• Architectural research
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• v.3 no.1
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• pp.61-70
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• 2001

The paper is intended to propose design strength of slender steel reinforced beam-columns by using the modified superposed method. The design of composite members is carried out by a superposed strength method in AIJ (Architectural Institute of Japan) design method. The bearing capacities of the steel part and the concrete part have to be determined separately and then added to a combined capacity. Authors have proposed a new superposed method in a modified form for the slender composite beam-columns and reinforced column. The modified superposed method is adopted for the slender steel reinforced beam-columns. Validation of the modified superposed method is undertaken by comparison with analytical results calculated assuming a sine curve deflected shape of the beam-columns, and with the test results conducted in Japan.

• International Journal of Concrete Structures and Materials
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• v.1 no.1
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• pp.63-73
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• 2007

Optimum multi-layered feed-forward neural network (NN) models using a resilient back-propagation algorithm and early stopping technique are built to predict the shear capacity of reinforced concrete deep and slender beams. The input layer neurons represent geometrical and material properties of reinforced concrete beams and the output layer produces the beam shear capacity. Training, validation and testing of the developed neural network have been achieved using 50%, 25%, and 25%, respectively, of a comprehensive database compiled from 631 deep and 549 slender beam specimens. The predictions obtained from the developed neural network models are in much better agreement with test results than those determined from shear provisions of different codes, such as KBCS, ACI 318-05, and EC2. The mean and standard deviation of the ratio between predicted using the neural network models and measured shear capacities are 1.02 and 0.18, respectively, for deep beams, and 1.04 and 0.17, respectively, for slender beams. In addition, the influence of different parameters on the shear capacity of reinforced concrete beams predicted by the developed neural network shows consistent agreement with those experimentally observed.

• Advances in Computational Design
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• v.8 no.2
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• pp.113-131
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• 2023

The use of fibers has been commonly considered in engineered cementitious composites, but their behavior with coarse aggregate in concrete has not been studied significantly, which is needed to meet structural performance objectives for design, such as ductility. This research analyzes the behavior of fiber-reinforced concrete with coarse aggregate with 0.62%, 1.23%, and 2% PVA (Polyvinyl-alcohol) content, varying the maximum aggregate size. Tensile (direct and indirect) and compressive concrete tests were performed. The PVA fiber addition in coarse aggregate concrete increased the ductility in compression, especially for the fiber with a larger aspect ratio, with a minor impact on strength. In addition, the tensile tests showed that the PVA fiber increased the tensile strength of concrete with coarse aggregate and, more significantly, improved the ductility. A selected mixture was used to build short and slender reinforced concrete beams to assess the behavior of structural members. PVA fiber addition in short beams changed the failure mode from shear to flexure, increasing the deflection capacity. On the other hand, the slender beam tests revealed negligible impact with the use of PVA.

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

• Proceedings of the Korea Concrete Institute Conference
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• 2005.05a
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• pp.391-394
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• 2005

A theoretical model was developed to predict the shear strength of slender reinforced concrete beams. The shear force applied to a cross-section of the beam was assumed to be resisted primarily by the compressive zone of intact concrete rather than by the tensile zone. The shear capacity of the cross section was defined based on the material failure criteria of concrete: failure controlled by compression and failure controlled by tension. In the evaluation of the shear capacity, interaction with the normal stresses developed by the flexural moment in the cross section was considered. In the proposed strength model, the shear strength of the beam and the location of the critical section were determined at the intersection between the shear capacity and shear demand curves. The proposed strength model was verified by the comparisons to prior experimental results.

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

• Structural Engineering and Mechanics
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• v.6 no.2
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• pp.161-172
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• 1998

Reinforced concrete beams possess variable flexural and torsional stiffnesses due to formation of cracks in the tension area along the beam. In order to check the stability of the beam, it is thus more appropriate to divide the beam into a finite number of segments for which mean stiffnesses and also bending moments are calculated. The stability analysis is further simplified, by using these mean values for each segment. In this paper, an algorithm for calculating the critical lateral buckling slenderness ratio for a definite load level, in a reinforced concrete beam without lateral support at the flanges, is presented. By using this ratio, the lateral buckling safety level of a slender beam may be checked or estimated.

• Journal of the Korea Concrete Institute
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• v.11 no.5
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• pp.131-137
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• 1999

The use of higher strength materials with the strength methed of design has resulted in more slender member and shallower sections. For this reason, it is necessary to satisfy the requirements of serviceability even though the structural safety is the most important limit state. This paper is only concerned with the control of deflections in the serviceability. In this study, an analytical model is presented to predict the deflections of reinforced concrete beams to given loading and environmental conditions. This model is based on the finite element approach in which a finite element is generally divided into a number of stiffening effect due to cracking, creep and shrinkage. Comparisons are made with available measured deflections reported by others to assess the capability of the layered beam model. The calculated values of instantaneous and long-term deflection show good agreement with experimental results in the range of tension stiffening parameter $\beta$ between 2.5 and 3.0.

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

• Journal of the Korea Concrete Institute
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• v.17 no.6 s.90
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• pp.911-922
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• 2005

A theoretical study was performed to investigate the behavioral chracteristics and shear strength of fiber reinforced concrete slender beams. In the fiber reinforced concrete beam, the shear force applied to a cross section of the beam was resisted by both compressive zone and tensile zone. The shear capacity of the compressive zone was defined addressing the interaction with the normal stresses developed by the flexural moment in the cross section. The shear capacity of the tensile zone was defined addressing the post-cracking tensile strength of fiber reinforced concrete. Since the magnitude and distribution of the normal stresses vary according to the flexural deformation of the beam, the shear capacity of the beam was defined as a function of the flexural deformation of the beam. The shear strength of the beam and the location of the critical section were determined at the intersection between the shear capacity and shear demand curves. The proposed method was developed as a unified shear design method which is applicable to conventional reinforced concrete as well as fiber reinforced concrete.