• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.3A
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• pp.193-200
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• 2010

Structural design requires simultaneously to ensure safety by considering quantitatively uncertainties in the applied loadings, material properties and fabrication error and to maximize economical efficiency. As a solution, system reliability-based design optimization (SRBDO), which takes into consideration both uncertainties and economical efficiency, has been extensively researched and numerous attempts have been done to apply it to structural design. Contrary to conventional deterministic optimization, SRBDO involves the evaluation of component and system probabilistic constraints. However, because of the complicated algorithm for calculating component reliability indices and system reliability, excessive computational time is required when the large-scale finite element analysis is involved in evaluating the probabilistic constraints. Accordingly, an algorithm for SRBDO exhibiting improved stability and efficiency needs to be developed for the large-scale problems. In this study, a more stable and efficient SRBDO based on the performance measure approach (PMA) is developed. PMA shows good performance when it is applied to reliability-based design optimization (RBDO) which has only component probabilistic constraints. However, PMA could not be applied to SRBDO because PMA only calculates the probabilistic performance measure for limit state functions and does not evaluate the reliability indices. In order to overcome these difficulties, the decoupled algorithm is proposed where RBDO based on PMA is sequentially performed with updated target component reliability indices until the calculated system reliability index approaches the target system reliability index. Through a mathematical problem and ten-bar truss problem, the proposed method shows better convergence and efficiency than other approaches.

• Structural Engineering and Mechanics
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• v.46 no.3
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• pp.371-385
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• 2013

Vibration isolators and anti-vibration mounts are ideal, for example, in creating floating floors for gymnasiums, or performance spaces. However, it is well-known that there are great difficulties on isolating vibration transmission in structural steel components, especially steel floors. Besides, the selection of inertia blocks, which are usually used by engineers as an effective vibration control measure, is usually based on crude methods or the experience of the engineers. Thus, no simple method or indices have been available for assessing the effect of inertia blocks on vibration isolation or stability of vibratory systems. Thus, the aims of this research are to provide further background description using a FE model and present and implement a modal approach, that was validated experimentally, the latter assisting in providing improved understanding of the vibration transmission phenomenon in steel buildings excited by a velocity-source type of excitation. A better visualization of the mean-square velocity distribution in the frequency domain is presented using the concept of modal expansion. Finally, the variation of the mean-square velocity with frequency, whilst varying mass and/or stiffness of the coupled system, is presented.

• Proceedings of the KSR Conference
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• 2003.10c
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• pp.446-454
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• 2003

This paper derived a theoretical method to estimate structural characteristics of carbody members when material substitution designs were performed, and verified the theoretical method with finite element analyses. For important factors in rolling stock design, such as bending stiffness, natural frequency, bending strength and buckling strength, some performance indices to estimate structural behaviors were developed in order to derive an equivalent design in spite of material substitutions. The developed method was used to reduce the weights of carbody components, as example problems, by substituting the aluminium alloy for the structural steel. The analysis results of the examples show that the proposed technique gives a reasonable initial guess in the case of a material substitution design.

• Computers and Concrete
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• v.24 no.1
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• pp.7-17
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• 2019

The complex phenomenon of the bond formation in geopolymer is not well understood and therefore, difficult to model. This paper present applied statistical models for evaluating the compressive strength of geopolymer. The applied statistical models studied are divided into three different categories - linear regression [least absolute shrinkage and selection operator (LASSO) and elastic net], tree regression [decision and bagging tree] and kernel methods (support vector regression (SVR), kernel ridge regression (KRR), Gaussian process regression (GPR), relevance vector machine (RVM)]. The performance of the methods is compared in terms of error indices, computational effort, convergence and residuals. Based on the present study, kernel based methods (GPR and KRR) are recommended for evaluating compressive strength of Geopolymer concrete.

• Structural Engineering and Mechanics
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• v.68 no.5
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• pp.549-561
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• 2018

Sobol method is applied as a powerful variance decomposition technique in the field of global sensitivity analysis (GSA). The paper is devoted to increase convergence speed of the extracted Sobol indices using a new proposed sampling technique called genetic based Latine hypercube sampling (GBLHS). This technique is indeed an improved version of restricted Latine hypercube sampling (LHS) and the optimization algorithm is inspired from genetic algorithm in a new approach. The new approach is based on the optimization of minimax value of LHS arrays using manipulation of array indices as chromosomes in genetic algorithm. The improved Sobol method is implemented to perform factor prioritization and fixing of an uncertain comprehensive high speed rotor-bearing system. The finite element method is employed for rotor-bearing modeling by considering Eshleman-Eubanks assumption and interaction of axial force on the rotor whirling behavior. The performance of the GBLHS technique are compared with the Monte Carlo Simulation (MCS), LHS and Optimized LHS (Minimax. criteria). Comparison of the GBLHS with other techniques demonstrates its capability for increasing convergence speed of the sensitivity indices and improving computational time of the GSA.

