• Structural Engineering and Mechanics
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• v.64 no.2
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• pp.259-270
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• 2017

The stochastic vibration response of the sandwich beam with the nonlinear adjustable visco-elastomer core and supported mass under stochastic support motion excitations is studied. The nonlinear dynamic properties of the visco-elastomer core are considered. The nonlinear partial differential equations for the horizontal and vertical coupling motions of the sandwich beam are derived. An analytical solution method for the stochastic vibration response of the nonlinear sandwich beam is developed. The nonlinear partial differential equations are converted into the nonlinear ordinary differential equations representing the nonlinear stochastic multi-degree-of-freedom system by using the Galerkin method. The nonlinear stochastic system is converted further into the equivalent quasi-linear system by using the statistic linearization method. The frequency-response function, response spectral density and mean square response expressions of the nonlinear sandwich beam are obtained. Numerical results are given to illustrate new stochastic vibration response characteristics and response reduction capability of the sandwich beam with the nonlinear visco-elastomer core and supported mass under stochastic support motion excitations. The influences of geometric and physical parameters on the stochastic response of the nonlinear sandwich beam are discussed, and the numerical results of the nonlinear sandwich beam are compared with those of the sandwich beam with linear visco-elastomer core.

• Earthquakes and Structures
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• v.3 no.6
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• pp.869-887
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• 2012

Ground motion scaling techniques are actively debated in the earthquake engineering community. Considerations such as what amplitude, over what period range and to what target spectrum are amongst the questions of practical importance. In this paper, the effect of various ground motion scaling approaches are explored using three reinforced concrete prototypical building models of 8, 12 and 20 stories designed to respond nonlinearly under a design level earthquake event in the seismically active Southern California region. Twenty-one recorded earthquake motions are selected using a probabilistic seismic hazard analysis and subsequently scaled using four different strategies. These motions are subsequently compared to spectrally compatible motions. The nonlinear response of a planar frameidealized building is evaluated in terms of plasticity distribution, floor level acceleration and uncorrelated acceleration amplification ratio distributions; and interstory drift distributions. The most pronounced response variability observed in association with the scaling method is the extent of higher mode participation in the nonlinear demands.

• Structural Engineering and Mechanics
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• v.83 no.3
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• pp.401-413
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• 2022

The collapse of civil infrastructure due to natural disasters results in financial losses and many casualties. In particular, the recent increase in earthquake activities has highlighted on the importance of assessing the seismic performance and predicting the seismic risk of a structure. However, the nonlinear behavior of a structure and the uncertainty in ground motion complicate the accurate seismic response prediction of a structure. Artificial intelligence can overcome these limitations to reasonably predict the nonlinear behavior of structures. In this study, a deep learning-based algorithm was developed to estimate the time-history seismic response of bridge structures. The proposed deep neural network was trained using structural and ground motion parameters. The performance of the seismic response prediction algorithm showed the similar phase and magnitude to those of the time-history analysis in a single-degree-of-freedom system that exhibits nonlinear behavior as a main structural element. Then, the proposed algorithm was expanded to predict the seismic response and fragility prediction of a bridge system. The proposed deep neural network reasonably predicted the nonlinear seismic behavior of piers and bearings for approximately 93% and 87% of the test dataset, respectively. The results of the study also demonstrated that the proposed algorithm can be utilized to assess the seismic fragility of bridge components and system.

• Structural Engineering and Mechanics
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• v.46 no.5
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• pp.695-711
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• 2013

It is reasonable to assume that reinforced concrete (RC) structures enter the nonlinear range of response during a severe ground motion. Numerical analysis to predict the behaviour therefore must allow for the presence of nonlinear deformations if an accurate estimate of seismic response is aimed. Among the factors contributing to inelastic deformations, the influence of the degradation of the bond slip phenomenon is important. Any rebar slip generates an additional rotation at the end regions of structural members which are not accounted for in a conventional analysis. Although these deformations could affect the seismic response of RC structures considerably, they are often neglected due to the unavailability of suitable models. In this paper, the seismic response of two types of RC structures, designed according to the Iranian concrete code (ABA) and the Iranian seismic code (2800), are evaluated using nonlinear dynamic and static analyses. The investigation is performed using nonlinear dynamic and static pushover analysis considering the deformations due to anchorage slip. The nonlinear analysis results confirm that bond slip significantly influences the seismic behavior of RC structure leading to an increase of lateral deformations by up to 30% depending on the height of building. The outcomes also identify important parameters affecting the extent of this influence.

