• Korean Journal of Construction Engineering and Management
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• v.6 no.4 s.26
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• pp.80-90
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• 2005

Activity durations retain probabilistic and stochastic natures due to diverse factors causing the delay or acceleration of activity completion. These natures make the final project duration to be a random variable. These factors are the major source of financial risk. Extending the Stochastic Project Scheduling Simulation system (SPSS) developed in previous research; this research presents a method to estimate how the final project duration behaves when activity durations change randomly. The final project cost is estimated by considering the fluctuation of indirect cost, which occurs due to the delay or acceleration of activity completion, along with direct cost assigned to an activity. The final project cost is estimated by considering how indirect cost behaves when activity duration change. The method quantifies the amount of contingency to cover the expected delay of project delivery. It is based on the quantitative analysis to obtain the descriptive statistics from the simulation outputs (final project durations). Existing deterministic scheduling method apply an arbitrary figures to the amount of delay contingency with uncertainty. However, the stochastic method developed in this research allows computing the amount of delay contingency with certainty and certain degree of confidence. An example project is used to illustrate the quantitative analysis method using simulation. When the statistical location and shape of probability distribution functions defining activity durations change, how the final project duration and cost behave are ascertained using automated sensitivity analysis method

• Korean Journal of Fisheries and Aquatic Sciences
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• v.40 no.6
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• pp.367-373
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• 2007

We assessed the stock of the southern bluefin tuna (SBT, Thunnus maccoyii) by applying the MULTIFAN-CL model. The model is spatially disaggregated, with the population and fisheries stratified into a number of regions within the overall stock range. Catch, effort, length-frequency, and tagging data from 1965 to 2003 were stratified by three regions and four quarters (Jan-Mar, Apr-Jun, Jul-Sept and Oct-Dec). These data were used to estimate the instantaneous fishing mortality (F), biomass, spawning biomass, recruitment, and so on. The Commission for the Conservation of Southern Bluefin Tuna (CCSBT) used only Japanese data and did not consider migration for the SBT stock assessment. By contrast, we used Japanese, Australian, New Zealand, Taiwanese, and Korean data, and considered migration. As a result, the estimated annual average F of all age classes was 0.073/yr and the F of age class 6-10 was the highest. The results also showed that the biomass and recruitment of SBT had declined significantly after 1965. Compared with the CCSBT results, the estimated spawning biomass in this study was lower and more uncertain. However, we will conduct a sensitivity analysis to get more accurate biological parameters and results. In addition, we need to use the bootstrap resampling method to quantify the uncertainty.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.99-99
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• 2022

Jeju island has a humid subtropical climate and this climate zone is expected to migrate northward toward the main land, Korea Peninsula, as temperature increases are accelerated. Vegetation type has been inevitably shifted along with the climatic change, having more subtropical species native in southeast Asia or even in Africa. With the forest composition shift, it becomes more important than ever to analyze the water balance of the forest wihth the ongoing as well as upcoming climate change. Here, we implemented the Ecosystem Demography Biosphere Model (ED2) by initializing the key variables using forest inventory data (diameter at breast height in 2012). Out of 10,000 parameter sets randomly generated from prior distribution distributions of each parameter (i.e., Monte-Carlo Method), we selected four behavioral parameter sets using remote-sensing data (LAI-MOD15A2H, GPP-MOD17A2H, and ET-MOD16A2, 8-days at 500-m during 2001-2005), and evaluated the performances using eddy-covariance carbon flux data (2012 Mar.-Sep. 30-min) and remote sensing data between 2006-2020. We simulated each of the four RCP scenarios (2.6, 4.5, 6.0, and 8.5) from four climate forcings (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5 from ISIMIP2b). Based on those 64 simulation sets, we estimate the changes in water balance resulting from the forest composition shift, and also uncertainty in the estimates and the sensitivity of the estimates to the parameters, climate forcings, and RCP scenarios.

• Geomechanics and Engineering
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• v.31 no.2
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• pp.129-147
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• 2022

The properties of soil are naturally highly variable and thus, to ensure proper safety and reliability, we need to test a large number of samples across the length and depth. In pile foundations, conducting field tests are highly expensive and the traditional empirical relations too have been proven to be poor in performance. The study proposes a state-of-art Particle Swarm Optimization (PSO) hybridized Artificial Neural Network (ANN), Extreme Learning Machine (ELM) and Adaptive Neuro Fuzzy Inference System (ANFIS); and comparative analysis of metaheuristic models (ANN-PSO, ELM-PSO, ANFIS-PSO) for prediction of bearing capacity of pile foundation trained and tested on dataset of nearly 300 dynamic pile tests from the literature. A novel ensemble model of three hybrid models is constructed to combine and enhance the predictions of the individual models effectively. The authenticity of the dataset is confirmed using descriptive statistics, correlation matrix and sensitivity analysis. Ram weight and diameter of pile are found to be most influential input parameter. The comparative analysis reveals that ANFIS-PSO is the best performing model in testing phase (R² = 0.85, RMSE = 0.01) while ELM-PSO performs best in training phase (R² = 0.88, RMSE = 0.08); while the ensemble provided overall best performance based on the rank score. The performance of ANN-PSO is least satisfactory compared to the other two models. The findings were confirmed using Taylor diagram, error matrix and uncertainty analysis. Based on the results ELM-PSO and ANFIS-PSO is proposed to be used for the prediction of bearing capacity of piles and ensemble learning method of joining the outputs of individual models should be encouraged. The study possesses the potential to assist geotechnical engineers in the design phase of civil engineering projects.

