• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.709-720
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• 2010

This paper describes the modeling of climate change impact on drought using a conceptual soil moisture model and presents the results of the modeling approach. The future climate series is obtained by scaling the historical series, informed by CCCma CGCM3-T63 with A2 green house emission scenario, using a daily scaling method that considers changes in the future monthly precipitation and potential evapotranspiration as well as in the daily precipitation distribution. The majority of the modeling results indicate that there will be more frequent drought in Korea in the future.

• Journal of Advanced Navigation Technology
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• v.22 no.2
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• pp.163-167
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• 2018

Probabilistic approach is applied to the experiment of Probability Density Function to get the information. Especially this method will be useful to make the montage to compare similarity. But in the case of art painting, it is more difficult than montage image. In this case, we should study the habit of painter with characteristic point in the paintings. Especially we will study characteristic point in the oil paintings to decide truth or falsehood in this paper.

• Structural Engineering and Mechanics
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• v.87 no.1
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• pp.1-18
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• 2023

The paper presents a Bayesian Finite element (FE) model updating methodology by utilizing modal data. The dynamic condensation technique is adopted in this work to reduce the full system model to a smaller model version such that the degrees of freedom (DOFs) in the reduced model correspond to the observed DOFs, which facilitates the model updating procedure without any mode-matching. The present work considers both the MPV and the covariance matrix of the modal parameters as the modal data. Besides, the modal data identified from multiple setups is considered for the model updating procedure, keeping in view of the realistic scenario of inability of limited number of sensors to measure the response of all the interested DOFs of a large structure. A relationship is established between the modal data and structural parameters based on the eigensystem equation through the introduction of additional uncertain parameters in the form of modal frequencies and partial mode shapes. A novel sampling strategy known as the Metropolis-within-Gibbs (MWG) sampler is proposed to sample from the posterior Probability Density Function (PDF). The effectiveness of the proposed approach is demonstrated by considering both simulated and experimental examples.

• Coupled systems mechanics
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• v.12 no.5
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• pp.429-444
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• 2023

The comprehension and structural modeling of masonry constructions is fundamental to safeguard the integrity of built cultural assets and intervene through adequate actions, especially in earthquake-prone regions. Despite the availability of several modeling strategies and modern computing power, modeling masonry remains a great challenge because of still demanding computational efforts, constraints in performing destructive or semi-destructive in-situ tests, and material uncertainties. This paper investigates the shear behavior of masonry walls by applying a plane-stress FE continuum model with the Modified Masonry-like Material (MMLM). Epistemic uncertainty affecting input parameters of the MMLM is considered in a probabilistic framework. After appointing a suitable probability density function to input quantities according to prior engineering knowledge, uncertainties are propagated to outputs relying on gPCE-based surrogate models to considerably speed up the forward problem-solving. The sensitivity of the response to input parameters is evaluated through the computation of Sobol' indices pointing out the parameters more worthy to be further investigated, when dealing with the seismic assessment of masonry buildings. Finally, masonry mechanical properties are calibrated in a probabilistic setting with the Bayesian approach to the inverse problem based on the available measurements obtained from the experimental load-displacement curves provided by shear compression in-situ tests.

• Wind and Structures
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• v.18 no.6
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• pp.693-715
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• 2014

Stochastic processes are used to represent phenomena in many diverse fields. Numerical simulation method is widely applied for the solution to stochastic problems of complex structures when alternative analytical methods are not applicable. In some practical applications the stochastic processes show non-Gaussian properties. When the stochastic processes deviate significantly from Gaussian, techniques for their accurate simulation must be available. The various existing simulation methods of non-Gaussian stochastic processes generally can only simulate super-Gaussian stochastic processes with the high-peak characteristics. And these methodologies are usually complicated and time consuming, not sufficiently intuitive. By revealing the inherent coupling effect of the phase and amplitude part of discrete Fourier representation of random time series on the non-Gaussian features (such as skewness and kurtosis) through theoretical analysis and simulation experiments, this paper presents a novel approach for the simulation of non-Gaussian stochastic processes with the prescribed amplitude probability density function (PDF) and power spectral density (PSD) by amplitude modulation and phase reconstruction. As compared to previous spectral representation method using phase modulation to obtain a non-Gaussian amplitude distribution, this non-Gaussian phase reconstruction strategy is more straightforward and efficient, capable of simulating both super-Gaussian and sub-Gaussian stochastic processes. Another attractive feature of the method is that the whole process can be implemented efficiently using the Fast Fourier Transform. Cases studies demonstrate the efficiency and accuracy of the proposed algorithm.

