete strength grade decreases, and the reduction range is about 20%; (3) Based on the simulation results, influences of concrete strength and CFRP cloth ratio on the shear strength of CFRP cloth-strengthened RC beams are quantitatively considered.

Prediction of shear strength in UHPC beams using machine learning-based models and SHAP interpretation

To provide more accurate and reliable predictions of the shear strength of ultrahigh-performance concrete (UHPC) beams, in this study, the machine learning (ML) approaches were employed to develop the data-driven models, and the ML models were interpreted using the Shapley additive explanations (SHAP) method. It was found that the ensemble models, particularly CatBoost, outperform individual ML models and traditional empirical models. The geometric dimensions and shear span-to-depth ratio were the most influential features for predicting the shear strength of UHPC beams, followed by the parameters of reinforcement and material properties of the UHPC.

Shear behavior of normal and high-strength concrete beams reinforced with BFRP bars and basalt macro fibers

This research examined the effect of basalt macro fibers (BMF) on the shear resistance and behavior of normal-strength concrete (NSC) and high-strength concrete (HSC) beams longitudinally reinforced with basalt fiber reinforced polymer (BFRP) bars. Twelve BFRP reinforced concrete beams (BFRP-RC) were cast and tested under two-point loads without and with BFRP stirrups. The volume fraction (Vf) of BMF, the concrete compressive strength (fc′), and the existence of stirrups were all evaluated. Three volume fractions of BMF were used (0 %, 0.75 %, 1.5 %) with three classes of concrete compressive strength (30 MPa, 60 MPa, and 90 MPa). The results indicated that adding BMF to BFRP-reinforced-NSC and BFRP-reinforced-HSC beams enhanced the shear resistance. For concrete classes C30 and C90 shear resistance increased by 38 % and 62 %, respectively, when 0.75 % BMF was added, and it increased by 81 % and 115 %, respectively, when 1.5 % BMF was added. The enhancement of shear resistance due to the addition of BMF to BFRP-reinforced-HSC beams was more effective than in BFRP-Reinforced-NSC beams. Moreover, the addition of BMF enhanced the shear resistance of both stirrup-equipped and stirrup-less beams. The experimental results showed that the shear resistance of BFRP-reinforced-HSC beams due to the addition of BMF rather than stirrups is comparable, if not better, with stirrups. For instance, the shear resistance of the beam reinforced with 1.5 % of BMF is more than the beam reinforced with the minimum amount of BFRP stirrups. The post-cracking stiffness of all beams remarkably increased due to BMF addition. Furthermore, the effect of novel BMF on the mechanical properties of concrete was investigated.Additionally, an equation to predict the concrete contribution to the shear strength of FRP-RC beams was developed using the experimental shear strength of the tested beams and the experimental results of 217 FRP-RC beams without stirrups published in the literature. The proposed equation is combined with Said et al. and Narayanan and Darwish expressions to account for FRP stirrups and BMF contributions in the shear strength of the BFRP-RC beams. The mean value and the coefficient of variance (CV) of experimental to predicted ultimate shear strength of the beams (Vu,exp/Vu,pred) were 1.12 and 7.64%, respectively.

Long‐term residual properties and durability of glass fiber reinforced polymer composite exposed to alkaline solution and natural weather for a decade

AbstractThis paper presents a comprehensive analysis of the residual thermal and mechanical properties of pultruded glass fiber reinforced polymer composite bars following a decade of conditioning in an alkaline solution and exposure to natural weather conditions. The study focuses on evaluating the changes in the glass transition temperature (Tg) of the polymer matrix and its impact on the bar's mechanical performance. The results indicate that the Tg retained approximately 94.7% of its initial value, with the decrease attributed to the plasticizing effect of absorbed water. The flexural modulus, flexural strength, and transverse shear strength were found to retain 91.8%, 77.2%, and 97.3% of their original values, respectively. The reductions in strength and stiffness were primarily attributed to a weakening in the bonding between the fibers and the polymer matrix. Fractographic analysis revealed that the failure of the plasticized and softened polymer matrix contributed to the observed reductions in strength. Interestingly, the short beam shear strength remained relatively unchanged, as the diffusion of water into the core of the bars at ambient temperatures had a minimal effect. This slower water diffusion in the core led to insignificant degradation of the short beam shear strength.Highlights GFRP bars were exposed to alkaline solution and natural weather for a decade. The water diffusion was a key factor in determining the residual properties of the GFRP bars. The absorbed water plasticises the polymer matrix in GFRP bars. The mechanical property losses in GFRP bars were associated with changes in the matrix property.

