• Earthquakes and Structures
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• 제10권1호
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• pp.141-161
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• 2016

In seismic fragility and risk analysis, the definition of structural limit state (LS) capacities is of crucial importance. Traditionally, LS capacities are defined according to design code provisions or using deterministic pushover analysis without considering the inherent randomness of structural parameters. To assess the effects of structural randomness on LS capacities, ten structural parameters that include material strengths and gravity loads are considered as random variables, and a probabilistic pushover method based on a correlation-controlled Latin hypercube sampling technique is used to estimate the uncertainties in LS capacities for four typical reinforced concrete frame buildings. A series of ten LSs are identified from the pushover curves based on the design-code-given thresholds and the available damage-controlled criteria. The obtained LS capacities are further represented by a lognormal model with the median $m_C$ and the dispersion ${\beta}_C$. The results show that structural uncertainties have limited influence on $m_C$ for the LSs other than that near collapse. The commonly used assumption of ${\beta}_C$ between 0.25 and 0.30 overestimates the uncertainties in LS capacities for each individual building, but they are suitable for a building group with moderate damages. A low uncertainty as ${\beta}_C=0.1{\sim}0.15$ is adequate for the LSs associated with slight damages of structures, while a large uncertainty as ${\beta}_C=0.40{\sim}0.45$ is suggested for the LSs near collapse.

• 한국융합학회논문지
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• 제12권2호
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• pp.247-258
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• 2021

최근 조직들은 조직원에게 엄격한 정보보안 수준을 요구하고 있다. 엄격한 정보보안 정책 및 기술은 정보보안 관련 스트레스를 유발할 수 있다. 연구 목적은 지식공유행동 및 개인조직 적합성을 감소시키는 정보보안 기술 및 업무스트레스의 부정적 영향을 제시하는 것이다. 연구 대상은 정보보안 정책을 보유한 조직에서 근무하는 조직원이며, 연구가설은 309개의 표본을 활용하여 구조방정식모델링으로 검증한다. 연구 결과, 개인조직적합성이 지식공유행동에 긍정적 영향을 주었으나, 업무스트레스가 부정적 영향을 주었다. 또한, 기술스트레스가 개인조직 적합성에 부정적 영향을 미쳤다. 추가적으로, 업무모호성이 개인조직 적합성과 지식공유행동사이에 조절효과를 가졌다. 연구 시사점은 조직원의 정보보안 기술 및 업무 스트레스의 부정적 영향을 확인하였으며, 내부자의 부정적 행동 최소화를 위한 방향을 제시한다.

• Earthquakes and Structures
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• 제5권4호
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• pp.439-459
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• 2013

Main objective of the present study is to determine the statistical properties and suitable probability distribution functions of spectral displacements from nonlinear static and nonlinear dynamic analysis within the frame work of Monte Carlo simulation for typical low rise and high rise RC framed buildings located in zone III and zone V and designed as per Indian seismic codes. Probabilistic analysis of spectral displacement is useful for strength assessment and loss estimation. To the author's knowledge, no study is reported in literature on comparison of spectral displacement including the uncertainties in capacity and demand in Indian context. In the present study, uncertainties in capacity of the building is modeled by choosing cross sectional dimensions of beams and columns, density and compressive strength of concrete, yield strength and elastic modulus of steel and, live load as random variables. Uncertainty in demand is modeled by choosing peak ground acceleration (PGA) as a random variable. Nonlinear static analysis (NSA) and nonlinear dynamic analysis (NDA) are carried out for typical low rise and high rise reinforced concrete framed buildings using IDARC 2D computer program with the random sample input parameters. Statistical properties are obtained for spectral displacements corresponding to performance point from NSA and maximum absolute roof displacement from NDA and suitable probability distribution functions viz., normal, Weibull, lognormal are examined for goodness-of-fit. From the hypothesis test for goodness-of-fit, lognormal function is found to be suitable to represent the statistical variation of spectral displacement obtained from NSA and NDA.

