• Advances in Computational Design
• /
• v.1 no.4
• /
• pp.315-327
• /
• 2016

Damage detection and localisation in structures is essential since it can be a means for preventive maintenance of those structures under service conditions. The use of structural modal data for detecting the damage is one of the most efficient methods. This paper presents comparative performance of various state-of-the-art meta-heuristics for use in structural damage detection based on changes in modal data. The metaheuristics include differential evolution (DE), artificial bee colony algorithm (ABC), real-code ant colony optimisation (ACOR), charged system search (ChSS), league championship algorithm (LCA), simulated annealing (SA), particle swarm optimisation (PSO), evolution strategies (ES), teaching-learning-based optimisation (TLBO), adaptive differential evolution (JADE), evolution strategy with covariance matrix adaptation (CMAES), success-history based adaptive differential evolution (SHADE) and SHADE with linear population size reduction (L-SHADE). Three truss structures are used to pose several test problems for structural damage detection. The meta-heuristics are then used to solve the test problems treated as optimisation problems. Comparative performance is carried out where the statistically best algorithms are identified.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2008.04a
• /
• pp.416-421
• /
• 2008

A Metropolis genetic algorithm (MGA) is a newly-developed hybrid algorithm combining simple genetic algorithm (SGA) and simulated annealing (SA). In the algorithm, favorable features of Metropolis criterion of SA are incorporated in the reproduction operations of SGA. This way, MGA alleviates the disadvantages of finding imprecise solution in SGA and time-consuming computation in SA. It has been successfully applied and the efficiency has been verified for the practical structural design optimization. However, applicability of MGA for the wider range of problems should be rigorously proved through the solution of mathematical optimization problems. Thus, performances of MGA for the typical mathematical problems are investigated and compared with those of conventional algorithms such as SGA, micro genetic algorithm (${\mu}GA$), and SA. And, for better application of MGA, the effects of acceptance level are also presented. From numerical Study, it is again verified that MGA is more efficient and robust than SA, SGA and ${\mu}GA$ in the solution of mathematical optimization problems having various features.

• Korean Journal of Child Studies
• /
• v.32 no.5
• /
• pp.49-65
• /
• 2011

The purpose of this study was to investigate the structural relationship between the related variables of children's internalization and externalization of problems. A total of 709 elementary school students residing in Daegu City and Kyungpook province completed questionnaires which assessed family interaction functions, emotional regulation, self-control, and internalization and externalization of problems. The sample variance-covariance matrix was analyzed using AMOS 19.0, and a maximum likelihood minimization function. Goodness of fit was evaluated using the SRMS, RMSEA, and its 90% confidence interval, CFI, and TLI. The results were as follows : First, the function of family interaction, and emotional regulation had a significant direct effect on the internalization of problems. Moreover, emotional regulation, self-control and internalization of problems had a statistically substantial direct effect on the externalization of problems. Second, family interaction functions did not have a statistically significant direct on children's externalization of problems, although it may well have an indirect effect on children's externalization of problems through emotional regulation and self-control. Finally, self-control did not enjoy a direct effect on children's internalization of problems.

• Computational Structural Engineering
• /
• v.25 no.2
• /
• pp.36-40
• /
• 2012

• Computational Structural Engineering
• /
• v.28 no.3
• /
• pp.7-12
• /
• 2015

• The Journal of Korean Institute for Practical Engineering Education
• /
• v.3 no.2
• /
• pp.106-113
• /
• 2011

The new computer program, visual SolidMech (ver 2.0), for mechanics of materials and structural mechanics has been developed using visual C++. The visual SolidMech is organized in a format similar to most standard texts on mechanics of materials and structural mechanics. This program consists of a number of menus to perform various calculations as well as a set of dedicated graphical user interfaces. Solutions to problems are given in both graphical and numerical forms. The visual SolidMech will help students develop problem-solving skills by showing them the important factors affecting various problem types, by helping them visualize the nature of internal stresses and member deformations, and by providing them an easy-to-use means of investigating a greater number of problems and variations. This new program can be utilized as a supplement to existing texts in mechanics of materials and structural mechanics.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 1998.04a
• /
• pp.199-209
• /
• 1998

Since most engineering problems have had open-ended and ill-defined characteristics, design process is in advance attended with determination of alternatives based on realistic constraints after definition of appropriate problem. And it is completed with selection of best alternative through their comparison and investigation, and with performance of selected-alternative's detail design. As the process of structural design compared with that of general design, this paper presents a paradigm which can generate structural design alternatives, select optimum structure among them and simultaneously set its optimum design variables in reference of several objective as a result in more extended design region. For this purpose, specialized genetic algorithms which can handle design alternatives and multicriteria problems is used.

• Computers and Concrete
• /
• v.16 no.4
• /
• pp.569-585
• /
• 2015

Various computational tools are available for modeling highly nonlinear structural engineering problems that lack a precise analytical theory or understanding of the phenomena involved. This paper adopts a fairly simple nonparametric adaptive regression algorithm known as multivariate adaptive regression splines (MARS) to model the nonlinear interactions between variables. The MARS method makes no specific assumptions about the underlying functional relationship between the input variables and the response. Details of MARS methodology and its associated procedures are introduced first, followed by a number of examples including three practical structural engineering problems. These examples indicate that accuracy of the MARS prediction approach. Additionally, MARS is able to assess the relative importance of the designed variables. As MARS explicitly defines the intervals for the input variables, the model enables engineers to have an insight and understanding of where significant changes in the data may occur. An example is also presented to demonstrate how the MARS developed model can be used to carry out structural reliability analysis.

• Structural Engineering and Mechanics
• /
• v.92 no.1
• /
• pp.111-120
• /
• 2024

Sampling methods are powerful approaches to solving the problems of structural reliability analysis and estimating the failure probability of structures. In this paper, a new sampling method is proposed offering lower variance and lower computational cost for complex and high-dimensional problems. The method is called Optimal Latinized partially stratified sampling (OLPSS) as it is based upon the Latinized Partially Stratified Sampling (LPSS) which itself is based on merging Stratified Sampling (SS) and Latin Hypercube Sampling (LHS) algorithms. While LPSS has a low variance, it may suffer from a lack of good space-filling of its generated samples in some cases. In the OLPSS, this issue has been resolved by employing a new columnwise-pairwise exchange optimization procedure for sample generation. The efficiency of the OLPSS has been tested and reported under several benchmark mathematical functions and structural examples including structures with a large number of variables (e.g., a structure with 67 variables). The proposed method provides highly accurate estimates of the failure probability of structures with a significantly lower variance relative to the Monte Carlo simulations, Latin Hypercube, and standard LPSS.

• Structural Engineering and Mechanics
• /
• v.58 no.3
• /
• pp.533-553
• /
• 2016

In this paper, the recently developed meta-heuristic algorithm called tug of war optimization is applied to optimal design of castellated beams. Two common types of laterally supported castellated beams are considered as design problems: beams with hexagonal openings and beams with circular openings. Here, castellated beams have been studied for two cases: beams without filled holes and beams with end-filled holes. Also, tug of war optimization algorithm is utilized for obtaining the solution of these design problems. For this purpose, the minimum cost is taken as the objective function, and some benchmark problems are solved from literature.