Shear strength assessment of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques

This paper presents a study to predict the shear strength of reinforced recycled aggregate concrete beams without stirrups using soft computing techniques. The methodology involves the development of a Multi-Objective Genetic Algorithm Evolutionary Polynomial Regression (MOGA-EPR) and Gene Expression Programming (GEP) models. The input variables considered are the longitudinal reinforcement ratio, recycled coarse aggregate ratio, beam cross-section dimensions, and concrete compressive strength. Data collected from the literature were used to train and validate the models. The results showed that the MOGA-EPR and GEP models can accurately predict the shear strength of beams without stirrups. The models also performed better than equations from the codes and literature. This study provides an alternative approach to accurately predict the shear strength of reinforced recycled aggregate concrete beams without stirrups.

Design oriented shear strength prediction model of UHPFRC beams

The application of Ultra-high performance fiber reinforced concrete (UHPFRC) to beams can effectively improve their strength and ductility properties. At present, there are more shear test studies on UHPFRC beams, but few theoretical studies on their strength calculation method. The fibers in UHPFRC can inhance the strength of the beams, and how to quantify the fibers’ contribution to the total strength is a research difficulty. Firstly, the shear strength database including 197 UHPFRC beam specimens is created. The key characteristics determining the shear strength, namely fiber volume fraction, stirrup configuration, and shear-span ratio, are determined by evaluating the test results. Then, considering the effects of the factors above, a truss-arch model is developed. The arch shear strength is determined by utilizing the shear deformation coordination of the arch compression structs and truss compression structs. Superimposing the shear strengths of the arch and truss models yields the shear strength of UHPFRC beams. Finally, the accuracy and reliability of the proposed method are assessed using the test database and compared to existing shear strength calculations. The study findings demonstrate that the mean value of the calculated to observed shear strength ratios produced by the suggested technique is 1.10, with a coefficient of variation of 0.28, indicating that the proposed method has superior calculation accuracy and reliability than existing methods.

Shear performance of partially encased composite beams with high-strength steel and UHPC

Various countries across the globe have implemented strategic policies that encourage the development and use of high-performance materials in engineering structures. This study focused on the shear performance of partially encased composite beams with high-strength steel and UHPC (PEHC beams). Seven PEHC beams under simply supported three-point loading conditions were tested to investigate the effect of main parameters such as the shear span-depth ratio, thickness of steel web, types of steel web, and types of stirrups on shear failure modes, crack patterns, load-deflection behavior, and the response of strains. Two failure modes were observed in PEHC beams, involving diagonal compression failure and shear compression failure. As the shear span-depth ratio decreased from 2.0 to 1.5 and 1.0, the shear strength increased significantly by 13.3% and 70.8%, respectively. Test results demonstrated that the increase in the thickness of steel web and the use of steel solid web positively affect the shear strength of PEHC beams, whereas the impact of normal-strength stirrups types proved insignificant. Finally, a theoretical method was proposed to predict the shear strength of PEHC beams by considering the coworking of four mechanisms: the composite truss, the steel web, the stirrups, and the steel fibres. Then the method was validated by the good agreement between the predicted and tested shear strength results, highlighting its applicability in providing accuracy.

Prediction of shear strength of RC slender beams based on interpretable machine learning

The purpose of this paper is to explore a prediction model of the shear strength of reinforced concrete (RC) slender beams driven by experimental data and mechanisms. An experimental database containing 774 shear test specimens of RC slender beams with and without stirrups is collected. According to that, six machine learning models are applied and compared, and the interpretability of these models is further explored. Through the comparison of six machine learning models, namely k-nearest neighbor, decision tree, random forests, Adaboost, gradient boosting decision tree, and XGBoost, the XGBoost model performs well in prediction accuracy (R2 = 0.999 and 0.953 in the training set and testing set). The XGBoost model is also significantly better than the mechanism-driven model in terms of prediction accuracy and variance, which is selected for further interpretation analysis. The interpretation analysis consists of three steps: (i) to identify the importance of features, (ii) to analyze feature dependence, (iii) to explore a novel interpretable idea combined the shear mechanism and SHapley Additive exPlanations (SHAP) method. It is the novel interpretable idea that the shear contribution distribution of the RC slender beam obtained by the SHAP value is further discussed. Through the comparison and discussion of the two mechanism models, the results of interpretable analysis indicate that the contribution distribution of the model conforms to the mechanism. Finally, the proposed interpretable approach is feasible and the developed XGBoost model is recommended for predicting the shear strength of RC beams and even similar questions.