• Structural Engineering and Mechanics
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• 제46권1호
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• pp.19-37
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• 2013

The optimum design of base isolation system considering model parameter uncertainty is usually performed by using the unconditional response of structure obtained by the total probability theory, as the performance index. Though, the probabilistic approach is powerful, it cannot be applied when the maximum possible ranges of variations are known and can be only modelled as uncertain but bounded type. In such cases, the interval analysis method is a viable alternative. The present study focuses on the bounded optimization of base isolation system to mitigate the seismic vibration effect of structures characterized by bounded type system parameters. With this intention in view, the conditional stochastic response quantities are obtained in random vibration framework using the state space formulation. Subsequently, with the aid of matrix perturbation theory using first order Taylor series expansion of dynamic response function and its interval extension, the vibration control problem is transformed to appropriate deterministic optimization problems correspond to a lower bound and upper bound optimum solutions. A lead rubber bearing isolating a multi-storeyed building frame is considered for numerical study to elucidate the proposed bounded optimization procedure and the optimum performance of the isolation system.

• Smart Structures and Systems
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• 제4권1호
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• pp.19-34
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• 2008

Nowadays developments in the field of laminated composite structures with piezoelectric have attracted significant attention of researchers due to their wide range of applications in engineering such as sensors, actuators, vibration suppression, shape control, noise attenuation and precision positioning. Due to large number of parameters associated with its manufacturing and fabrication, composite structures with piezoelectric display a considerable amount of uncertainty in their material properties. The present work investigates the effect of the uncertainty on the free vibration response of piezoelectric laminated composite plate. The lamina material properties have been modeled as independent random variables for accurate prediction of the system behavior. System equations have been derived using higher order shear deformation theory. A finite element method in conjunction with Monte Carlo simulation is employed to obtain the secondorder statistics of the natural frequencies. Typical results are presented for all edges simply supported piezoelectric laminated composite plates to show the influence of scattering in material properties on the second order statistics of the natural frequencies. The results have been compared with those available in literature.

• Wind and Structures
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• 제22권1호
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• pp.65-87
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• 2016

The wind velocity profile over the height of a structure in high intensity wind (HIW) events, such as downbursts, differs from that associated with atmospheric boundary layer (ABL) winds. Current design codes for lattice transmission structures contain only limited advice on the treatment of HIW effects, and structural design is carried out using wind load profiles and response factors derived for ABL winds. The present study assesses the load-deformation curve (capacity curve) of a transmission tower under modeled downburst wind loading, and compares it with that obtained for an ABL wind loading profile. The analysis considers nonlinear inelastic response under simulated downburst wind fields. The capacity curve is represented using the relationship between the base shear and the maximum tip displacement. The results indicate that the capacity curve remains relatively consistent between different downburst scenarios and an ABL loading profile. The use of the capacity curve avoids the difficulty associated with defining a reference wind speed and corresponding wind profile that are adequate and applicable for downburst and ABL winds, thereby allowing a direct comparison of response under synoptic and downburst events. Uncertainty propagation analysis is carried out to evaluate the tower capacity by considering the uncertainty in material properties and geometric variables. The results indicated the coefficient of variation of the tower capacity is small compared to those associated with extreme wind speeds.

• Smart Structures and Systems
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• 제17권3호
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• pp.445-470
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• 2016

In this study, the Bayesian probabilistic framework is investigated for modal identification and modal identifiability based on the field measurements provided in the structural health monitoring benchmark problem of an instrumented cable-stayed bridge named Ting Kau Bridge (TKB). The comprehensive structural health monitoring system on the cable-stayed TKB has been operated for more than ten years and it is recognized as one of the best test-beds with readily available field measurements. The benchmark problem of the cable-stayed bridge is established to stimulate investigations on modal identifiability and the present paper addresses this benchmark problem from the Bayesian prospective. In contrast to deterministic approaches, an appealing feature of the Bayesian approach is that not only the optimal values of the modal parameters can be obtained but also the associated estimation uncertainty can be quantified in the form of probability distribution. The uncertainty quantification provides necessary information to evaluate the reliability of parametric identification results as well as modal identifiability. Herein, the Bayesian spectral density approach is conducted for output-only modal identification and the Bayesian model class selection approach is used to evaluate the significance of different modes in modal identification. Detailed analysis on the modal identification and modal identifiability based on the measurements of the bridge will be presented. Moreover, the advantages and potentials of Bayesian probabilistic framework on structural health monitoring will be discussed.

