• Advances in concrete construction
• /
• v.13 no.1
• /
• pp.1-10
• /
• 2022

Concrete is a brittle material and weak in tension. Traditionally, web reinforcement in the form of vertical stirrups is used in reinforced concrete (RC) beams to take care of principal stresses that may cause failure when they are subjected to shear stresses. In recent decades, the potential of various types of fibers for improving post-cracking behavior of RC beams and replacing stirrups completely or partially have been studied. It has been shown that the use of steel fibers randomly dispersed and oriented in concrete has a significant potential for enhancing mechanical properties of RC beams. However, the studies on deep steel fiber reinforced concrete (SFRC) beams are limited when compared to those focusing on slender beams. An experimental program consisting of three RC and nine SFRC deep beams without stirrups were conducted in this study. Besides, various models developed for predicting the ultimate shear strength and diagonal cracking strength of SFRC deep beams without stirrups were applied to experimental data obtained from the literature and this study.

• Computers and Concrete
• /
• v.12 no.5
• /
• pp.697-715
• /
• 2013

Numerous studies have been conducted to understand the shear behavior of reinforced concrete (RC) beams since it is a complex phenomenon. The diagonal cracking strength of a RC beam is critical since it is essential for determining the minimum amount of stirrups and the contribution of concrete to the shear strength of the beam. Most of the existing equations predicting the diagonal cracking strength of RC beams are based on experimental data. A powerful computational tool for analyzing experimental data is an artificial neural network (ANN). Its advantage over conventional methods for empirical modeling is that it does not require any functional form and it can be easily updated whenever additional data is available. An ANN model was developed for predicting the diagonal cracking strength of RC slender beams without stirrups. It is shown that the performance of the ANN model over the experimental data considered in this study is better than the performances of six design code equations and twelve equations proposed by various researchers. In addition, a parametric study was conducted to study the effects of various parameters on the diagonal cracking strength of RC slender beams without stirrups upon verifying the model.

• Proceedings of the Korea Concrete Institute Conference
• /
• 2001.05a
• /
• pp.591-596
• /
• 2001

Nine steel fiber reinforced high strength concrete beams and three steel fiber reinforced normal strength concrete beams without stirrups were tested by two point load. The variables studied in this investigation are the shear span/depth ratios of a/d = 2, 3 and 4, steel fiber volume fractions of V$_{f}$ : 0, 0.5% and 0.75% and concrete compressive strengths of f$_{ck}$: 630kgf/$cm^{2}$, and 310kgf/$cm^{2}$. Based on these tests and on tests by previous investigators, predictive equation is proposed for evaluating the ultimate shear strength of steel fiber reinforced concrete beams without stirrups. The proposed equation gave good prediction for the ultimate shear strength of the tested beams.

• Computers and Concrete
• /
• v.28 no.6
• /
• pp.533-547
• /
• 2021

Calculating the shear capacity of slender reinforced concrete beams without shear reinforcement was the subject of numerous studies, where the eternal problem of developing a single relationship that will be able to predict the expected shear capacity is still present. Using experimental results to extrapolate formulae was so far the main approach for solving this problem, whereas in the last two decades different research studies attempted to use artificial intelligence algorithms and available data sets of experimentally tested beams to develop new models that would demonstrate improved prediction capabilities. Given the limited number of available experimental databases, these studies were numerically restrained, unable to holistically address this problem. In this manuscript, a new approach is proposed where a numerically generated database is used to train machine-learning algorithms and develop an improved model for predicting the shear capacity of slender concrete beams reinforced only with longitudinal rebars. Finally, the proposed predictive model was validated through the use of an available ACI database that was developed by using experimental results on physical reinforced concrete beam specimens without shear and compressive reinforcement. For the first time, a numerically generated database was used to train a model for computing the shear capacity of slender concrete beams without stirrups and was found to have improved predictive abilities compared to the corresponding ACI equations. According to the analysis performed in this research work, it is deemed necessary to further enrich the current numerically generated database with additional data to further improve the dataset used for training and extrapolation. Finally, future research work foresees the study of beams with stirrups and deep beams for the development of improved predictive models.

• Computers and Concrete
• /
• v.27 no.5
• /
• pp.473-487
• /
• 2021

The review of the literature and design guidelines indicates a lack of design codes governing the shear strength of reinforced concrete (RC) beams strengthened with ultrahigh-performance fiber-reinforced concrete (UHPFRC). This study uses the results of a 3D finite element model constructed previously by the authors and verified against an experimental programme to gain a clear understanding of the shear strength of RC beams strengthened with UHPFRC by using different schemes. Experimental results found in the literature along with the numerical results for shear capacities of normal-strength RC and UHPFRC beams without stirrups are compared with available code design guidelines and empirical models found in the literature. The results show variance between the empirical models and the experimental results. Accordingly, proposed equations derived based on empirical models found in the literature were set to estimate the shear capacity of normal-strength RC beams without stirrups. In addition, the term 'shear span-to-depth ratio' is not considered in the equations for design guidelines found in the literature regarding the shear capacity of UHPFRC beams without stirrups. Consequently, a formula estimating the shear strength of UHPFRC and RC beams strengthened with UHPFRC plates and considering the effect of shear span-to-depth ratio is proposed and validated against an experimental programme previously conducted by the authors.

