• Earthquakes and Structures
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• v.8 no.1
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• pp.185-204
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• 2015

This study investigates the impact of the earthquake incident angle on the structural demand and the influence of ground motion selection and scaling methods on seismic directionality effects. The structural demand produced by Non-Linear Time-History Analyses (NLTHA) varies with the seismic input incidence angle. The seismic directionality effects are evaluated by subjecting four three-dimensional reinforced concrete structures to different scaled and un-scaled records oriented along nine incidence angles, whose values range between 0 and 180 degrees, with an increment of 22.5 degrees. The results show that NLTHAs performed applying the ground motion records along the principal axes underestimate the structural demand prediction, especially when plan-irregular structures are analyzed. The ground motion records generate the highest demand when applied along the lowest strength structural direction and a high energy content of the records increases the structural demand corresponding to this direction. The seismic directionality impact on structural demand is particularly important for irregular buildings subjected to un-scaled accelerograms. However, the orientation effects are much lower if spectrum-compatible combinations of scaled records are used. In both cases, irregular structures should be analyzed first with pushover analyses in order to identify the weaker structural directions and then with NLTHAs for different incidence angles.

• Wind and Structures
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• v.17 no.4
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• pp.361-377
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• 2013

Wall functions have been widely used in computational fluid dynamics (CFD) simulations and can save significant computational costs compared to other near-wall flow treatment strategies. However, most of the existing wall functions were based on the asymptotic characteristics of near-wall flow quantities, which are inapplicable in complex and non-equilibrium flows. A modified wall function is thus derived in this study based on flow over a plate at zero-pressure gradient, instead of on the basis of asymptotic formulations. Turbulent kinetic energy generation ($G_P$), dissipation rate (${\varepsilon}$) and shear stress (${\tau}_{\omega}$) are composed together as the near-wall expressions. Performances of the modified wall function combined with the nonlinear realizable k-${\varepsilon}$ turbulence model are investigated in homogeneous equilibrium atmosphere boundary layer (ABL) and flow around a 6 m cube. The computational results and associated comparisons to available full-scale measurements show a clear improvement over the standard wall function, especially in reproducing the boundary layer flow. It is demonstrated through the two case studies that the modified wall function is indeed adaptive and can yield accurate prediction results, in spite of its simplicity.

• Advances in concrete construction
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• v.1 no.1
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• pp.1-27
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• 2013

Discrete steel fibres can increase significantly the bending and the shear resistance of concrete structural elements when Steel Fibre Reinforced Concrete (SFRC) is designed in such a way that fibre reinforcing mechanisms are optimized. To assess the fibre reinforcement effectiveness in shallow structural elements failing in bending and in shear, experimental and numerical research were performed. Uniaxial compression and bending tests were executed to derive the constitutive laws of the developed SFRC. Using a cross-section layered model and the material constitutive laws, the deformational behaviour of structural elements failing in bending was predicted from the moment-curvature relationship of the representative cross sections. To evaluate the influence of the percentage of fibres on the shear resistance of shallow structures, three point bending tests with shallow beams were performed. The applicability of the formulation proposed by RILEM TC 162-TDF for the prediction of the shear resistance of SFRC elements was evaluated. Inverse analysis was adopted to determine indirectly the values of the fracture mode I parameters of the developed SFRC. With these values, and using a softening diagram for modelling the crack shear softening behaviour, the response of the SFRC beams failing in shear was predicted.

• Computers and Concrete
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• v.21 no.6
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• pp.697-703
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• 2018

Compressive strength is one of the most important engineering properties of concrete, and testing of the compressive strength of concrete specimens is often costly and time consuming. In order to provide the time for concrete form removal, re-shoring to slab, project scheduling and quality control, it is necessary to predict the concrete strength based upon the early strength data. However, concrete compressive strength is affected by many factors, such as quality of raw materials, water cement ratio, ratio of fine aggregate to coarse aggregate, age of concrete, compaction of concrete, temperature, relative humidity and curing of concrete. The concrete compressive strength is a quite nonlinear function that changes depend on the materials used in the concrete and the time. This paper presents an adaptive neuro-fuzzy inference system (ANFIS) for the prediction of concrete compressive strength. The training of fuzzy system was performed by a hybrid method of gradient descent method and least squares algorithm, and the subtractive clustering algorithm (SCA) was utilized for optimizing the number of fuzzy rules. Experimental data on concrete compressive strength in the literature were used to validate and evaluate the performance of the proposed ANFIS model. Further, predictions from three models (the back propagation neural network model, the statistics model, and the ANFIS model) were compared with the experimental data. The results show that the proposed ANFIS model is a feasible, efficient, and accurate tool for predicting the concrete compressive strength.

• Nuclear Engineering and Technology
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• v.53 no.10
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• pp.3379-3397
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• 2021

In this study, a multi-physics modeling method was developed to analyze a nuclear fuel rod's thermo-mechanical behavior especially for high temperature anisotropic creep deformation during ballooning and burst occurring in Loss of Coolant Accident (LOCA). Based on transient heat transfer and nonlinear mechanical analysis, the present work newly incorporated the nuclear fuel rod's special characteristics which include gap heat transfer, temperature and burnup dependent material properties, and especially for high temperature creep with material anisotropy. The proposed method was tested through various benchmark analyses and showed good agreements with analytical solutions. From the validation study with a cladding burst experiment which postulates the LOCA scenario, it was shown that the present development could predict the ballooning and burst behaviors accurately and showed the capability to predict anisotropic creep behavior during the LOCA. Moreover, in order to verify the anisotropic creep methodology proposed in this study, the comparison between modeling and experiment was made with isotropic material assumption. It was found that the present methodology with anisotropic creep could predict ballooning and burst more accurately and showed more realistic behavior of the cladding.