• Steel and Composite Structures
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• v.24 no.3
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• pp.369-382
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• 2017

This study investigates the reliability of the performance levels of moment resisting steel frames subjected to lateral loads such as wind and earthquake. The reliability assessment has been performed with respect to three performance levels: serviceability, damageability, and ultimate limit states. A four-story moment resisting frame is used as a typical example. In the reliability assessment the uncertainties in the loadings and in the capacity of the frame have been considered. The wind and earthquake loads are assumed to have lognormal distribution, and the frame resistance is assumed to have a normal distribution. In order to obtain an appropriate limit state function a linear relation between the loading and the deflection is formed. For the reliability analysis an algorithm has been developed for determination of limit state functions and iterations of the first order reliability method (FORM) procedure. By the method presented herein the multivariable analysis of a complicated reliability problem is reduced to an S-R problem. The procedure for iterations has been tested by a known problem for the purpose of avoiding convergence problems. The reliability indices for many cases have been obtained and also the effects of the coefficient of variation of load and resistance have been investigated.

• Structural Engineering and Mechanics
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• v.80 no.6
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• pp.645-655
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• 2021

Performance-based reliability analysis is a practical approach to investigate the seismic performance and stochastic nonlinear response of structures considering a random process. This is significant due to the uncertainties involved in every aspect of the analysis. Therefore, the present study aims to evaluate the performance-based reliability within a stochastic finite element (FE) framework for reinforced concrete (RC) shear walls that are considered as one of the most essential elements of structures. To accomplish this purpose, deterministic FE analyses are conducted for both squat and slender shear walls to validate numerical models through experimental results. The presented numerical analysis is performed by using the ABAQUS FE program. Afterwards, a random-effects investigation is carried out to consider the influence of different random variables on the lateral load-top displacement behavior of RC members. Using these results and through utilizing the Monte-Carlo simulation method, stochastic nonlinear analyses are also performed to generate random FE models based on input parameters and their probabilistic distributions. In order to evaluate the reliability of RC walls, failure probabilities and corresponding reliability indices are calculated at life safety and collapse prevention levels of performance as suggested by FEMA 356. Moreover, based on reliability indices, capacity reduction factors are determined subjected to shear for all specimens that are designed according to the ACI 318 Building Code. Obtained results show that the lateral load and the compressive strength of concrete have the highest effects on load-displacement responses compared to those of other random variables. It is also found that the probability of shear failure for the squat wall is slightly lower than that for slender walls. This implies that 𝛽 values are higher in a non-ductile mode of failure. Besides, the reliability of both squat and slender shear walls does not change significantly in the case of varying capacity reduction factors.

• Structural Engineering and Mechanics
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• v.66 no.3
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• pp.399-409
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• 2018

This paper demonstrates the potential application of machine learning algorithms for approximate prediction of the load and deflection capacities of the novel type of Laced Steel Concrete-Composite (LSCC) beams proposed by Anandavalli et al. (Engineering Structures 2012). Initially, global and local responses measured on LSCC beam specimen in an experiment are used to validate nonlinear FE model of the LSCC beams. The data for the machine learning algorithms is then generated using validated FE model for a range of values of the identified sensitive parameters. The performance of four well-known machine learning algorithms, viz., Support Vector Regression (SVR), Minimax Probability Machine Regression (MPMR), Relevance Vector Machine (RVM) and Multigene Genetic Programing (MGGP) for the approximate estimation of the load and deflection capacities are compared in terms of well-defined error indices. Through relative comparison of the estimated values, it is demonstrated that the algorithms explored in the present study provide a good alternative to expensive experimental testing and sophisticated numerical simulation of the response of LSCC beams. The load carrying and displacement capacity of the LSCC was predicted well by MGGP and MPMR, respectively.

• Journal of Biomedical Engineering Research
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• v.14 no.3
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• pp.265-272
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• 1993

We present a new waveform detection method, based on the search of distinctive line-segments. The method is based on the basic assumption that the waveform morphology of biological signals is readily characterized by a sequence of the distinctive line-segments and their structural features. In this method, the distinctive line-segments are first searched for, and a structural feature analysis is performed an the distinctive line-segments found. Experiments of detecting epileptic spikes were carried out to evaluate the detection per formance of the method. Two subjects were used for training and tuning the algorithm and four subjects for testing the method. The results were obtained on two different performance indices, detection ratio and the number of false detections per minute.

• Structural Engineering and Mechanics
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• v.41 no.5
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• pp.575-589
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• 2012

This paper presents a reliability analysis of steel I-beams with rectangular web openings, based on a combination of the common probabilistic reliability methods, such as RSM, FORM and SORM and using data obtained from experimental tests performed at the Istanbul Technical University. A procedure is proposed to obtain the optimum design load that can be applied to this type of structural members, by taking into account specified target values of reliability indices for ultimate and serviceability limit states. The goal of the paper is to present an algorithm to obtain more realistic and economical design of beams and to demonstrate that it can be applied efficiently to steel I-beams with web openings. Finally, a sensitivity analysis is performed allowing to ranking the random variables according to their importance in the reliability analysis.