• Computers and Concrete
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• v.14 no.1
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• pp.19-40
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• 2014

Reinforced concrete deep beams are structural beams with low shear span-to-depth ratio, and hence in which the strain distribution is significantly nonlinear and the conventional beam theory is not applicable. A strut-and-tie model is considered one of the most rational and simplest methods available for shear strength prediction and design of deep beams. The strut-and-tie model approach describes the shear failure of a deep beam using diagonal strut and truss mechanism: The diagonal strut mechanism represents compression stress fields that develop in the concrete web between diagonal cracks of the concrete while the truss mechanism accounts for the contributions of the horizontal and vertical web reinforcements. Based on a database of 406 experimental observations, this paper proposes a new strut-and-tie-model for accurate prediction of shear strength of reinforced concrete deep beams, and further improves the model by correcting the bias and quantifying the scatter using a Bayesian parameter estimation method. Seven existing deterministic models from design codes and the literature are compared with the proposed method. Finally, a limit-state design formula and the corresponding reduction factor are developed for the proposed strut-andtie model.

• Journal of the Earthquake Engineering Society of Korea
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• v.13 no.2
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• pp.29-36
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• 2009

The present study emphasizes a nonlinear model to predict the shear behaviour of reinforced concrete interior beam-column joints. To model the shear behaviour of a panel zone in the beam-column joint, a modified softened truss model theory for in-plane shear prediction was introduced. This relationship was changed to define the characteristics for the rotational spring to represent the shear deformation in the joint by an equivalent moment-rotation relationship from the joint equilibrium. The analysis model was compared with experiments on reinforced concrete interior beam-column joints that were subjected to axial and shear forces, and the current model was found to accurately predict not only the shear force but also the shear deformation in the joint.

• Proceedings of the Korea Concrete Institute Conference
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• 2003.05a
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• pp.896-901
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• 2003

This paper describes a model on shear strength of RC columns strengthened with FRP sheets. In this study, we propose a confined concrete strength model of RC columns confined by transverse reinforcement as well as FRP sheet by introducing corresponding effective confinement coefficient for each confined concrete area. Then, a shear strength model of the confined RC columns is proposed by lower and upper bound limit analysis which are based on the truss-arch model theory and shear band failure theory, respectively. Along with shear test data obtained from strengthened column specimens, the developed analytical models are verified. The comparison shows that the proposed model can be used effectively for the prediction of both ultimate strength and required amount of strengthening in retrofit design for RC columns.

• Structural Engineering and Mechanics
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• v.29 no.1
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• pp.77-89
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• 2008

Presence of high degree of orthotropy enhances shear lag phenomenon in laminated composite box-beams and it persists till failure. In this paper three key parameters governing shear lag behavior of laminated composite box beams are identified and defined by simple expressions. Uniqueness of the identified key parameters is proved with the help of finite element method (FEM) based studies. In addition to this, for the sake of generalization of prediction of shear lag effect in symmetrical laminated composite box beams a feed forward back propagation neural network (BPNN) model is developed. The network is trained and tested using the data base generated by extensive FEM studies carried out for various b/D, b/tF, tF/tW and laminate configurations. An optimum network architecture has been established which can effectively learn the pattern. Computational efficiency of the developed ANN makes it suitable for use in optimum design of laminated composite box-beams.

• Journal of Korean Association for Spatial Structures
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• v.24 no.2
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• pp.83-90
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• 2024

Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.18 no.2
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• pp.201-211
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• 2005

Due to the complex mechanism and various parameters that affect shear behavior of reinforced concrete (RC) members, models on shear tend to be complex and difficult to utilize for design of structural members, and empirical relationships formulated with limited test data often work lot members having a specific range of influencing parameters on shear. As an alternative approach tot solving this problem, artificial neural networks have been suggested by some researchers. In this paper, artificial neural networks were used to predict shear strengths of RC beams without shear reinforcement. Especially, a large database that consists of shear test results of 398 RC members without shear reinforcement was used for artificial neural network analysis. Three well known approaches for shear strength of RC members, ACI 318-02 shear provision, Zsutiy's equation, and Okamura's relationship, are also evaluated with test results in the shear database and compared with neural network approach. While ACI 318-02 provided inaccurate predictions for RC members without shear reinforcement, the empirical equations by Zsutty and Okamura provided more improved prediction of Shear strength than ACI 318-02. The artificial neural networks, however provided the best prediction of shear strengths of RC beams without shear reinforcement that was closest to test results.

• 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.

• Proceedings of the Korean Society for Technology of Plasticity Conference
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• 2003.05a
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• pp.427-733
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• 2003

Adiabatic shear bands have been observed in the serrated chip during high strain rate metal cutting process of medium carbon steel and titanium alloy. The recent microscopic observations have shown that dynamic recrystallization occurs in the narrow adiabatic shear bands. However the conventional flow stress models such as the Zerilli-Armstrong model and the Johnson-Cook model, in general, do not predict the occurrence of dynamic recrystallization (DRX) in the shear bands and the thermal softening effects accompanied by DRX. In the present study, a strain hardening and thermal softening model is proposed to predict the adiabatic shear localized chip formation. The finite element analysis (FEA) with this proposed flow stress model shows that the temperature of the shear band during cutting process rises above 0.5T$\sub$m/. The simulation shows that temperature rises to initiate dynamic recrystallization, dynamic recrystallization lowers the flow stress, and that adiabatic shear localized band and the serrated chip are formed. FEA is also used to predict and compare chip formations of two flow stress models in orthogonal metal cutting with AISI 1045. The predictions of the FEA agreed well with the experimental measurements.

• Journal of the Korea Concrete Institute
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• v.14 no.6
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• pp.910-921
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• 2002

For the prediction of shear behavior of reinforced concrete beams, this paper proposed Transformation Angle Truss Model (TATM) considered bending moment effect. Shear stress-strain relationship obtained from the TATM was agreed well with test results conducted by this study Further, shear strength obtained from the TATM was compared to the experimentally observed results of 170 reinforced concrete beams which had various shear span ratios shapes of support and shapes of cross section. The shear strength of reinforced concrete beams obtained from test was better predicted by the TATM with 0.96 in average and 11.9％ in coefficient of variation than by other truss models. And the ratio of experimental results to theoretical results obtained from the TATM was almost constant regardless of the η and a/d.

• Advances in materials Research
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• v.9 no.2
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• pp.133-153
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• 2020

In this paper, an improved theoretical interfacial stress and slip analysis is presented for simply supported composite steel-concrete beam bonded with an adhesive. The adherend shear deformations have been included in the present theoretical analyses by assuming a linear shear stress through the thickness of the adherends, while all existing solutions neglect this effect. Remarkable effect of shear deformations of elements has been noted in the results. It is observed that large shear is concentrated and slip at the edges of the composite steel-concrete. Comparing with some experimental results from references, analytical advantage of this improvement is possible to determine the normal and shear stress to estimate exact prediction of normal and shear stress interfacial along span between concrete and steel beam. The exact prediction of these stresses will be very important to make an accurate analysis of the mode of fracture. It is shown that both the normal and shear stresses at the interface are influenced by the material and geometry parameters of the composite steel-concrete beam. This research is helpful for the understanding on mechanical behavior of the connection and design of such structures.