Development of Prediction Models for Shear Strength of FRP Reinforced Fibrous Concrete Beams without Stirrups Using ANN and Nonlinear Regression

ABSTRACT The shear behaviour of fibre-reinforced polymer reinforced concrete (FRP-RC) beams without web reinforcement suffers from low strength, low stiffness, more brittleness and wide and quick propagated cracks. Fortunately, the addition of various types of fibres could improve most of these weaknesses. On the other hand, the shear strength prediction of FRP-RC beams with various types of fibres is one of the most complex cases in structural engineering applications. Developing generalised, precise and consistent prediction models are necessary and very limited. This paper investigates the impacts of various types of fibres on shear strength and presents proposing four new prediction models, utilising artificial neural networks and empirical nonlinear regression analysis, and modifying the combination of available models based on a collected database of 49 shear test results of FRP-RC members with various types of fibres. The comparison of the developed models with the available equations from the literature indicates that the developed models yielded excellent performance, great efficiency and a high level of accuracy over all other existing models. Additionally, the parametric study confirmed that all the developed models have great abilities to accurately predict the actual response of each parameter, in spite of its complexity, on the shear strength of FRP-reinforced fibrous concrete beams without stirrups.

Influence of Fiber Orientation on the Shear Properties of Steel Fiber-Reinforced Reactive Powder Concrete Beams

Steel fiber-reinforced reactive powder concrete (SFRPC) has good mechanical properties and is thus useful for engineering applications. Generally, the fibers in SFRPC are randomly distributed. To study the effects of the amount, length, and orientation of steel fibers on the shear strength of SFRPC beams, nonreinforced aligned steel fiber-reinforced reactive powder concrete (ASFRPC) beams were prepared using a large-scale electromagnetic field orientation device. Shear tests of specimens with a shear span ratio of 1.5 were performed under two fiber contents (1.0% and 2.0%) and three fiber lengths (20, 30, and 40 mm). The full-field strain during the loading process was obtained via the digital image correlation method, and the effects of fiber quantity and orientation on the shear strength of the beams were analyzed. The ASFRPC beams exhibited higher ductility but lower shear capacity than the SFRPC beams under the same conditions. The shear capacity increased with the quantity of steel fibers. According to the test results, the calculation formulas of the shear-bearing capacities of ASFRPC and SFRPC beams were established.

Shear behavior of self-consolidating concrete deep beams reinforced with hybrid of steel and GFRP bars

This paper experimentally investigates the dowel action contribution of longitudinal reinforcement, a hybrid of steel and GFRP bars (usually adopted for avoiding corrosion of steel bars), in concrete deep beams for different reinforcement schemes. The self-consolidating concrete without and with steel fibers (0.9 % and 1.2 % by volume) was used. Sixteen simply supported deep beams of 140 × 300 (depth) × 1500 mm were tested under the two-point loads. A model is developed to predict the shear capacity of deep beams without web reinforcement, taking into consideration the dowel action contribution of longitudinal reinforcement, which is a hybrid of steel and GFRP rebars. The results showed a significant dowel action contribution of steel as well as GFRP rebars to beam shear strength. However, the use of a hybrid system of steel and GFRP bars caused a reduction in the dowel action of steel bars due to their combined action.

Prediction of shear behavior of glass FRP bars-reinforced ultra-highperformance concrete I-shaped beams using machine learning

This study focuses on using various machine learning (ML) models to evaluate the shear behaviors of ultra-high-performance concrete (UHPC) beams reinforced with glass fiber-reinforced polymer (GFRP) bars. The main objective of the study is to predict the shear strength of UHPC beams reinforced with GFRP bars using ML models. We use four different ML models: support vector machine (SVM), artificial neural network (ANN), random forest (R.F.), and extreme gradient boosting (XGBoost). The experimental database used in the study is acquired from various literature sources and comprises 54 test observations with 11 input features. These input features are likely parameters related to the composition, geometry, and properties of the UHPC beams and GFRP bars. To ensure the ML models' generalizability and scalability, random search methods are utilized to tune the hyperparameters of the algorithms. This tuning process helps improve the performance of the models when predicting the shear strength. The study uses the ACI318M-14 and Eurocode 2 standard building codes to predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams. The ML models' predictions are compared to the results obtained from these building code standards. According to the findings, the XGBoost model demonstrates the highest predictive test performance among the investigated ML models. The study employs the SHAP (SHapley Additive exPlanations) analysis to assess the significance of each input parameter in the ML models' predictive capabilities. A Taylor diagram is used to statistically compare the accuracy of the ML models. This study concludes that ML models, particularly XGBoost, can effectively predict the shear capacity behavior of GFRP bars-reinforced UHPC I-shaped beams.