Shear Strength Of Beams Research Articles

A new robust equation for shear strength of GFRP-RC deep beams using hybrid experimental and synthetic data based-FE quasi-static analysis procedure

The main objective of this article is to propose a new, more robust, and comprehensive design equation for Glass-Fiber-Reinforced-Polymer (GFRP) reinforced concrete (RC) Deep Beams (DBs). The investigation is based on a numerical model using Quasi-Static Two-Dimensional FE simulation of GFRP-RC DBs. The numerical model is validated by calibrating four parameters (concrete dilatation angle ψ, concrete tensile strength fct, fracture energy Gf, mesh size) against data from ten full-scale experiments on GFRP-RC DBs available in the literature. Additionally, a numerical parametric study based on a full factorial design with 5 factors (and between 2 and 5 modalities) is conducted using the validated quasi-static FE model, which includes 360 large-scale GFRP-RC DBs. The impacts of primary design variables on the concrete contribution Vc to the shear capacity of GFRP-RC DBs are assessed. The effects of various geometrical and mechanical variables, such as cross-sectional dimensions (width b × height H), concrete compressive strength grade (fc), GFRP properties (modulus Ef, and strength ffu), GFRP reinforcement ratio (ρf), and shear span to depth ratios (a/df), are examined to understand the shear behavior of GFRP-RC DBs.The numerical simulations demonstrate that the proposed FE model accurately captures the behavior of GFRP-RC DBs. Building upon these findings, a new shear design equation is proposed for DBs, and its accuracy and robustness are compared with five established international design codes (ACI-440.1R-15, ISIS-Canada, CAN/CSA-S806-12, JSCE-1997, and BS-8110). The existing design codes significantly underestimate the concrete contribution to shear capacity, leading to uneconomical designs. In contrast, the new design equation provides a fair prediction of Vc, with a standard error of 7.734 % and 20.84 % compared to the FE and experimental datasets, respectively. This represents a substantial improvement over the existing design guides.

Theoretical modeling for predicting the shear strength of partially-encased composite members with multiple transverse stiffeners

Partially encased composite (PEC) members are frequently used as girders in heavily-loaded or long-span structures. In PEC members, the steel web is encased in web concrete between the top and bottom flanges. When PEC members are used in continuous beam spans, the intermediate support area is subjected to significant shear force and negative bending, which is more critical than the span center. As a result, accurate prediction of shear strength is crucial for structural safety in practical design. Unlike traditional concrete or steel beams, PEC beams have multiple shear transfer mechanisms consisting of concrete, steel flanges, axial steel web, and transverse stiffeners (if necessary), and it is essential to investigate the shear contributions of the different mechanisms and how they work together as a composite structure. To solve this problem, this paper presents a novel shear strength model, in which the global shear strength is composed of two main components: the composite truss (steel flanges, web concrete, and transverse stiffeners) and the axial steel web, and these two components are unified by strain compatibility to show the composite action. Then, the proposed model is compared with existing shear equations using 25 existing specimens in the literature, and the results indicate that the proposed model can predict the shear strength of PEC beams with sufficient accuracy. Finally, the paper presents a simplified design method after validating the shear contributions of different anti-shear mechanisms, and the simplified method can facilitate practical design in engineering practices.

Shear Strength of SFRC Beams Without Coarse Aggregate Using Finite Element Analysis with Bond-Slip

Shear Strength of SFRC Beams Without Coarse Aggregate Using Finite Element Analysis with Bond-Slip

Analysis of the shear behavior of reinforced recycled aggregate concrete beams based on shear transfer mechanisms

