• Structural Engineering and Mechanics
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• v.77 no.4
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• pp.463-472
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• 2021

Fully rigid floor diaphragm is one of the main assumptions that are widely used in common practices due to its simple application. However, determining the exact degree of diaphragms flexibility cannot be easily accomplished without finite element modeling, which is an expensive and time-consuming procedure. Therefore, it is always possible that apparently rigid diaphragms, based on prescriptive limitations of seismic codes, experience some degrees of flexibility during the earthquakes. Since diaphragm flexibility has more uncertainties in asymmetric-plan structures, this study focuses on errors resulting from probable floor diaphragm flexibility of torsionally restrained structures. The analytical models used in this study were single-story buildings with asymmetric plan and RC shear walls. Although floor system is not considered explicitly, a wide range of considered diaphragm flexibility, from fully rigid to quite flexible, allows the results to be generalizable to a lot of lateral load resisting systems as well as floor systems. It has been shown that in addition to previously known effects of diaphragm flexibility, presence of orthogonal side elements during design procedure with rigid diaphragm assumption and rapid reduction in their absorbed forces can also be an important source to increase errors due to flexibility. Accordingly, from the obtained results the authors suggest designers to consider the possibility of diaphragm flexibility and its adverse effects, especially in torsionally restrained systems in their common designs.

• Earthquakes and Structures
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• v.21 no.5
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• pp.477-487
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• 2021

This study focuses on the influence of strong ground motion duration on the response and collapse probability of reinforced concrete walls with a predominant response in flexure. Walls with different height and mass were used to account for a broad spectrum of configurations and fundamental periods. The walls were designed following the specifications of the Chilean design code. Non-linear models of the reinforced concrete walls using a distributed plasticity approach were performed in OpenSees and calibrated with experimental data. Special attention was put on modeling strength and stiffness degradation. The effect of duration was isolated using spectrally equivalent ground motions of long and short duration. In order to assess the behavior of the RC shear walls, incremental dynamic analyses (IDA) were performed, and fragility curves were obtained using cumulative and non-cumulative engineering demand parameters. The spectral acceleration at the fundamental period of the wall was used as the intensity measure (IM) for the IDAs. The results show that the long duration ground motion set decreases the average collapse capacity in walls of medium and long periods compared to the results using the short duration set. Also, it was found that a lower median intensity is required to achieve moderate damage states in the same medium and long period wall models. Finally, strength and stiffness degradation are important modelling parameters and if they are not included, the damage in reinforced concrete walls may be greatly underestimated.

• Computers and Concrete
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• v.28 no.5
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• pp.507-519
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• 2021

In this study, a new procedure was proposed in order to predict the crack pattern and failure mode of steel fiber reinforced concrete (SFRC) corbels. Moreover, an experimental study was carried out in order to investigate the effect of several parameters, such as compressive strength, tensile strength, steel fiber ratio, shear span on the mechanical behavior of SFRC corbels in detail. Totally, 24 RC and SFRC corbels were prepared for the experimental study. Experimental results indicate that each investigated parameter has noticeable effect on the load capacity and failure mode of SFRC corbels. Moreover, finite element (FE) model of the tested corbels were prepared and efficiency of FE model was investigated for further studies. Comparison of FE and experimental results show that there is an acceptable fit between them regarding load capacity and crack patterns. Thereafter, parametric study was carried out via FE analyses in order to obtain a methodology for crack pattern and failure mode prediction of SFRC corbels. As a result of parametric studies, a new procedure was proposed as flowcharts in order to predict the failure mode of SFRC corbels for normal and high strength concrete class separately.