• Computers and Concrete
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• v.33 no.1
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• pp.55-75
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• 2024

The present study introduces an optimum performance soft computing model for predicting the compressive strength of high-performance concrete (HPC) by comparing models based on conventional (kernel-based, covariance function-based, and tree-based), advanced machine (least square support vector machine-LSSVM and minimax probability machine regressor-MPMR), and deep (artificial neural network-ANN) learning approaches using a common database for the first time. A compressive strength database, having results of 1030 concrete samples, has been compiled from the literature and preprocessed. For the purpose of training, testing, and validation of soft computing models, 803, 101, and 101 data points have been selected arbitrarily from preprocessed data points, i.e., 1005. Thirteen performance metrics, including three new metrics, i.e., a20-index, index of agreement, and index of scatter, have been implemented for each model. The performance comparison reveals that the SVM (kernel-based), ET (tree-based), MPMR (advanced), and ANN (deep) models have achieved higher performance in predicting the compressive strength of HPC. From the overall analysis of performance, accuracy, Taylor plot, accuracy metric, regression error characteristics curve, Anderson-Darling, Wilcoxon, Uncertainty, and reliability, it has been observed that model CS4 based on the ensemble tree has been recognized as an optimum performance model with higher performance, i.e., a correlation coefficient of 0.9352, root mean square error of 5.76 MPa, and mean absolute error of 4.1069 MPa. The present study also reveals that multicollinearity affects the prediction accuracy of Gaussian process regression, decision tree, multilinear regression, and adaptive boosting regressor models, novel research in compressive strength prediction of HPC. The cosine sensitivity analysis reveals that the prediction of compressive strength of HPC is highly affected by cement content, fine aggregate, coarse aggregate, and water content.

• Nuclear Engineering and Technology
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• v.49 no.2
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• pp.411-425
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• 2017

Using a probabilistic safety assessment, a risk evaluation framework for an aircraft crash into an interim spent fuel storage facility is presented. Damage evaluation of a detailed generic cask model in a simplified building structure under an aircraft impact is discussed through a numerical structural analysis and an analytical fragility assessment. Sequences of the impact scenario are shown in a developed event tree, with uncertainties considered in the impact analysis and failure probabilities calculated. To evaluate the influence of parameters relevant to design safety, risks are estimated for three specification levels of cask and storage facility structures. The proposed assessment procedure includes the determination of the loading parameters, reference impact scenario, structural response analyses of facility walls, cask containment, and fuel assemblies, and a radiological consequence analysis with dose-risk estimation. The risk results for the proposed scenario in this study are expected to be small relative to those of design basis accidents for best-estimated conservative values. The importance of this framework is seen in its flexibility to evaluate the capability of the facility to withstand an aircraft impact and in its ability to anticipate potential realistic risks; the framework also provides insight into epistemic uncertainty in the available data and into the sensitivity of the design parameters for future research.

• Computers and Concrete
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• v.15 no.2
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• pp.259-277
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• 2015

The lack of experimental studies on the mechanical behavior of reinforced concrete (RC) haunched beams leads to difficulties in statistical and reliability analyses. This study performs stochastic and reliability analyses of the ultimate shear capacity of RC haunched beams based on nonlinear finite element analysis. The main aim of this study is to investigate the influence of uncertainty in material properties and geometry parameters on the mechanical performance and shear capacity of RC haunched beams. Firstly, 65 experimentally tested RC haunched beams and prismatic beams are analyzed via deterministic nonlinear finite element method by a special program (ATENA) to verify the efficiency of utilized numerical models, the shear capacity and the crack pattern. The accuracy of nonlinear finite element analyses is verified by comparing the results of nonlinear finite element and experiments and both results are found to be in a good agreement. Afterwards, stochastic analyses are performed for each beam where the RC material properties and geometry parameters are assigned to take probabilistic values using an advanced simulating procedure. As a result of stochastic analysis, statistical parameters are determined. The statistical parameters are obtained for resistance bias factor and the coefficient of variation which were found to be equal to 1.053 and 0.137 respectively. Finally, reliability analyses are accomplished using the limit state functions of ACI-318 and ASCE-7 depending on the calculated statistical parameters. The results show that the RC haunched beams have higher sensitivity and riskiness than the RC prismatic beams.

