• 한국건축시공학회:학술대회논문집
• /
• 한국건축시공학회 2015년도 춘계 학술논문 발표대회
• /
• pp.109-110
• /
• 2015

Estimation of rebar quantity may be used as an index to evaluate the economic feasibility of structural designs. However, when using the software to estimate the rebar quantity, there may be some limitations such as data loss caused by human errors and estimation delays caused by increased input time, since the information on arrangement of rebar is inserted manually. To solve the problems of such quantity estimation software, it is necessary to develop a method on automatic input/output of structural design information for quantity estimation and an algorithm for accurate estimation of rebar quantity. The purpose of this study is to improve the existing rebar quantity estimation by connecting with the database on information related to rebar estimation and the algorithm for rebar estimation, in order to develop an algorithm to estimate an accurate, net rebar quantity. The study result can be used as basic data for development of software for efficient structural designs and automatic framework estimation of buildings.

• Communications for Statistical Applications and Methods
• /
• 제24권5호
• /
• pp.421-441
• /
• 2017

In this survey, estimation methods for structural vector autoregressive models are presented in a systematic way. Both frequentist and Bayesian methods are considered. Depending on the model setup and type of restrictions, least squares estimation, instrumental variables estimation, method-of-moments estimation and generalized method-of-moments are considered. The methods are presented in a unified framework that enables a practitioner to find the most suitable estimation method for a given model setup and set of restrictions. It is emphasized that specifying the identifying restrictions such that they are linear restrictions on the structural parameters is helpful. Examples are provided to illustrate alternative model setups, types of restrictions and the most suitable corresponding estimation methods.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 1996년도 봄 학술발표회 논문집
• /
• pp.119-126
• /
• 1996

In this paper, a method of substructural identification is presented for the estimation of localized structural parameters. for this purpose, an auto-regressive and moving average with stochastic input (ARMAX) model is derived for the substructure to process the measurement data impaired by noises. The sequential prediction error method is used fer the estimation of unknown localized parameters. Using the substructural method, the number of unknown parameters can be reduced and the convergence and accuracy of estimation can be improved. For some substructures, the effect of the input excitation is expressed in terms of the responses at the inferences with the main structure, and substructural identification may be carried out without measuring the actual input excitation to the whole structure. Example analysis is carried out for idealized structural models of a multistory building and a truss bridge. The results indicate that the present method is effective and efficient for local damage estimation of complex structures.

• Structural Engineering and Mechanics
• /
• 제22권4호
• /
• pp.401-418
• /
• 2006

Solutions to the problems of structural parameter estimation from modal response using leastsquares minimization of force or displacement residuals are generally sensitive to noise in the response measurements. The sensitivity of the parameter estimates is governed by the physical characteristics of the structure and certain features of the noisy measurements. It has been shown that the regularization method can be used to reduce effects of the measurement noise on the estimation error through adding a regularization function to the parameter estimation objective function. In this paper, we adopt the regularization function as the Euclidean norm of the difference between the values of the currently estimated parameters and the a priori parameter estimates. The effect of the regularization function on the outcome of parameter estimation is determined by a regularization factor. Based on a singular value decomposition of the sensitivity matrix of the structural response, it is shown that the optimal regularization factor is obtained by using the maximum singular value of the sensitivity matrix. This selection exhibits the condition where the effect of the a priori estimates on the solutions to the parameter estimation problem is minimal. The performance of the proposed algorithm is investigated in comparison with certain algorithms selected from the literature by using a numerical example.