• Structural Engineering and Mechanics
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• v.83 no.5
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• pp.671-680
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• 2022

The shear capacity of beams is an essential parameter in designing beams carrying shear loads. Precise estimation of the ultimate shear capacity typically requires comprehensive calculation methods. For steel fiber reinforced concrete (SFRC) beams, traditional design methods may not accurately predict the interaction between different parameters affecting ultimate shear capacity. In this study, artificial neural network (ANN) modeling was utilized to predict the ultimate shear capacity of SFRC beams using ten input parameters. The results demonstrated that the ANN with 30 neurons had the best performance based on the values of root mean square error (RMSE) and coefficient of determination (R²) compared to other ANN models with different neurons. Analysis of the ANN model has shown that the clear shear span to depth ratio significantly affects the predicted ultimate shear capacity, followed by the reinforcement steel tensile strength and steel fiber tensile strength. Moreover, a Genetic Algorithm (GA) was used to optimize the ANN model's input parameters, resulting in the least cost for the SFRC beams. Results have shown that SFRC beams' cost increased with the clear span to depth ratio. Increasing the clear span to depth ratio has increased the depth, height, steel, and fiber ratio needed to support the SFRC beams against shear failures. This study approach is considered among the earliest in the field of SFRC.

• The Journal of Asian Finance, Economics and Business
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• v.10 no.1
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• pp.9-17
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• 2023

The study attempted to answer the questions related to the expected effects of supply chain management variables which include: Partnership with Suppliers (PS), Relationships with Customers (RC), Quality of Information Exchange (QIE), and Postponement (P), as independent variables, total quality management (TQM) as a dependents variable, and just in time (JIT) as a mediator variable. The study relies on the descriptive approach by conducting an empirical study on commercial companies in Riyadh Governorate in the Kingdom of Saudi Arabia. Through the methods of descriptive and analytical statistics represented in exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM)), using the AMOS and SPSS programs, the results of the applied study indicated that: There is an effect of supply chain management variables (quality of information exchange, Postponement) on just in time (JIT), there is no effect of the supply chain variables (relationship with customers, partnership with suppliers) on just in time (JIT), there is an indirect effect of the quality of information exchange, Postponement, partnership with suppliers, the relationship with customers on total quality management (TQM), relationship with customers affect on total quality management (TQM) indirectly, just in time (JIT) does not affect total quality management (TQM)

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.43 no.6 s.312
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• pp.34-42
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• 2006

As art analytic method to uncover interesting patterns hidden under a large volume of data, data mining research has been actively done so far in various fields. However, current state-of-the-arts in data mining research have several challenging problems such as being too ad-hoc. The existing techniques are mostly the ones designed for individual problems, so there is no unifying theory applicable for more general data mining problems. In this paper, we address the problem of classification, which is one of significant data mining tasks. Specifically, our objective is to evaluate radial basis function (RBF) model for classification tasks and investigate its usefulness. For evaluation, we analyze the popular Monk's problems which are well-known datasets in data mining research. First, we develop RBF models by using the representational capacity based learning algorithm, and then perform a comparative assessment of the results with other models generated by the existing techniques. Through a variety of experiments, it is empirically shown that the RBF model has not only the superior performance on the Monk's problems but also its modeling process can be controlled in a systematic way, so the RBF model with RC-based algorithm might be a good candidate to handle the current ad-hoc problem.

• Journal of the Korea Concrete Institute
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• v.24 no.2
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• pp.157-164
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• 2012

This study evaluates the shear performance of reinforced concrete beams with recycled coarse aggregates. A total of six specimens with various replacement ratios of recycled coarse aggregates (0%, 50%, and 100%) and different amount of shear reinforcement were cast and tested in this study. A finite element analysis was performed to predict the shear behavior of the specimens with natural or recycled coarse aggregates. The FE analysis was performed using a two-dimensional nonlinear FE analysis program based on the disturbed stress field model (DSFM), which is an extension of the modified compression field theory (MCFT). Experimental results showed that the specimens with 50% and 100% replacement ratios of recycled coarse aggregates had the similar shear strength compared to the specimen with natural aggregates, regardless of the replacement ratios of recycled coarse aggregates and the amount of the shear reinforcement. Furthermore, the comparison between experimental and analytical results showed that the proposed numerical modeling methods and the analytical model, DSFM, can be successfully used to predict the shear behavior of reinforced concrete beams with recycled coarse aggregates.

