• Structural Engineering and Mechanics
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• v.86 no.1
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• pp.85-92
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• 2023

This article examines the seismic vulnerability of soil nail wall structures. Detailed information regarding finite element modeling has been provided. The fragility function evaluates the relationship between ground motion intensities and the probability of surpassing a specific level of damage. The use of incremental dynamic analysis (IDA) has been applied to the soil nail wall against low to severe ground motions. In the nonlinear dynamic analysis of the soil nail wall, a set of twenty seismic ground motions with varying PGA ranges are used. The numerical results demonstrate that the soil-nailed wall reaction is extremely sensitive to earthquake ground vibrations under different intensity measures (IM). In addition, the analytical fragility curve is provided for various intensity values.

• Geomechanics and Engineering
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• v.1 no.1
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• pp.17-34
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• 2009

There has been growing agreement that geotechnical reliability-based design (RBD) is necessary for establishing more advanced and integrated design system. In this study, resistance factors for LRFD pile design using CPT results were investigated for axially loaded driven piles. In order to address variability in design methodology, different CPT-based methods and load-settlement criteria, popular in practice, were selected and used for evaluation of resistance factors. A total of 32 data sets from 13 test sites were collected from the literature. In order to maintain the statistical consistency of the data sets, the characteristic pile load capacity was introduced in reliability analysis and evaluation of resistance factors. It was found that values of resistance factors considerably differ for different design methods, load-settlement criteria, and load capacity components. For the total resistance, resistance factors for LCPC method were higher than others, while those for Aoki-Velloso's and Philipponnat's methods were in similar ranges. In respect to load-settlement criteria, 0.1B and Chin's criteria produced higher resistance factors than DeBeer's and Davisson's criteria. Resistance factors for the base and shaft resistances were also presented and analyzed.

• Ecology and Resilient Infrastructure
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• v.9 no.1
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• pp.24-35
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• 2022

For water quality management, it is necessary to continuously improve the forecasting by analyzing the past water quality, and a Data-driven model is emerging as an alternative. Because the Data-driven model is built based on a wide range of data, it is essential to apply the correlation analysis method for the combination of input variables to obtain more reliable results. In this study, the Gamma Test was applied as a preceding step to build a faster and more accurate data-driven water quality prediction model. First, a physical-based model (HSPF, EFDC) was operated to produce daily water quality reflecting the complexity of the watershed according to various hydrological conditions for Paldang Dam. The Gamma Test was performed on the water quality at the water quality prediction site (Paldangdam2) and major rivers flowing into the Paldang Dam, and the method of selecting the optimal input data combination was presented through the analysis results (Gamma, Gradient, Standar Error, V-Ratio). As a result of the study, the selection criteria for a more efficient combination of input data that can save time by omitting trial and error when building a data-driven model are presented.

• Nuclear Engineering and Technology
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• v.52 no.12
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• pp.2687-2698
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• 2020

Most of the machine learning-based intrusion detection tools developed for Industrial Control Systems (ICS) are trained on network packet captures, and they rely on monitoring network layer traffic alone for intrusion detection. This approach produces weak intrusion detection systems, as ICS cyber-attacks have a real and significant impact on the process variables. A limited number of researchers consider integrating process measurements. However, in complex systems, process variable changes could result from different combinations of abnormal occurrences. This paper examines recent advances in intrusion detection algorithms, their limitations, challenges and the status of their application in critical infrastructures. We also introduce the discussion on the similarities and conflicts observed in the development of machine learning tools and techniques for fault diagnosis and cybersecurity in the protection of complex systems and the need to establish a clear difference between them. As a case study, we discuss special characteristics in nuclear power control systems and the factors that constraint the direct integration of security algorithms. Moreover, we discuss data reliability issues and present references and direct URL to recent open-source data repositories to aid researchers in developing data-driven ICS intrusion detection systems.

• Computers and Concrete
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• v.32 no.6
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• pp.625-637
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• 2023

The application of ML approaches in determining the resisting capacity of fire damaged RC columns is introduced in this paper, on the basis of analysis data driven ML modeling. Considering the characteristics of the structural behavior of fire damaged RC columns, the representative five approaches of Kernel SVM, ANN, RF, XGB and LGBM are adopted and applied. Additional partial monotonic constraints are adopted in modelling, to ensure the monotone decrease of resisting capacity in RC column with fire exposure time. Furthermore, additional suggestions are also added to mitigate the heterogeneous composition of the training data. Since the use of ML approaches will significantly reduce the computation time in determining the resisting capacity of fire damaged RC columns, which requires many complex solution procedures from the heat transfer analysis to the rigorous nonlinear analyses and their repetition with time, the introduced ML approach can more effectively be used in large complex structures with many RC members. Because of the very small amount of experimental data, the training data are analytically determined from a heat transfer analysis and a subsequent nonlinear finite element (FE) analysis, and their accuracy was previously verified through a correlation study between the numerical results and experimental data. The results obtained from the application of ML approaches show that the resisting capacity of fire damaged RC columns can effectively be predicted by ML approaches.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.4
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• pp.356-365
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• 2014

