• 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.

• 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.

• 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.

• Journal of the Earthquake Engineering Society of Korea
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• v.20 no.6
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• pp.361-368
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• 2016

Coupling beams serve as primary source of energy dissipation in coupled shear wall systems during large earthquakes. However, the overestimation of the shear strength of diagonally reinforced coupling beams may be adverse effect on the seismic performance of coupled shear wall systems. In order to force coupling beams to properly work during earthquakes, coupling beams should be designed with accurate shear strength equations. The objective of this study is to propose the accurate shear strength equation for slender diagonally reinforced coupling beams. For this purpose, experimental tests were conducted using three diagonally reinforced coupling specimens with different amount of transverse reinforcement under reversed cyclic loads to evaluate the hysteretic behavior of the specimens. The test results show that transverse reinforcement of slender diagonally reinforced coupling beam affects the maximum strength and drift ratio.

• 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 Ocean Engineering and Technology
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• v.21 no.6
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• pp.7-15
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• 2007

A mechanical model was developed to predict the behavior of point-loaded RC slender beams (a/d > 2.5) without stirrups. It is commonly accepted by most researchers that a diagonal tension crack plays a predominant role in the failure mode of these beams, but the failure mechanism of these members is still debatable. In this paper, it was assumed that diagonal tension failure was triggered by the concrete cover splitting due to the dowel action at the initial location of diagonal tension cracks, which propagate from flexural cracks. When concrete cover splitting occurred, the shape of a diagonal tension crack was simultaneously developed, which can be determined from the principal tensile stress trajectory. This fictitious crack rotates onto the crack tip with load increase. During the rotation, all forces acting on the crack (i.e, dowel force of longitudinal bars, vertical component of concrete tensile force, shear force by aggregate interlock, shear force in compression zone) were calculated by considering the kinematical conditions such as crack width or sliding. These forces except for the shear force in the compression zone were uncoupled with respect to crack width and sliding by the proposed constitutive relations for friction along the crack. Uncoupling the shear forces along the crack was aimed at distinguishing each force from the total shear force and clarifying the failure mechanism of RC slender beams without stirrups. In addition, a proposed method deriving the dowel force of longitudinal bars made it possible to predict the secondary shear failure. The proposed model can be used to predict not only the entire behavior of point-loaded RC slender shear beams, but also the ultimate shear strength. The experiments used to validate the proposed model are reported in a companion paper.

• Journal of Ocean Engineering and Technology
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• v.21 no.6
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• pp.16-25
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• 2007

In a companion paper, a rational mechanical model to predict the entire behavior of point-loaded RC slender beams (a/d > 2.5) without shear reinforcement was developed. This paper presents the test results of 9 slender shear beams and compares them with analytical results performed by the proposed model. They are grouped by two parameters, which are shear span ratio and concrete strength. Three kinds of concrete strength the 26.5, 39.2, and 58.8 MPa were included as a major experimental parameter together with different shear span ratios ranging from 3 to 6 depending on the test series. Tests were set up as a traditional 3 point bending test. Various measurements were taken to monitor abrupt shear failure. Test results were not only compared with analytical results from the proposed model, but also other formulas, to consider the various aspects of shear failure such as kinematical conditions or shear capacity. Finally, a review of the proposed model is presented with respect to the shear transfer mechanisms and the effect of test parameters. Results show that several assumptions and proposals adopted in the proposed model are rational and reasonable.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2939-2948
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• 2020

This paper presents an analytic solution procedure of the rotationally restrained hinged-hinged beam subjected to transverse motions at supports based on EBT (Euler-Bernoulli beam theory). The EBT solutions are compared with the solutions based on TBT (Timoshenko beam theory) for a wide range of the rotational restraint parameter (kL/EI) of slender beams whose slenderness ratio is greater than 100. The comparison shows the followings. The internal loads such as bending moment and shearing force of an extremely thin beam obtained by EBT show a good agreement with those obtained by TBT. But the discrepancy between two solutions of internal loads tends to increase as the slenderness ratio decreases. A careful examination shows that the discrepancy of the internal loads originates from their dynamic components whereas their static components show a little difference between EBT and TBT. This result suggests that TBT should be employed even for slender beams to consider the rotational effect and the shear deformation effect on dynamic components of the internal loads. The influence of the parameter on boundary conditions is examined by manipulating the spring stiffness from zero to a sufficiently large value.

• Steel and Composite Structures
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• v.44 no.5
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• pp.691-705
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• 2022

Shear failure in reinforced concrete (RC) structures is very hazardous. This failure is rarely predicted and may occur without any prior signs. Accurate shear strength prediction of the RC members is challenging, and traditional methods have difficulty solving it. This study develops a JAYA-GBRT model based on the JAYA algorithm and the gradient boosting regression tree (GBRT) to predict the shear strength of RC slender beams without stirrups. Firstly, 484 tests are carefully collected and divided into training and test sets. Then, the hyperparameters of the GBRT model are determined using the JAYA algorithm and 10-fold cross-validation. The performance of the JAYA-GBRT model is compared with five well-known empirical models. The comparative results show that the JAYA-GBRT model (R² = 0.982, RMSE = 9.466 kN, MAE = 6.299 kN, µ = 1.018, and Cov = 0.116) outperforms the other models. Moreover, the predictions of the JAYA-GBRT model are globally and locally explained using the Shapley Additive exPlanation (SHAP) method. The effective depth is determined as the most crucial parameter influencing the shear strength through the SHAP method. Finally, a Graphic User Interface (GUI) tool and a web application (WA) are developed to apply the JAYA-GBRT model for rapidly predicting the shear strength of RC slender beams without stirrups.

• Smart Structures and Systems
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• v.3 no.1
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• pp.51-68
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• 2007

High-strength concrete (HSC) is becoming increasingly attractive for various construction projects since it offers a multitude of benefits over normal-strength concrete (NSC). Unfortunately, current design provisions for shear capacity of RC slender beams are generally based on data developed for NSC members having a compressive strength of up to 50 MPa, with limited recommendations on the use of HSC. The failure of HSC beams is noticeably different than that of NSC beams since the transition zone between the cement paste and aggregates is much denser in HSC. Thus, unlike NSC beams in which micro-cracks propagate around aggregates, providing significant aggregate interlock, micro-cracks in HSC are trans-granular, resulting in relatively smoother fracture surfaces, thereby inhibiting aggregate interlock as a shear transfer mechanism and reducing the influence of compressive strength on the ultimate shear strength of HSC beams. In this study, a new approach based on genetic algorithms (GAs) was used to predict the shear capacity of both NSC and HSC slender beams without shear reinforcement. Shear capacity predictions of the GA model were compared to calculations of four other commonly used methods: the ACI method, CSA method, Eurocode-2, and Zsutty's equation. A parametric study was conducted to evaluate the ability of the GA model to capture the effect of basic shear design parameters on the behaviour of reinforced concrete (RC) beams under shear loading. The parameters investigated include compressivestrength, amount of longitudinal reinforcement, and beam's depth. It was found that the GA model provided more accurate evaluation of shear capacity compared to that of the other common methods and better captured the influence of the significant shear design parameters. Therefore, the GA model offers an attractive user-friendly alternative to conventional shear design methods.