• Structural Engineering and Mechanics
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• v.90 no.6
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• pp.611-633
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• 2024

This study intends to investigate the response of multi-cell (MC) beams to flexural loads in which the primary reinforcement is composed of both metallic and non-metallic materials. "Multi-cell" describes beam sections with multiple longitudinal voids separated by thin webs. Seven reinforced concrete MC beams measuring 300×200×1800 mm were tested under flexural loadings until failure. Two series of beams are formed, depending on the type of main reinforcement that is being used. A control RC beam with no openings and six MC beams are found in these two series. Series one and two are reinforced with metallic and non-metallic main reinforcement, respectively, in order to maintain a constant reinforcement ratio. The first crack, ultimate load, deflection, ductility index, energy absorption, strain characteristics, crack pattern, and failure mode were among the structural parameters of the beams under investigation that were documented. The primary variables that vary are the kind of reinforcing materials that are utilized, as well as the kind and quantity of mesh layers. The outcomes of this study that looked at the experimental and numerical performance of ferrocement reinforced concrete MC beams are presented in this article. Nonlinear finite element analysis (NLFEA) was performed with ANSYS-16.0 software to demonstrate the behavior of composite MC beams with holes. A parametric study is also carried out to investigate the factors, such as opening size, that can most strongly affect the mechanical behavior of the suggested model. The experimental and numerical results obtained demonstrate that the FE simulations generated an acceptable degree of experimental value estimation. It's also important to demonstrate that, when compared to the control beam, the MC beam reinforced with geogrid mesh (MCGB) decreases its strength capacity by a maximum of 73.33%. In contrast, the minimum strength reduction value of 16.71% is observed in the MC beams reinforced with carbon reinforcing bars (MCCR). The findings of the experiments on MC beams with openings demonstrate that the presence of openings has a significant impact on the behavior of the beams, as there is a decrease in both the ultimate load and maximum deflection.

• Journal of the Earthquake Engineering Society of Korea
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• v.18 no.1
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• pp.53-61
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• 2014

RC shear wall sections which have irregular shapes such as T, ㄱ, ㄷ sections are typically used in low-rise buildings in Korea. Pushover analysis of building containing such members costs a lot of computation time and needs professional knowledge since it requires complicated modeling and, sometimes, fails to converge. In this study, a method using an equivalent column element for the shear wall is proposed. The equivalent column element consists of an elastic column, an inelastic rotational spring, and rigid beams. The inelastic properties of the rotational spring represent the nonlinear behavior of the shearwall and are obtained from the section analysis results and moment distribution for the member. The use of an axial force to compensate the difference in the axial deformation between the equivalent column element and the actual shear wall is also proposed. The proposed method is applied for the pushover analysis of a 5- story shear wall-frame building and the results are compared with ones using the fiber elements. The comparison shows that the inelastic behavior at the same drift was comparable. However, the performance points estimated using the pushover curves showed some deviations, which seem to be caused by the differences of estimated yield point and damping ratios.

• International Journal of Concrete Structures and Materials
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• v.9 no.2
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• pp.159-172
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• 2015

Estimation of the residual strength of corroded reinforced concrete beams has been studied from experimental and theoretical perspectives. The former is arduous as it involves casting beams of various sizes, which are then subjected to various degrees of corrosion damage. The latter are static; hence cannot be generalized as new coefficients need to be re-generated for new cases. This calls for dynamic models that are adaptive to new cases and offer efficient generalization capability. Computational intelligence techniques have been applied in Construction Engineering modeling problems. However, these techniques have not been adequately applied to the problem addressed in this paper. This study extends the empirical model proposed by Azad et al. (Mag Concr Res 62(6):405-414, 2010), which considered all the adverse effects of corrosion on steel. We proposed four artificial neural networks (ANN) models to predict the residual flexural strength of corroded RC beams using the same data from Azad et al. (2010). We employed two modes of prediction: through the correction factor ($C_f$) and through the residual strength ($M_{res}$). For each mode, we studied the effect of fixed and random data stratification on the performance of the models. The results of the ANN models were found to be in good agreement with experimental values. When compared with the results of Azad et al. (2010), the ANN model with randomized data stratification gave a $C_f$-based prediction with up to 49 % improvement in correlation coefficient and 92 % error reduction. This confirms the reliability of ANN over the empirical models.