This study aims to contribute to a more rational understanding of the shear behavior of reinforced recycled (concrete) aggregate concrete beams. Twelve reinforced concrete beams - six with natural aggregate concrete (NAC) and six with 100% coarse recycled concrete aggregates (RAC) - with same concrete compressive strength (38 MPa) and proportions of cement and aggregates per volume, varying the longitudinal (1.15%, 1.75%, and 2.50%) and transverse (0%, 0.086%, and 0.114%) reinforcement ratios, were tested under a 4-point bending setup with a full-field displacement monitoring. A thorough examination of the failure span along the tests, associating critical crack shape and kinematics with an investigation of the shear transfer mechanisms provided by mechanical models from the literature was conducted. Direct shear tests on push-off specimens followed by analyses of their failure surface evaluated the characteristics of shear crack surfaces and a proper modeling of the aggregate interlock contribution. The results showed a 28% average reduction in the shear strength of RAC beams with no stirrups in comparison with their reference beams (NAC), whereas beams with stirrups of same characteristics showed a similar shear strength, regardless of the type of coarse aggregate. The evaluation of the shear transfer mechanisms enabled an understanding of the reasons for those behaviors. Experimental results were also compared with those predicted in design codes. They were conservative for beams with stirrups, and, in general, less accurate than those obtained by the shear transfer mechanisms.

Shear Behavior of Reinforced Concrete Beams with Different Arrangements of Externally Bonded Carbon Fiber-Reinforced Polymer

Carbonfiber-reinforced polymer (CFRP) composites are frequently utilized in the repair and reinforcement of reinforced concrete members because they are more corrosion-resistant, durable, and flexible than commonly used materials like steel plates. Many studies have demonstrated that the use of CFRP as an internal strengthening material has a substantial impact on the shear capacity of RC beams. This research looks into the possibility of using FRP material as an externally bonded reinforcement to increase the shear strength of RC beams. Because shear improvement and mode of failure are based on the right configuration of the CFRP sheets, the experimental work consisted of eight RC beams with various types, orientations, and configurations of CFRP sheets. All beams were examined under two-point bending. The test results show that the CFRP sheet significantly improved the beam’s shear strength and ductility and that it is advantageous to orient the FRP at 45 degrees to the beam’s longitudinal axis.

Model analysis of carbon fiber reinforcement properties for reinforced concrete beams to resist blast loads

Safety is paramount in Oil & Gas plants, and continuous monitoring and improvements ensure that all measures are taken to protect them. The purpose of this paper is to examine how composite materials can be used to improve the structural reinforcement of concrete beams. Concrete structural beams have been improved in the past by using varying Fiber Reinforced Properties (FRP). It has been investigated how Carbon Fiber Reinforced Properties (CFRP) composites perform under blast loads and how they behave, respond, and perform as reinforcement for reinforced concrete beams. The response of RC beams to blasts was analyzed using a software modelling program called ANSYS that can mimic RC beam properties when reinforced with CFRP in concrete structures. The reason CFRP was chosen was because its properties showed great potential and it is well suited for testing and analysis. As well as absorbing a lot of energy, this material is strong, elastomeric, and alkali-resistant. A numerical analysis and model analysis have been performed with the help of the ANSYS software program. In the experimental results, CFRP was found to increase the flexural and shear strength of RC beams. The RC beams reinforced with CFRP has outperformed RC beam (control beam) in factors such as in Deformation, Equivalent Stress, and Shear Stress by a minimum percentage difference of 0.784% and maximum of 7.09% depending on the layers of CFRP and load applied on the beams in each factor.

Maximum shear strength of concrete-encased steel composite beams: A novel strength model and FE verification

Due to the high load-carrying capacity and rigidity, concrete-encased steel (CES) beams are widely applied as transfer elements in heavily-loaded structures. However, the accurate shear strength prediction method is still under investigation because of the difficulty in the shear contribution decomposition of steel shape and reinforced concrete. Current design codes apply the strength superposition for CES beams, namely that their shear strength can be obtained by directly superimposing the shear strengths of RC and steel shape, which has been proven questionable by existing research. Therefore, it needs to carefully decouple the multiple shear transfer mechanisms in CES beams and explore their roles in a composite structure. In this paper, a novel shear strength model is proposed, in which a CES beam can be divided into a shear web and a composite truss consisting of RC with two steel flanges, and strain compatibility is applied to evaluate the shear contribution of these two parts. After that, an elaborate FE model is established to further quantitatively investigate the shear contribution of the composite truss and the shear web. Finally, the proposed model is verified with 61 existing specimens in the literature, and the comparison between the calculated and test results indicates that the proposed model could predict the shear strength of CES beams accurately.

