• Computers and Concrete
• /
• v.16 no.3
• /
• pp.399-414
• /
• 2015

Nowadays, fiber reinforced polymer (FRP) composites are widely used for rehabilitation, repair and strengthening of reinforced concrete (RC) structures. Also, recent advances in concrete technology have led to the production of high strength concrete, HSC. Such concrete due to its very high compression strength is less ductile; so in seismic areas, ductility is an important factor in design of HSC members (especially FRP strengthened members) under flexure. In this study, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and multiple regression analysis are used to predict the curvature ductility factor of FRP strengthened reinforced HSC (RHSC) beams. Also, the effects of concrete strength, steel reinforcement ratio and externally reinforcement (FRP) stiffness on the complete moment-curvature behavior and the curvature ductility factor of the FRP strengthened RHSC beams are evaluated using the analytical approach. Results indicate that the predictions of ANFIS and multiple regression models for the curvature ductility factor are accurate to within -0.22% and 1.87% error for practical applications respectively. Finally, the effects of height to wide ratio (h/b) of the cross section on the proposed models are investigated.

• Journal of Positioning, Navigation, and Timing
• /
• v.5 no.1
• /
• pp.1-9
• /
• 2016

Range-based wireless localization system must measure accurate range between a mobile node (MN) and reference nodes. However, non-line-of-sight (NLOS) error caused by the spatial structures disturbs the localization system obtaining the accurate range measurements. Localization methods using the range measurements including NLOS error yield large localization error. But filter-based localization methods can provide comparatively accurate location solution. Motivated by the accuracy of the filter-based localization method, a filter residual-based NLOS error estimation method is presented in this paper. Range measurement-based residual contains NLOS error. By considering this factor with NLOS error properties, NLOS error is mitigated. Also a process noise covariance matrix tuning method is presented to reduce the time-delay estimation error caused by the single dynamic model-based filter when the speed or moving direction of a MN changes, that is the used dynamic model is not fit the current dynamic of a MN. The presented methods are evaluated by simulation allowing direct comparison between different localization methods. The simulation results show that the presented filter is more accurate than the iterative least squares- and extended Kalman filter-based localization methods.

• Industrial Engineering and Management Systems
• /
• v.10 no.4
• /
• pp.272-278
• /
• 2011

Decision tree as a classification tool is being used successfully in many areas such as medical diagnosis, customer churn prediction, signal detection and so on. The main advantage of decision tree classifiers is their capability to break down a complex structure into a collection of simpler structures, thus providing a solution that is easy to interpret. Since decision tree is a top-down algorithm using a divide and conquer induction process, there is a risk of reaching a local optimal solution. This paper proposes a procedure of optimally determining thresholds of the chosen variables for a decision tree using an adaptive particle swarm optimization (APSO). The proposed algorithm consists of two phases. First, we construct a decision tree and choose the relevant variables. Second, we find the optimum thresholds simultaneously using an APSO for those selected variables. To validate the proposed algorithm, several artificial and real datasets are used. We compare our results with the original CART results and show that the proposed algorithm is promising for improving prediction accuracy.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.13 no.4
• /
• pp.102-108
• /
• 1999

The sliding rmde controller(SMC) is known as having the robust variable structures for the nonlinear control systems such as coupled tank system with the pararretric perturbations and with the rapid disturbances. But the adaptive tuning algorit1uns for their pararreters are not satisfactory. Therefore, in this paper, a Real Variable Elitist Genetic Algorithm based Sliding Mode Controller (RVEGA SMC) for the precise control of the coupled tank level was tried. The SMC's switching pararreters were optimized easily and rapidly by RVEGA The simulation results showed that the tank level could be satisfactorily controlled without any overshoot and any steady-state error by the proposed RVEGA SMC.GA SMC.

• Earthquakes and Structures
• /
• v.13 no.4
• /
• pp.397-407
• /
• 2017

With the growing demand for metallic dampers in engineering practice, it is urgent to establish a reasonable approach to evaluating the mechanical performance of metallic dampers under seismic excitations. This paper introduces an effective method for parameter identification of the modified Bouc-Wen model and its application to evaluating the fatigue performance of metallic dampers (MDs). The modified Bouc-Wen model which eliminates the redundant parameter is used to describe the hysteresis behavior of MDs. Relations between the parameters of the modified Bouc-Wen model and the mechanical performance parameters of MDs are studied first. A modified Genetic Algorithm using real-integer hybrid coding with relative fitness as well as adaptive crossover and mutation rates (called RFAGA) is then proposed to identify the parameters of the modified Bouc-Wen model. A reliable approach to evaluating the fatigue performance of the MDs with respect to the Chinese Code for Seismic Design of Buildings (GB 50011-2010) is finally proposed based on the research results. Experimental data are employed to demonstrate the process and verify the effectiveness of the proposed approach. It is shown that the RFAGA is able to converge quickly in the identification process, and the simulation curves based on the identification results fit well with the experimental hysteresis curves. Furthermore, the proposed approach is shown to be a useful tool for evaluating the fatigue performance of MDs with respect to the Chinese Code for Seismic Design of Buildings (GB 50011-2010).