• Structural Engineering and Mechanics
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• v.69 no.5
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• pp.499-509
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• 2019

While the wheel flat is an asymmetrical phenomenon in the railway, majority of researches have used two-dimensional models in the investigation of the effect of wheel flat on the wheel rail forces. This is due to the considerably low computational costs of two dimensional (2D) models although their reliability is questionable. This leaves us with the question of "what is the optimum modeling technique?". It is addressed in this research. For this purpose, two and three dimensional numerical models of railway vehicle/track interaction were developed. The three dimensional (3D) model was validated by comparisons of its results with those obtained from a comprehensive field tests carried out in this research and then, the results obtained from the 2D and 3D models were compared. The results obtained indicate that there are considerable differences between wheel/rail forces obtained from the 2D and 3D models in the conditions of medium to large wheel-flats. On the other hand, it was shown that the results of the 2D models are reliable for particular ranges of vehicle speed, railway track stiffness and wheel-fats lengths and depths. The results were used to draw a diagram, which presents the optimum modeling technique, compromising between the costs and accuracy of the obtained results.

• Advances in concrete construction
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• v.11 no.1
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• pp.1-9
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• 2021

This article discusses the issue of optimizing controller design issues, in which the artificial intelligence (AI) evolutionary bat (EB) optimization algorithm is combined with the fuzzy controller in the practical application of the building. The controller of the system design includes different sub-parts such as system initial condition parameters, EB optimal algorithm, fuzzy controller, stability analysis and sensor actuator. The advantage of the design is that for continuous systems with polytypic uncertainties, the integrated H2/H∞ robust output strategy with modified criterion is derived by asymptotically adjusting design parameters. Numerical verification of the time domain and the frequency domain shows that the novel system design provides precise prediction and control of the structural displacement response, which is necessary for the active control structure in the fuzzy model. Due to genetic algorithm (GA), we use a hierarchical conditions of the Hurwitz matrix test technique and the limits of average performance, Hierarchical Fitness Function Structure (HFFS). The dynamic fuzzy controller proposed in this paper is used to find the optimal control force required for active nonlinear control of building structures. This method has achieved successful results in closed system design from the example.

• Computers and Concrete
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• v.27 no.3
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• pp.225-239
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• 2021

Carbon fiber reinforced polymer (CFRP) has extensive use in strengthening reinforced concrete structures due to its high strength and elastic modulus, low weight, fast and easy application, and excellent durability performance. Many studies have been carried out to determine the performance of the CFRP confined concrete cylinder. Although studies about the prediction of confined compressive strength using ANN are in the literature, the insufficiency of the studies to predict the strain of confined concrete cylinder using ANN, which is the most appropriate analysis method for nonlinear and complex problems, draws attention. Therefore, to predict both strengths and also strain values, two different ANNs were created using an extensive experimental database. The strength and strain networks were evaluated with the statistical parameters of correlation coefficients (R2), root mean square error (RMSE), and mean absolute error (MAE). The estimated values were found to be close to the experimental results. Mathematical equations to predict the strength and strain values were derived using networks prepared for convenience in engineering applications. The sensitivity analysis of mathematical models was performed by considering the inputs with the highest importance factors. Considering the limit values obtained from the sensitivity analysis of the parameters, the performances of the proposed models were evaluated by using the test data determined from the experimental database. Model performances were evaluated comparatively with other analytical models most commonly used in the literature, and it was found that the closest results to experimental data were obtained from the proposed strength and strain models.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.5
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• pp.317-323
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• 2022

In this paper, a deep neural network(DNN) model for predicting the vibration characteristics of the composite rotor blade with c-spar cross section was developed. Herein, the present DNN model is defined by using the natural frequencies obtained through the in-house code based on the nonlinear co-rotational(CR) shell element. For the present DNN model, the accuracy of the model was evaluated via the data with a random distribution of thickness and a tendency to decrease in thickness along the blade span.

• Nuclear Engineering and Technology
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• v.53 no.12
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• pp.4022-4032
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• 2021

In this study, machine learning (ML) techniques were used to model surveillance test data of nuclear power plants from an international database of the ASTM E10.02 committee. Regression modeling was conducted using various techniques, including Cubist, XGBoost, and a support vector machine. The root mean square deviation of each ML model for the baseline dataset was less than that of the ASTM E900-15 nonlinear regression model. With respect to the interpolation, the ML methods provided excellent predictions with relatively few computations when applied to the given data range. The effect of the explanatory variables on the transition temperature shift (TTS) for the ML methods was analyzed, and the trends were slightly different from those for the ASTM E900-15 model. ML methods showed some weakness in the extrapolation of the fluence in comparison to the ASTM E900-15, while the Cubist method achieved an extrapolation to a certain extent. To achieve a more reliable prediction of the TTS, it was confirmed that advanced techniques should be considered for extrapolation when applying ML modeling.