A comparative study of shear strength prediction models for SFRC deep beams without stirrups using Machine learning algorithms

This study aims to evaluate the shear strength of stirrups-free Steel Fiber Reinforced Concrete (SFRC) deep beams and to predict their shear strength values using Machine Learning (ML) algorithms. A database of 172 tested SFRC deep beam specimens was compiled and processed, and the results were analyzed using Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and Gene Expression (GEP) algorithms. The LightGBM and XGBoost algorithms were the most accurate, with R2 values of 97.8% and 94.5%, respectively. The authors proposed a closed-form GEP-based model to predict the shear strength of SFRC deep beams, with an R2 value of 78.9%. The analysis results indicate that the GEP model accurately predicts the impacts of concrete strength, flexural steel percentage, and the ratio of shear span to beam depth. The findings of this study provide practitioners with a solid foundation for making accurate and practical assessments of shear strength in stirrups-free SFRC deep beams.

Shear strength behavior of high-strength fly ash concrete beams with low-bond reinforcement

This study aims to investigate the shear behavior of high-strength fly ash (HSFA) concrete beams with low-bond reinforcement (LBR). With this purpose, twenty-three HSFA concrete beam specimens were constructed and tested. The experimental results showed that owing to the high-strength property of LBR, the tested beams easily failed in shear. The shear strength of the HSFA beams was mainly controlled by the shear span to depth ratio (a/d) and transverse reinforcement ratio. The shear strength Vs carried by the transverse reinforcement started to increase when the shear strength Vc carried by the concrete started reducing or growing at a lower rate. Formulas in design provisions, except for strut-and-tie model (STM) and those in the “AIJ standard for structural calculation of reinforced concrete structures” proposed by Architectural Institute of Japan (AIJ), could not accurately assess the shear strength of HSFA concrete beams because they do not reasonably consider the effect of a/d on the Vc. Generally, the current design models underestimate the Vs. Based on the test results, a new model that can reasonably consider the effect of a/d on Vc was proposed to accurately evaluate the shear strength of HSFA concrete beams reinforced by LBRs. This study deepens the understanding of researchers and engineers of the shear behavior of HSFA concrete beams with LBRs.

External Shear Strengthening of RC Beams with Anchored Geopolymer Fiber Mortar Panel: An Experimental Study

Failure due to shear deficiency in reinforced concrete (RC) structures, particularly RC beams, is one of the most common problems encountered in RC structures. As a result, the effectiveness of using externally anchored geopolymer fiber mortar panels were investigated in the scope of this experimental study. The variable of the test programmed herein was the change in anchor bolt spacing. Two strengthened beams with 200 mm and 300 mm anchor bolt spacings, respectively, were tested. In addition, one un-strengthened beam was prepared as the reference beam to compared the load-deflection behavior, shear capacity, and failure mode of the strengthened beams. All the beams were tested under monotonic loading until reached the failure load. The experimental results of all strengthening beams showed an increase in the shear strength of the RC beams by 34.9% (spacing 200 mm) and 22.8% (spacing of 300 mm) compared to the reference beam. In addition to increasing shear capacity, the failure mode of the strengthened beam with 200 mm anchors spacing changed from brittle shear failure to ductile flexural failure with increased stiffness. This indicated that geopolymer fiber mortar panel is considered a promising technique that could be utilized to repair and rehabilitate shear deficient RC beams.

Experimental Study on Shear Behavior of Non-Stirrup Ultra-High Performance Concrete Beams.

Due to the high tensile strength of ultra-high performance concrete (UHPC), the shear stirrups in UHPC beams could potentially be removed. The aim of this study is to assess the shear performance of non-stirrup UHPC beams. Six UHPC beams were tested and compared with three stirrup-reinforced normal concrete (NC) beams, taking into consideration the testing parameters of steel fiber volume content and shear span-to-depth ratio. The findings demonstrated that incorporating steel fibers can efficiently strengthen the ductility, cracking strength, and shear strength of non-stirrup UHPC beams and alter their failure mode. Additionally, the shear span-to-depth ratio had a significant impact on the shear strength of beams, as it was negatively related to it. This study revealed that the French Standard and PCI-2021 formulae were suitable for designing UHPC beams with 2% steel fibers and no stirrups. When applying Xu's formulae for non-stirrup UHPC beams, taking into account a reduction factor was necessary.