• 한국전산구조공학회논문집
• /
• 제23권6호
• /
• pp.641-649
• /
• 2010

Estimation of uncertain parameters is required in many engineering problems which involve probabilistic structural analysis as well as prognosis of existing structures. In this case, Bayesian framework is often employed, which is to represent the uncertainty of parameters in terms of probability distributions conditional on the provided data. The resulting form of distribution, however, is not amenable to the practical application due to its complex nature making the standard probability functions useless. In this study, Markov chain Monte Carlo (MCMC) method is proposed to overcome this difficulty, which is a modern computational technique for the efficient and straightforward estimation of parameters. Three case studies that implement the estimation are presented to illustrate the concept. The first one is an inverse estimation, in which the unknown input parameters are inversely estimated based on a finite number of measured response data. The next one is a metamodel uncertainty problem that arises when the original response function is approximated by a metamodel using a finite set of response values. The last one is a prognostics problem, in which the unknown parameters of the degradation model are estimated based on the monitored data.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 2005년도 춘계 학술발표회 논문집
• /
• pp.672-679
• /
• 2005

Damage detection is a very active research field, in which significant efforts have been invested in recent years. In this paper, analysis using structural stiffness estimation for damage detection is presented and compared to other methodologies. By using a cantilever analytical beam model, it is shown here that not only location but also the amount of damage in structure can be predicted from the ratio of change in stiffness. Damage detection experiment in real beam specimen on is also peformed and the results are compared.

• 한국지진공학회논문집
• /
• 제6권6호
• /
• pp.7-15
• /
• 2002

본 논문에서는 다자유도계 구조물의 진동대 실험결과 분석에서 효율적인 지진손상도 평가 및 소성모형 추정을 목적으로, 계측결과를 각 부재별 소성이력으로 환산하고 이 소성이력에 대해 비선형 계수 추정법을 적용하는 다단계 방안을 연구하였다. 이때, 추정된 부재별 소성이력은 부재별 지진 손상도를 평가하는 지표로 활용될 수 있으며, 추정된 비선형 모형 계수를 이용하여 구축된 비선형 다자유도계 구조는 다양한 구조재해석의 모형으로 활용될 수 있다. 제시된 방법의 검증을 위해, 해석적 방법과 실험적 방법의 예제해석이 수행되었다. 예제해석 결과는 해석적 방법과 실험적 방법 모두에서 본 논문의 방법이 매우 효과적임을 보여 주고 있다.

• 한국전산구조공학회:학술대회논문집
• /
• 한국전산구조공학회 1994년도 가을 학술발표회 논문집
• /
• pp.175-182
• /
• 1994

As for the safety evaluation of existing large-scale structures, methods for estimation of the structural and dynamic properties are studied. Sequential prediction error method in time domain and improved FRF estimator in frequency domain are comparatively studied. For this purpose, impact tests of 2 bay 3 floor steel frame structure are performed. Results from both methods are found to be consistent to each others, however those from the finite-element analysis are slightly different from experimental results.

• Communications for Statistical Applications and Methods
• /
• 제17권4호
• /
• pp.551-560
• /
• 2010

This paper considers a Bayesian approach to modeling a flexible regression function under structural measurement error model. The regression function is modeled based on semiparametric regression with penalized splines. Model fitting and parameter estimation are carried out in a hierarchical Bayesian framework using Markov chain Monte Carlo methodology. Their performances are compared with those of the estimators under structural measurement error model without a semiparametric component.

• Earthquakes and Structures
• /
• 제13권3호
• /
• pp.323-335
• /
• 2017

A simplified framework for the probabilistic estimation of economic losses induced by the structural vulnerability in single-story and single-bay precast industrial buildings is presented. The simplifications introduced in the framework are oriented to the definition of an expeditious procedure adoptable by government agencies and insurance companies for preliminary risk assessment. The economic losses are evaluated considering seismic hazard, structural response, damage resulting from the structural vulnerability and only structural-vulnerability-induced e]conomic losses, i.e., structural repair or reconstruction costs (stock and flow costs) and content losses induced by structural collapse. The uncertainties associated with each step are accounted for via Monte Carlo simulations. The estimation results in a probabilistic description of the seismic risk of portal-like industrial buildings, expressed in terms of economic losses for each occurrence (i.e., seismic event) that owners (i.e., insured) and stakeholders can use to make risk management decisions. The outcome may also be useful for the definition of the insurance premiums and the evaluation of the risks and costs for the owner corresponding to the insurance industrial costs. A prototype of a precast concrete industrial building located in Mirandola, Italy, hit by the 2012 Emilia earthquake, is used as an example of the application of the procedure.