• Wind and Structures
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• 제22권1호
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• pp.17-41
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• 2016

To address the uncertainty of the flight trajectories caused by the turbulence and gustiness of the wind field over the roof and in the wake of a building, a 3-D probabilistic trajectory model of flat-type wind-borne debris is developed in this study. The core of this methodology is a 6 degree-of-freedom deterministic model, derived from the governing equations of motion of the debris, and a Monte Carlo simulation engine used to account for the uncertainty resulting from vertical and lateral gust wind velocity components. The influence of several parameters, including initial wind speed, time step, gust sampling frequency, number of Monte Carlo simulations, and the extreme gust factor, on the accuracy of the proposed model is examined. For the purpose of validation and calibration, the simulated results from the 3-D probabilistic trajectory model are compared against the available wind tunnel test data. Results show that the maximum relative error between the simulated and wind tunnel test results of the average longitudinal position is about 20%, implying that the probabilistic model provides a reliable and effective means to predict the 3-D flight of the plate-type wind-borne debris.

• Wind and Structures
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• 제18권6호
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• pp.633-650
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• 2014

The uncertainty of extreme wind speeds is one key contributor to the uncertainty of wind loads and their effects on structures. The probability distribution of annual extreme wind speeds may be characterized using a classical Gumbel Type distribution. The expression that establishes the relationship between the extreme wind speeds at different recurrence periods and the corresponding coefficients of variation is formulated, and its efficacy is validated. The coefficients of variation are calibrated to be about 0.125 and 0.184 according to defined Chinese and US design specifications, respectively. Based on the wind data of 54 cities in China, 49 meteorological stations in the US, 3 stations in Singapore, the coefficients span intervals of (0.1, 0.35), (0.08, 0.20) and (0.06, 0.14), respectively. For hurricanes in the US, the coefficients range approximately from 0.3 to 0.4. This convenient technique is recommended as one alternative tool for coefficient of variation analyses in the future revisions of related codes. The sensitivities of coefficients of variation for 49 meteorological stations in the US are quantified and demonstrated. Some contradictions and incompatibilities can be clearly detected and illustrated by comparing the coefficients of variation obtained with different combinations of recurrence period wind data.

• Smart Structures and Systems
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• 제21권5호
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• pp.601-609
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• 2018

The estimated probabilistic model of wind data based on the conventional approach may have high discrepancy compared with the true distribution because of the uncertainty caused by the instrument error and limited monitoring data. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method has been developed in the companion paper and is conducted to formulate the joint probability density function (PDF) of wind speed and direction using the wind monitoring data of the investigated bridge. The established bivariate model of wind speed and direction only represents the features of available wind monitoring data. To characterize the stochastic properties of the wind parameters with the subsequent wind monitoring data, in this study, Bayesian inference approach considering the uncertainty is proposed to update the wind parameters in the bivariate probabilistic model. The slice sampling algorithm of Markov chain Monte Carlo (MCMC) method is applied to establish the multi-dimensional and complex posterior distribution which is analytically intractable. The numerical simulation examples for univariate and bivariate models are carried out to verify the effectiveness of the proposed method. In addition, the proposed Bayesian inference approach is used to update and optimize the parameters in the bivariate model using the wind monitoring data from the investigated bridge. The results indicate that the proposed Bayesian inference approach is feasible and can be employed to predict the bivariate distribution of wind speed and direction with limited monitoring data.