Comparative study on the performance of different machine learning techniques to predict the shear strength of RC deep beams: Model selection and industry implications

This study presents a comprehensive and rigorous process to develop the most appropriate machine learning (ML) model for predicting the shear strength of RC deep beams (RCDBs). The process consists of the crucial stages and state-of-the-art techniques of ML, including the development of ML models, selection of input features using Shapley Additive explanations, optimisation of the training process, assessment of data randomness, comparisons to the conventional practice codes, and development of novel web-based design platform based on the proposed ML model. For this purpose, seven machine learning models, i.e., linear regression, artificial neural networks (ANN), support vector machines, decision trees, ensemble of trees (EoT), extreme gradient boosting (XGBoost), and Gaussian process regression (GPR) were developed to predict the shear strength of RC deep beams based on a database of 518 samples with 15 input features. The four best models (i.e., ANN, EoT, XGBoost, and GPR) were then considered to assess the influence of varying the number of input features on the prediction performance. The results proved that GPR is the most reliable and accurate ML model. In addition, a set of nine optimal input features is proposed for predicting the shear strength of RCDBs. It was observed that randomly dividing the dataset into training and testing sets can significantly impact the predicted results. In some cases, the R2 value dropped to under 0.78, highlighting the importance of carefully considering the methodology for dividing the dataset when conducting machine learning experiments. The shear strength predicted by ML models was then compared with the three most prominent practice codes (i.e., ACI318, EC2, CSA 23.3-04), which indicated ML approach is highly reliable and accurate over conventional methods. In addition, the study used the Monte Carlo method to evaluate the robustness of the machine learning models and developed a user-interface platform to facilitate the practical application of the proposed machine learning model.

Shear Contribution of GFRP Reinforced Concrete Beams Mixed with Seawater, Freshwater, and Chopped Glass Fiber

The behavior and shear strength of concrete beams reinforced with longitudinal GFRP bars mixed with seawater are the subject matter of experimental research discussed in this paper. Seven beams were tested in bending to determine how much concrete contributes to shear resistance. The beams were longitudinally reinforced with glass fiber-reinforced polymer bars and were similar in size and concrete strength; however, they were not even shear-reinforced. The 3,100 mm long, 400 mm deep, and 200 mm wide beams underwent thorough testing until the failure. The test parameters included chopped fiber content (0, 0.5, 2, and 3 kg/m3), longitudinal reinforcement ratios (1.0, 1.4, and 2.0%), and mixing water type (freshwater and seawater). The test findings showed that Increasing the reinforcement ratio raised the neutral-axis depth, enabled the formation of more closely spaced cracks, and minimized the loss of flexural stiffness after cracking, according to the test results. The contribution of aggregate interlock as well as the contribution of uncracked concrete are both enhanced through increasing the area of concrete in compression. Additionally, increasing the reinforcement ratio enhances the dowel action, which reduces the tensile stresses produced in the surrounding concrete. Keywords: Seawater; Chopped Fiber; Fiber-Reinforced Polymer (FRP); Shear Strength.

Experimental Study and Numerical Modelling on Flexural and Shear Strengthening of RC Beam with Prestressed FRP Bars