• Computers and Concrete
• /
• v.11 no.5
• /
• pp.365-382
• /
• 2013

Although shear reinforcement in beams typically consists of steel bars bent in the form of stirrups or hoops, the addition of deformed steel fibres to the concrete has been shown to enhance shear resistance and ductility in reinforced concrete beams. This paper presents a model that can be used to predict the shear strength of fibrous concrete rectangular members without stirrups. The model is an extension of the plasticity-based crack sliding model originally developed for plain concrete beams. The crack sliding model has been improved in order to take into account several aspects: the arch effect for deep beams, the post-cracking tensile strength of steel fibre reinforced concrete and its ability to control sliding along shear cracks, and the mitigation of the shear size effect due to presence of fibres. The results obtained by the model have been validated by a large set of experimental tests taken from literature, compared with several models proposed in literature, and numerical analyses are carried out showing the influence of fibres on the beam failure mode.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.9 no.1
• /
• pp.205-215
• /
• 2005

The main objective of this study is to investigate the behavior of NSC and HSC beams with stirrups. Main variables were the concrete compressive strength and amount of vertical stirrups. A total of 24 beams was tested; 4 beams without web reinforcement and 20 beams with web reinforcement in the form of vertical stirrups. Main variables were 2 different compressive strengths of concrete of 26.9MPa and 63.5MPa, 5 different spacing of stirrups of 200, 150, 120, 100 and 90mm. Therefore, the results were compared with the strengths predicted by the equations of ACI code 318-99 and other researchers. The shear reinforcement ratio, where the test beams were failed simultaneously under flexure and shear, were $0.63{\rho}_{vmax}$ for NSC beams and $0.53{\rho}_{vmax}$ for HSC beams, respectively. The ACI code equation was found to be very conservative for shear design.

• Smart Structures and Systems
• /
• v.14 no.5
• /
• pp.785-809
• /
• 2014

In this paper, an Adaptive nerou-based inference system (ANFIS) is being used for the prediction of shear strength of high strength concrete (HSC) beams without stirrups. The input parameters comprise of tensile reinforcement ratio, concrete compressive strength and shear span to depth ratio. Additionally, 122 experimental datasets were extracted from the literature review on the HSC beams with some comparable cross sectional dimensions and loading conditions. A comparative analysis has been carried out on the predicted shear strength of HSC beams without stirrups via the ANFIS method with those from the CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94 codes of design. The shear strength prediction with ANFIS is discovered to be superior to CEB-FIP Model Code (1990), AASHTO LRFD 1994 and CSA A23.3 - 94. The predictions obtained from the ANFIS are harmonious with the test results not accounting for the shear span to depth ratio, tensile reinforcement ratio and concrete compressive strength; the data of the average, variance, correlation coefficient and coefficient of variation (CV) of the ratio between the shear strength predicted using the ANFIS method and the real shear strength are 0.995, 0.014, 0.969 and 11.97%, respectively. Taking a look at the CV index, the shear strength prediction shows better in nonlinear iterations such as the ANFIS for shear strength prediction of HSC beams without stirrups.

• Proceedings of the Korea Concrete Institute Conference
• /
• 1995.04a
• /
• pp.331-338
• /
• 1995

Results of an experimental of the shear and flexures strength of doubly reinforced concrete beams were summarized. A total of 24 beams was tested; 4 without web reinforcement and 20 with web reinforcement in the form of vertical stirrups. Main variables were compressive strength of concrete which was 26.88MPa and 63.4MPa, spacing of stirrups which was no-stirrups, 200, 150, 120, 100 and 90mm. Tests results were compared with stength predicted using the equations of ACI 318-89. The shear reinforcement ratio of the beams, which failed simultaneously under both flexures and shear, were 0.66pvmax for low strength concrete beams and 0.56pvmax for high strength concrete beams, respectively. Thus, ACI equations for shear reinforcement were very conservative.

• Structural Engineering and Mechanics
• /
• v.61 no.5
• /
• pp.605-615
• /
• 2017

Shear failure of reinforced concrete (RC) beams is a major concern for structural engineers. It has been shown through various studies that the shear strength and ductility of RC beams can be improved by adding steel fibers to the concrete. An accurate model predicting the shear strength of steel fiber reinforced concrete (SFRC) beams will help SFRC to become widely used. An artificial neural network (ANN) model consisting of an input layer, a hidden layer of six neurons and an output layer was developed to predict the shear strength of SFRC slender beams without stirrups, where the input parameters are concrete compressive strength, tensile reinforcement ratio, shear span-to-depth ratio, effective depth, volume fraction of fibers, aspect ratio of fibers and fiber bond factor, and the output is an estimate of shear strength. It is shown that the model is superior to fourteen equations proposed by various researchers in predicting the shear strength of SFRC beams considered in this study and it is verified through a parametric study that the model has a good generalization capability.