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

• Korean Journal of Biological Psychiatry
• /
• v.28 no.2
• /
• pp.23-35
• /
• 2021

Fear conditioning and extinction, which are adaptive processes to learn and avoid potential threats, have essential roles in the pathophysiology of anxiety disorders. Experimental fear conditioning and extinction have been used to identify the mechanism of fear and anxiety in humans. However, the brain-based mechanisms of fear conditioning and extinction are yet to be established. In the current review, we summarized the results of neuroimaging studies that examined the brain changes-functional activity and structures-regarding fear conditioning or extinction in healthy individuals. The functional activity of the amygdala, insula, anterior cingulate gyrus, ventromedial prefrontal cortex, and hippocampus changed dynamically with both fear conditioning and extinction. This review may provide an up-to-date summary that may broaden our understanding of pathophysiological mechanisms of anxiety disorder. In addition, the brain regions that are involved in the fear conditioning and extinction may be considered as potential treatment targets in the future studies.

• Steel and Composite Structures
• /
• v.43 no.5
• /
• pp.625-637
• /
• 2022

Axial compression capacity (P_u) is a significant yet complex parameter of concrete-filled steel tube (CFST) columns. This study offers a novel ensemble tool, adaptive neuro-fuzzy inference system (ANFIS) supervised by equilibrium optimization (EO), for accurately predicting this parameter. Moreover, grey wolf optimization (GWO) and Harris hawk optimizer (HHO) are considered as comparative supervisors. The used data is taken from earlier literature provided by finite element analysis. ANFIS is trained by several population sizes of the EO, GWO, and HHO to detect the best configurations. At a glance, the results showed the competency of such ensembles for learning and reproducing the P_u behavior. In details, respective mean absolute errors along with correlation values of 4.1809% and 0.99564, 10.5947% and 0.98006, and 4.8947% and 0.99462 obtained for the EO-ANFIS, GWO-ANFIS, and HHO-ANFIS, respectively, indicated that the proposed EO-ANFIS can analyze and predict the behavior of CFST columns with the highest accuracy. Considering both time and accuracy, the EO provides the most efficient optimization of ANFIS and can be a nice substitute for experimental approaches.

• Smart Structures and Systems
• /
• v.29 no.3
• /
• pp.433-444
• /
• 2022

Concrete is a most utilized material in the construction industry that have main components. The strength of concrete can be improved by adding some admixtures. Evaluating the impact of fly ash (FA) and silica fume (SF) on the long-term compressive strength (CS) of concrete provokes to find the significant parameters in predicting the CS, which could be useful in the practical works and would be extensible in the future analysis. In this study, to evaluate the effective parameters in predicting the CS of concrete containing admixtures in the long-term and present a fitted equation, the multivariate adaptive regression splines (MARS) method has been used, which could find a relationship between independent and dependent variables. Next, for optimizing the output equation, biogeography-based optimization (BBO), particle swarm optimization (PSO), and hybrid PSOBBO methods have been utilized to find the most optimal conclusions. It could be concluded that for CS predictions in the long-term, all proposed models have the coefficient of determination (R²) larger than 0.9243. Furthermore, MARS-PSOBBO could be offered as the best model to predict CS between three hybrid algorithms accurately.

• Geomechanics and Engineering
• /
• v.28 no.5
• /
• pp.477-491
• /
• 2022

In the present study, the effects of thermal loading on the buckling and resonance frequency of graphene platelets (GPL) reinforced nano-composites are examined. Functionally graded (FG) material properties are considered in thickness direction for the thermal responses of the composite. The equivalent material properties are obtained using Halphin-Tsai nano-mechanical model for composite layers. Moreover, the effects of nano-scale sizes are taken into account, employing functionally modified couple stress (FMCS) parameter. In this regard, for the first time, it is demonstrated that at certain values of GPL weight fraction, thermal buckling occurs. In obtaining results of vibrational behavior, both analytical solution and deep neural network (DNN) methods are used. The DNN method needs low computational costs to predict the resonance behavior. A comprehensive parametric study is conducted to indicate the effects of several geometrical, material, and loading conditions on the vibrational and buckling behavior of cylindrical shell structures made of GPL-nanocomposites. It is shown that the effect of temperature change on the occurrence of buckling is vital while it has a negligible impact on the resonance frequency of the structure. Moreover, the size-dependency of the results is demonstrated, and it cannot be neglected in nano-scales.