Abstract: The most prevalent and widely used building material in the world is reinforced concrete. The primary building material for the majority of constructions, including buildings, bridges, etc., is reinforced concrete. Because of various factors, some of these buildings or portions of them are failing to perform as designed. The best solution to increase the load carrying capacity and extend the useful life of these inadequate structures is to strengthen them because replacing them would need significant investments and not be an appealing alternative. Even though the effectiveness of other techniques is widely accepted,a new effective and promising technique of strengthening civil engineering structures externally is gaining popularity, where fibre-reinforced polymer (FRP) is used. One of the most popular strengthening methods recently is the use of carbon fibre reinforced polymer (CFRP) to strengthen reinforced concrete beams. It provides a desirable way to improve the shear and flexural strengths of RC beams. The manner in which these composites are bonded to the beam has a significant impact on the behaviour of reinforced concrete beams in shear and flexure. The purpose of this study was to present a three-dimensional nonlinear finite element analysis (FEA) of a reinforced concrete beam (RC beam) reinforced by prestressed carbon fibre plate (CFRP plate). While there are many commercial programmes available for three-dimensional nonlinear FEA, each one has a unique ability to represent complex behaviour of composite materials, such as RC beams strengthened with prestressed CFRP plate and the contact interaction. Because of its reputation for accuracy in simulating the behaviour of a range of materials, including concrete, and its potent contact algorithms, the ABAQUS finite element programme was chosen for this work. Steel reinforcements were modelled as elastic- perfectly plastic materials, while concrete was studied using the concrete damage plasticity (CDP) constitutive model. The CFRP plate was modelled as an entirely elastic substance that breaks under the highest tensile strain. Concrete and steel reinforcement were considered to have a perfect connection, and the behaviour of concrete and CFRP plate was represented using a contact model. The FEA's findings were verified against experiment findings that were published in the literature. In terms of load-deflection behaviour, crack patterns, and mode of failure, the results were compared. The proposed FEA model was able to accurately simulate the behaviour of RC beams strengthened with prestressed CFRP plate based on the validation. To find out the impact of the thickness, width, and length of the CFRP plate, as well as the prestressing levels and steel grades, a parametric research was done. In particular, the strength of the RC beam for the ultimate load was shown to be increased by increasing prestressing of the CFRP plate. However, as prestressing increased, the fracture of the CFRP plate altered the mechanism of failure, preventing the ultimate load from increasing further. The use of higher grade steel improved the ability to support additional weight. Although the general load carrying capability of the CFRP plate was increased by lengthening the plate, it was discovered that strengthening RC beams by 25% of their shear span was sufficient and cost- effective. The load-deflection curve, failure modes, and crack patterns from the experiments are fairly reflected in the FE model.

Metaheuristic Optimization of Random Forest for Predicting Punch Shear Strength of FRP-Reinforced Concrete Beams.

Predicting the punching shear strength (PSS) of fiber-reinforced polymer reinforced concrete (FRP-RC) beams is a critical task in the design and assessment of reinforced concrete structures. This study utilized three meta-heuristic optimization algorithms, namely ant lion optimizer (ALO), moth flame optimizer (MFO), and salp swarm algorithm (SSA), to select the optimal hyperparameters of the random forest (RF) model for predicting the punching shear strength (PSS) of FRP-RC beams. Seven features of FRP-RC beams were considered as inputs parameters, including types of column section (TCS), cross-sectional area of the column (CAC), slab's effective depth (SED), span-depth ratio (SDR), compressive strength of concrete (CSC), yield strength of reinforcement (YSR), and reinforcement ratio (RR). The results indicate that the ALO-RF model with a population size of 100 has the best prediction performance among all models, with MAE of 25.0525, MAPE of 6.5696, R2 of 0.9820, and RMSE of 59.9677 in the training phase, and MAE of 52.5601, MAPE of 15.5083, R2 of 0.941, and RMSE of 101.6494 in the testing phase. The slab's effective depth (SED) has the largest contribution to predicting the PSS, which means that adjusting SED can effectively control the PSS. Furthermore, the hybrid machine learning model optimized by metaheuristic algorithms outperforms traditional models in terms of prediction accuracy and error control.

The Aging Behavior and Life Prediction of CFRP Rods under a Hygrothermal Environment.