• Journal of the Korean Institute of Landscape Architecture
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• v.34 no.5 s.118
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• pp.39-51
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• 2006

This study was carried out to provide guidance to environmental policy makers when deciding which assessment fields (biotic, abiotic, qualitative, functional) should have priority for ecological preservation and to develop an objective and scientific methodology by introducing the engineering concept of the fuzzy integral. The grant of weights was used the eigenvalues calculated by factor analysis, and the converted values of indicators were obtained in multiplying the arithmetic values and eigenvalues. The results of the appropriateness and reliability of assessment fields were examined over 0.6, and the results showed that the design of questionnaire presented no great problems. When the fuzzy integral was calculated to determine the rankings at ${\lambda}$=1, 2, 3, 4, 5, respectively, they were 0.646, 0.630, 0.943, 1.423, and 1.167 for the biotic field, 1.298, 1.400, 0.901, 0.580, and 1.456 for the abiotic field, 0.714, 0.674, 0.346, 0.674, and 1.610 in the qualitative field and 1.000, 0.973, 0.943, 1.024, and 1.008 in the functional field. The sensitivity to ${\lambda}$ value showed that ${\lambda}=4$ was the most suitable. In comparison with ${\lambda}=0$ (the arithmetic mean), the range of change was narrow. Because the range for ${\lambda}=4$ was narrower than my other values, ${\lambda}=4$ was sure to be available in ranking-decision. The fuzzy integral is expected to be a method for analyzing and filtering human thoughts. In the future, in order to overcome linguistic uncertainty and subjectivity, other fuzzy integral models including Sugeno's method, AHP, and so forth should be used.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.36-48
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• 2022

As a valid numerical method to obtain a high-resolution result of a flow field, computational fluid dynamics (CFD) have been widely used to study coolant flow and heat transfer characteristics in fuel rod bundles. However, the time-consuming, iterative calculation of Navier-Stokes equations makes CFD unsuitable for the scenarios that require efficient simulation such as sensitivity analysis and uncertainty quantification. To solve this problem, a reduced-order model (ROM) based on proper orthogonal decomposition (POD) and machine learning (ML) is proposed to simulate the flow field efficiently. Firstly, a validated CFD model to output the flow field data set of the rod bundle is established. Secondly, based on the POD method, the modes and corresponding coefficients of the flow field were extracted. Then, an deep feed-forward neural network, due to its efficiency in approximating arbitrary functions and its ability to handle high-dimensional and strong nonlinear problems, is selected to build a model that maps the non-linear relationship between the mode coefficients and the boundary conditions. A trained surrogate model for modes coefficients prediction is obtained after a certain number of training iterations. Finally, the flow field is reconstructed by combining the product of the POD basis and coefficients. Based on the test dataset, an evaluation of the ROM is carried out. The evaluation results show that the proposed POD-ROM accurately describe the flow status of the fluid field in rod bundles with high resolution in only a few milliseconds.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2733-2742
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• 2020

This paper presents an assessment of applicability of the multigroup cross sections generated with Monte Carlo tools to the fast reactor analysis based on transport calculations. 33-group cross section sets were generated for simple one- (1-D) and two-dimensional (2-D) sodium-cooled fast reactor problems using the SERPENT code and applied to deterministic steady-state and depletion calculations. Relative to the reference continuous-energy SERPENT results, with the transport corrected P₀ scattering cross section, the k-eff value was overestimated by 506 and 588 pcm for 1-D and 2-D problems, respectively, since anisotropic scattering is important in fast reactors. When the scattering order was increased to P₅, the 1-D and 2-D problem errors were increased to 577 and 643 pcm, respectively. A sensitivity and uncertainty analysis with the PERSENT code indicated that these large k-eff errors cannot be attributed to the statistical uncertainties of cross sections and they are likely due to the approximate anisotropic scattering matrices determined by scalar flux weighting. The anisotropic scattering cross sections were alternatively generated using the MC²-3 code and merged with the SERPENT cross sections. The mixed cross section set consistently reduced the errors in k-eff, assembly powers, and nuclide densities. For example, in the 2-D calculation with P3 scattering order, the k-eff error was reduced from 634 pcm to -223 pcm. The maximum error in assembly power was reduced from 2.8% to 0.8% and the RMS error was reduced from 1.4% to 0.4%. The maximum error in the nuclide densities at the end of 12-month depletion that occurred in ²³⁷Np was reduced from 3.4% to 1.5%. The errors of the other nuclides are also reduced consistently, for example, from 1.1% to 0.1% for ²³⁵U, from 2.2% to 0.7% for ²³⁸Pu, and from 1.6% to 0.2% for ²⁴¹Pu. These results indicate that the scalar flux weighted anisotropic scattering cross sections of SERPENT may not be adequate for application to fast reactors where anisotropic scattering is important.