• Journal of Korean Society of Steel Construction
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• v.8 no.4 s.29
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• pp.77-84
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• 1996

The structural members under seismic loading actually show inelastic behavior, so the inelastic responses should be calculated for the seismic design of structures or estimating the structural damage level. Although direct time history analysis may calculate the exact dynamic nonlinear responses for given ground motions, this approach involves a high computational cost and long period. Therefore, it should be developed the approach to estimate nonlinear responses for the practical purpose. The artificial earthquake accelerograms were generated to obtain the smoothed responses spectra, and the samples of generated accelerogram for each seismic event was used to examine average nonlinear response spectra. The stabilized response spectra for each earthquake event was used to evaluate the effects of various yield strength ratios, damping values and nonlinear hysteretic models. The approach, which can simply predict the nonlinear seismic responses of structures, was shown in this study.

• Steel and Composite Structures
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• v.25 no.3
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• pp.287-298
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• 2017

The present study aims to determine the structural response of full scaled rectangular columns under both of vertical and lateral loads using numerical methods. In the study, the composite columns considering full concrete filled circular steel tube (FCFRST) and concrete filled double-skin rectangular steel tube (CFDSRST) section types are numerically modelled using ANSYS software. Vertical and lateral loads are applied to models to assess the structural response of the composite elements. Also similar investigations are done for reinforced concrete rectangular (RCR) columns to compare the results with those of composite elements. The analyses of the systems are statically performed for both linear and nonlinear materials. In linear static analyses, both of vertical and lateral loads are applied to models as only one step. However in nonlinear analyses, while vertical loads are applied to model as only one step, lateral loads are applied to systems as step by step. The displacement and stress changes in some critical nodes and sections and contour diagrams are reported by graphs and figures. At the end of the study, it is demonstrated that the nonlinear models reveal more accurate result then those of linear models. Also, it is highlighted that composite columns provide more and more safety, ductility compared to reinforced concrete column.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.6
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• pp.583-590
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• 2015

Automobile crash optimization is nonlinear dynamic response structural optimization that uses highly nonlinear crash analysis in the time domain. The equivalent static loads (ESLs) method has been proposed to solve such problems. The ESLs are the static load sets generating the same displacement field as that of nonlinear dynamic analysis. Linear static response structural optimization is employed with the ESLs as multiple loading conditions. Nonlinear dynamic analysis and linear static structural optimization are repeated until the convergence criteria are satisfied. Nonlinear dynamic crash analysis for frontal analysis may not have boundary conditions, but boundary conditions are required in linear static response optimization. This study proposes a method to use the inertia relief method to overcome the mismatch. An optimization problem is formulated for the design of an automobile frontal structure and solved by the proposed method.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.30 no.1 s.244
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• pp.66-75
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• 2006

Nonlinear response structural optimization using equivalent static loads (NROESL) has been proposed. Nonlinear response optimization is solved by sequential linear response optimization with equivalent static loads which are generated from the nonlinear responses and linear stiffness matrix. The linear stiffness matrix should be obtained in NROESL, and this process can be fairly difficult for some applications. Proportional transformation of loads (PTL) is proposed to overcome the difficulties. Equivalent static loads are obtained by PTL. It is the same as NROESL except for the process of calculating equivalent static loads. PTL is developed for large-scale probems. First, linear and nonlinear responses are evaluated from linear and nonlinear analyses, respectively. At a DOF of the finite element method, the ratio of the two responses is calculated and an equivalent static load is made by multiplying the ratio and the loads for linear analysis. Therefore, the mumber of the equivalent static loads is as many as that of DOF's and an equivalent static load is used with the reponse for the corresponding DOF in the optimization process. All the equivalent static loads are used as multiple loading conditions during linear response optimization. The process iterates until it converges. Examples are solved by using the proposed method and the results are compared with conventional methods.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.8 s.239
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• pp.1051-1060
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• 2005

Nonlinear Response Optimization using Equivalent Static Loads (NROESL) method/algorithm is proposed to perform optimization of non-linear response structures. The conventional method spends most of the total design time on nonlinear analysis. The NROESL algorithm makes the equivalent static load cases for each response and repeatedly performs linear response optimization and uses them as multiple loading conditions. The equivalent static loads are defined as the loads in the linear analysis, which generates the same response field as those in non-linear analysis. The algorithm is validated for the convergence and the optimality. The proposed algorithm is applied to a simple mathematical problem to verify the convergence and the optimality.

• Structural Engineering and Mechanics
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• v.26 no.3
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• pp.251-262
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• 2007

This paper examines the application of artificial neural networks (ANN) to the response prediction of geometrically nonlinear truss structures. Two types of analysis (deterministic and probabilistic analyses) are considered. A three-layer feed-forward backpropagation network with three input nodes, five hidden layer nodes and two output nodes is firstly developed for the deterministic response analysis. Then a back propagation training algorithm with Bayesian regularization is used to train the network. The trained network is then successfully combined with a direct Monte Carlo Simulation (MCS) to perform a probabilistic response analysis of geometrically nonlinear truss structures. Finally, the proposed ANN is applied to predict the response of a geometrically nonlinear truss structure. It is found that the proposed ANN is very efficient and reasonable in predicting the response of geometrically nonlinear truss structures.