• Journal of Korea Water Resources Association
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• v.49 no.5
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• pp.411-422
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• 2016

Rainfall-runoff modeling in conjunction with rainfall frequency analysis has been widely used for estimating design floods in South Korea. However, uncertainties associated with underlying distribution and sampling error have not been properly addressed. This study applied a Bayesian method to quantify the uncertainties in the rainfall frequency analysis along with Gumbel distribution. For a purpose of comparison, a probability weighted moment (PWM) was employed to estimate confidence interval. The uncertainties associated with design rainfalls were quantitatively assessed using both Bayesian and PWM methods. The results showed that the uncertainty ranges with PWM are larger than those with Bayesian approach. In addition, the Bayesian approach was able to effectively represent asymmetric feature of underlying distribution; whereas the PWM resulted in symmetric confidence interval due to the normal approximation. The use of long period data provided better results leading to the reduction of uncertainty in both methods, and the Bayesian approach showed better performance in terms of the reduction of the uncertainty.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Smart Structures and Systems
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• v.17 no.6
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• pp.1087-1105
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• 2016

Traffic load and volume is one of the most important physical quantities for bridge safety evaluation and maintenance strategies formulation. This paper aims to conduct the statistical analysis of traffic volume information and the multimodal modeling of gross vehicle weight (GVW) based on the monitoring data obtained from the weigh-in-motion (WIM) system instrumented on the arch Jiubao Bridge located in Hangzhou, China. A genetic algorithm (GA)-based mixture parameter estimation approach is developed for derivation of the unknown mixture parameters in mixed distribution models. The statistical analysis of one-year WIM data is firstly performed according to the vehicle type, single axle weight, and GVW. The probability density function (PDF) and cumulative distribution function (CDF) of the GVW data of selected vehicle types are then formulated by use of three kinds of finite mixed distributions (normal, lognormal and Weibull). The mixture parameters are determined by use of the proposed GA-based method. The results indicate that the stochastic properties of the GVW data acquired from the field-instrumented WIM sensors are effectively characterized by the method of finite mixture distributions in conjunction with the proposed GA-based mixture parameter identification algorithm. Moreover, it is revealed that the Weibull mixture distribution is relatively superior in modeling of the WIM data on the basis of the calculated Akaike's information criterion (AIC) values.

• Journal of the Korean Society of Hazard Mitigation
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• v.10 no.1
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• pp.73-80
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• 2010

The optimal design of water distribution system have started with the least cost design of single objective function using fixed hydraulic variables, eg. fixed water demand and pipe roughness. However, more adequate design is accomplished with considering uncertainties laid on water distribution system such as uncertain future water demands, resulting in successful estimation of real network's behaviors. So, many researchers have suggested a variety of approaches to consider uncertainties in water distribution system using uncertainties quantification methods and the optimal design of multi-objective function is also studied. This paper suggests the new approach of a multi-objective optimization seeking the minimum cost and maximum robustness of the network based on two uncertain variables, nodal demands and pipe roughness uncertainties. Total design procedure consists of two folds: least cost design and final optimal design under uncertainties. The uncertainties of demands and roughness are considered with Latin Hypercube sampling technique with beta probability density functions and multi-objective genetic algorithms (MOGA) is used for the optimization process. The suggested approach is tested in a case study of real network named the New York Tunnels and the applicability of new approach is checked. As the computation time passes, we can check that initial populations, one solution of solutions of multi-objective genetic algorithm, spread to lower right section on the solution space and yield Pareto Optimum solutions building Pareto Front.

• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.8 no.2
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• pp.123-133
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• 2010

A statistical evaluation methodology was developed to determine the compliance of candidate waste stream with clearance criteria based upon distribution of radionuclide in a waste stream at a certain confidence level. For the cases where any information on the radionuclide distribution is not available, the relation between arithmetic mean of radioactivity concentration and its acceptable maximum standard deviation was demonstrated by applying widely-known Markov Inequality and One-side Chebyshev Inequality. The relations between arithmetic mean and its acceptable maximum standard deviation were newly derived for normally or lognormally distributed radionuclide in a waste stream, using probability density function, cumulative density function, and other statistical relations. The evaluation methodology was tested for a representative case at 95% of confidence level and 100 Bq/g of clearance level of radioactivity concentration, and then the acceptable range of standard deviation at a given arithmetic mean was quantitatively shown and compared, by varying the type of radionuclide distribution. Furthermore, it was statistically demonstrated that the allowable range of clearance can be expanded, even at the same confidence level, if information on the radionuclide distribution is available.