Carbon fiber-reinforced polymer (CFRP) composites have been widely used in civil engineering structures due to their excellent mechanical and durability properties. The harsh service environment of civil engineering leads to significant degradation of the thermal and mechanical performances of CFRP, which then reduces its service reliability, service safety, and life. Research on the durability of CFRP is urgently needed to understand the long-term performance degradation mechanism. In the present study, the hygrothermal aging behavior of CFRP rods was investigated experimentally through immersion in distilled water for 360 days. The water absorption and diffusion behavior, the evolution rules of short beam shear strength (SBSS), and dynamic thermal mechanical properties were obtained to investigate the hygrothermal resistance of CFRP rods. The research results show that the water absorption behavior conforms to Fick's model. The ingression of water molecules leads to a significant decrease in SBSS and glass transition temperature (Tg). This is attributed to the plasticization effect of the resin matrix and interfacial debonding. Furthermore, the Arrhenius equation was used to predict the long-term life of SBSS in the actual service environment based on the time-temperature equivalence theory, obtaining a stable strength retention of SBSS of 72.78%, which was meaningful to provide a design guideline for the long-term durability of CFRP rods.

Analytical truss model for concrete beams reinforced with FRP bars

A truss model supplementing the concrete contribution is introduced in this paper to predict the shear strength of concrete beams reinforced with various types of FRP bars. The contributions from truss and direct strut mechanisms are considered in the analytical model. The truss model which has struts at various angles considering concrete contribution is derived in this paper. The concept of equivalent transverse FRP reinforcement is applied to integrate the concrete contribution into the proposed truss model. The shear strengths of concrete and FRP transverse reinforcements in the proposed model are calculated based on El-sayed et al. and CSA S806, respectively. The validity and applicability of the proposed model are evaluated by comparison with available experimental database, which consists of concrete beams reinforced with glass, carbon, aramid and basalt FRP bars. The comparison has shown a good correlation between the experimental data and analytical results. The proposed model is also compared with ACI 440.1R and CSA S806’s shear strength models and the better correlation is found. The results from this research shows that the properly-treated truss analogy can be used to assess the shear strength of concrete beams reinforced with various types of FRP bars.

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

The purpose of this paper is to explore a data and mechanism co-driven model for predicting the shear strength of reinforced concrete (RC) deep beams. The established experimental database contains 457 RC deep beams with or without web reinforcements and 9 key input features are determined by the shear mechanism of the RC deep beam. Six typical machine-learning models and five mechanism models are selected and compared. The comparison results show that the XGBoost model performs well in terms of prediction accuracy and generalization ability (R2 = 0.992 and 0.917 in the training and testing sets, respectively). The XGBoost model is explained by the Shapley additive explanation (SHAP) approach and the proposed interpretable approach combined with the shear mechanism. This interpretable approach is proposed based on SHAP and the contribution rates of main shear components. It can be qualitatively proved that the results of the XGBoost model conform to shear mechanism based on SHAP feature importance and dependency. The interpretability of prediction results is further quantitatively confirmed by comparing the contribution rates of different shear components obtained from the proposed interpretable approach and two mechanism models. As can be concluded from the above, the proposed interpretable approach and the data and mechanism co-driven model can be recommended for similar shear issues of RC members.

Effect of Concrete Strength on Shear Capacity of Reinforced High-Strength Concrete Continuous Beams without Web Reinforcements

In order to evaluate the shear strength and behavior of high-strength concrete beams without web reinforcing, eight high-strength continuous concrete beams with cross sections of 200 mm by 300 mm were cast and tested to failure. The ultimate load-carrying capability and shear behavior are presented. The applicability of the Sudheer et al. equations and ACI 318M-14 is examined. In addition, the effects of the compressive strength ( f c ′ ) and shear span to effective depth ratio (a/d) on the shear strength and behavior of HSRC beams without stirrups are also studied. 63 MPa, 78.8 MPa, 85.9 MPa, and 92 MPa were the concrete’s compressive strengths, while 2.41 and 3.33 were its shear span to effective depth ratios. There were two equal spans of continuous beams, and at each span, they were evaluated under a single-point load. It was found that with increasing compressive strength, the failure load was increased. But the deflection did not affect it significantly. While increasing, (a/d) led to a decrease in failure load but increased deflection. It was also found that both ACI 318 M-14 and Sudheer et al. equation were more conservative.

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.