This paper is the second in a series and aims to build a high-fidelity mathematical model for a propeller-driven airplane using the propeller's aerodynamics and inertial models, as developed in the first paper. It focuses on aerodynamic models for the fuselage, the main wing, and the stabilizers under the influence of the wake trailed from the propeller. For this, application of the vortex lattice method is proposed to reflect the propeller's wake effect on those aerodynamic surfaces. By considering the maneuvering flight states and the flow field generated by the propeller wake, the induced velocity at any point on the aerodynamic surfaces can be computed for general flight conditions. Thus, strip theory is well suited to predict the distribution of air loads over wing components and the viscous flow effect can be duly considered using the 2D aerodynamic coefficients for the airfoils used in each wing. These approaches are implemented in building a high-fidelity mathematical model for a propeller-driven airplane. Flight dynamic analysis modules for the trim, linearization, and simulation analyses were developed using the proposed techniques. The flight test results for a series of maneuvering flights with a scaled model were used for comparison with those obtained using the flight dynamics analysis modules to validate the usefulness of the present approaches. The resulting good correlations between the two data sets demonstrate that the flight characteristics of the propeller-driven airplane can be analyzed effectively through the integrated framework with the propeller and airframe aerodynamic models proposed in this study.

• Journal of Korea Multimedia Society
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• v.20 no.9
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• pp.1559-1566
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• 2017

Recently in the IoT(Internet of Things) environment, a data collection in real-time through device's sensor has increased with an emergence of various devices. Collected data from IoT environment shows a large scale, non-uniform generation cycle and atypical. For this reason, the distributed processing technique is required to analyze the IoT sensor data. However if you do not consider the optimal scheduling for data and the processor of IoT in a distributed processing environment complexity increase the amount in assigning a task, the user is difficult to guarantee the QoS(Quality of Service) for the sensor data. In this paper, we propose APQTA(Adaptive Priority Queue-driven Task Allocation method for sensor data processing) to efficiently process the sensor data generated by the IoT environment. APQTA is to separate the data into job and by applying the priority allocation scheduling based on the deadline to ensure that guarantee the QoS at the same time increasing the efficiency of the data processing.

• Proceedings of the IEEK Conference
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• 2004.08c
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• pp.764-767
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• 2004

The performance of a speech recognition system is generally degraded in telephone environment because of distortions caused by background noise and various channel characteristics. In this paper, data-driven temporal filters are investigated to improve the performance of a specific recognition task such as telephone speech. Three different temporal filtering methods are presented with recognition results for Korean connected-digit telephone speech. Filter coefficients are derived from the cepstral domain feature vectors using the principal component analysis.

• Wind and Structures
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• v.9 no.2
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• pp.159-178
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• 2006

For the slender and flexible cable supported bridges, identification of all the flutter derivatives for the vertical, lateral and torsional motions is essential for its stability investigation. In all, eighteen flutter derivatives may have to be considered, the identification of which using a three degree-of-freedom elastic suspension system has been a challenging task. In this paper, a system identification technique, known as covariance-driven stochastic subspace identification (COV-SSI) technique, has been utilized to extract the flutter derivatives for a typical bridge deck. This method identifies the stochastic state-space model from the covariances of the output-only (stochastic) data. All the eighteen flutter derivatives have been simultaneously extracted from the output response data obtained from wind tunnel test on a 3-DOF elastically suspended bridge deck section-model. Simplicity in model suspension and measurements of only output responses are additional motivating factors for adopting COV-SSI technique. The identified discrete values of flutter derivatives have been approximated by rational functions.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.893-903
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• 2022

Recently, computer vision (CV)-based safety monitoring (i.e., object detection) system has been widely researched in the construction industry. Sufficient and high-quality data collection is required to detect objects accurately. Such data collection is significant for detecting small objects or images from different camera angles. Although several previous studies proposed novel data augmentation and synthetic data generation approaches, it is still not thoroughly addressed (i.e., limited accuracy) in the dynamic construction work environment. In this study, we proposed a game engine-driven synthetic data generation model to enhance the accuracy of the CV-based object detection model, mainly targeting small objects. In the virtual 3D environment, we generated synthetic data to complement training images by altering the virtual camera angles. The main contribution of this paper is to confirm whether synthetic data generated in the game engine can improve the accuracy of the CV-based object detection model.