• Architectural research
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• v.25 no.1
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• pp.1-9
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• 2023

Structural ageing influences the structural performance in a negative way by reducing the seismic resilience of the structure which makes it a major concern around the world. Retrofitting is considered to be a pragmatic and feasible solution to address this issue. Numerous retrofitting techniques are devised by researchers over the years. The viability of using steel bracings as retrofitting component is evaluated on a G+30 storied building model designed according to ACI318-14 and ASCE 7-16. Four different types of steel bracing arrangements (V, Inverted V/ Chevron, Cross/ X, Diagonal) are assessed in the model developed in commercial nu-merical analysis software while considering both material and geometric nonlinearities. Reducing displacement and cost in the structures indicates that the design is safe and economical. Therefore, the purpose of this article is to find the best bracing system that causes minimum displacement, which indicates maximum lateral stiffness. To evaluate the seismic vulnerability of each system, incremental dynamic analysis was conducted to develop fragility curves, followed by the formation of collapse margin ratio (CMR) as stipulated in FEMA P695 and finally, a cost estimation was made for each system. The outcomes revealed that the effects of ge-ometric nonlinearity tend to evoke hazardous consequences if not considered in the structural design. Probabilistic seismic and economic probes indicated the superior performance of V braced frame system and its competency to be a germane technique for retrofitting.

• Composites Research
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• v.21 no.6
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• pp.23-30
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• 2008

In this paper, the effect of the natural aging on the strength distribution and structural service life of a Filament Wound (FW) composite pressure vessel was studied. The fiber failure strain, which is varied significantly, was considered as the design random variable and the strength analysis was carried out by probabilistic numerical approach. The progressive failure analysis technique and the First Order Reliability Method (FORM) were embedded in this numerical model. As the calculation results, the probability of failure was obtained for each aging time steps and it is found that the strength degradation in FW composite pressure vessel, due to the natural aging, appears within 10 year-aging-time. As an example of the life prediction under natural aging using arbitrary laminated model, the service lifetime of 13 years was predicted based on the probability of failure of 2.5% and the design pressure of 3,250 psi.

• Structural Engineering and Mechanics
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• v.86 no.5
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• pp.647-661
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• 2023

It is recognized that the installation of energy dissipation devices, such as the tuned mass damper (TMD), decreases the dynamic response of structures, however, the best parameters of each device persist hard to determine. Unlike many works that perform only a deterministic optimization, this work proposes a complete methodology to minimize the dynamic response of footbridges by optimizing the parameters of multiple tuned mass dampers (MTMD) taking into account uncertainties present in the parameters of the structure and also of the human excitation. For application purposes, a steel footbridge, based on a real structure, is studied. Three different scenarios for the MTMD are simulated. The proposed robust optimization problem is solved via the Circle-Inspired Optimization Algorithm (CIOA), a novel and efficient metaheuristic algorithm recently developed by the authors. The objective function is to minimize the mean maximum vertical displacement of the footbridge, whereas the design variables are the stiffness and damping constants of the MTMD. The results showed the excellent capacity of the proposed methodology, reducing the mean maximum vertical displacement by more than 36% and in a computational time about 9% less than using a classical genetic algorithm. The results obtained by the proposed methodology are also compared with results obtained through traditional TMD design methods, showing again the best performance of the proposed optimization method. Finally, an analysis of the maximum vertical acceleration showed a reduction of more than 91% for the three scenarios, leading the footbridge to acceleration values below the recommended comfort limits. Hence, the proposed methodology could be employed to optimize MTMD, improving the design of footbridges.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6C
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• pp.349-358
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• 2008

In performing the reliability analysis for predicting the settlement with time of alluvial clay layer at Kobe airport, the uncertainties of geotechnical properties were examined based on the stochastic and probabilistic theory. By using Terzaghi's consolidation theory as the objective function, the failure probability was normalized based on AFOSM method. As the result of reliability analysis, the occurrence probabilities for the cases of the target settlement of ${\pm}10%,\;{\pm}25%$ of the total settlement from the deterministic analysis were 30~50%, 60%~90%, respectively. Considering that the variation coefficients of input variable are almost similar as those of past researches, the acceptable error range of the total settlement would be expected in the range of 10% of the predicted total settlement. As the result of sensitivity analysis, the factors which affect significantly on the settlement analysis were the uncertainties of the compression coefficient Cc, the pre-consolidation stress Pc, and the prediction model employed. Accordingly, it is very important for the reliable prediction with high reliability to obtain reliable soil properties such as Cc and Pc by performing laboratory tests in which the in-situ stress and strain conditions are properly simulated.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.6
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• pp.61-67
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• 2023

The rise of 5G and the proliferation of smart devices have underscored the significance of multi-access edge computing (MEC). Amidst this trend, interest in effectively processing computation-intensive and latency-sensitive applications has increased. This study investigated a novel task offloading strategy considering the probabilistic MEC environment to address these challenges. Initially, we considered the frequency of dynamic task requests and the unstable conditions of wireless channels to propose a method for minimizing vehicle power consumption and latency. Subsequently, our research delved into a deep reinforcement learning (DRL) based offloading technique, offering a way to achieve equilibrium between local computation and offloading transmission power. We analyzed the power consumption and queuing latency of vehicles using the deep deterministic policy gradient (DDPG) and deep Q-network (DQN) techniques. Finally, we derived and validated the optimal performance enhancement strategy in a vehicle based MEC environment.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.37 no.1
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• pp.1-8
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• 2024

This paper employs stochastic processing techniques to analyze explosion risks in plant facilities based on explosion return periods. Release probability is calculated using data from the Health and Safety Executive (HSE), along with annual leakage frequency per plant provided by DNV. Ignition probability, derived from various researchers' findings, is then considered to calculate the explosion return period based on the release quantity. The explosion risk is assessed by examining the volume, radius, and blast load of the vapor cloud, taking into account the calculated explosion return period. The reference distance for the design blast load model is determined by comparing and analyzing the vapor cloud radius according to the return period, historical vapor cloud explosion cases, and blast-resistant design guidelines. Utilizing the multi-energy method, the blast load range corresponding to the explosion return period is presented. The proposed return period serves as a standard for the design blast load model, established through a comparative analysis of vapor cloud explosion cases and blast-resistant design guidelines. The outcomes of this study contribute to the development of a performance-based blast-resistant design framework for plant facilities.

• Steel and Composite Structures
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• v.51 no.6
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• pp.679-695
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• 2024

This paper presents a novel technique that combines machine learning (ML) with moth-flame optimization (MFO) methods to predict the axial compressive strength (ACS) of concrete filled double skin steel tubes (CFDST) columns. The proposed model is trained and tested with a dataset containing 125 tests of the CFDST column subjected to compressive loading. Five ML models, including extreme gradient boosting (XGBoost), gradient tree boosting (GBT), categorical gradient boosting (CAT), support vector machines (SVM), and decision tree (DT) algorithms, are utilized in this work. The MFO algorithm is applied to find optimal hyperparameters of these ML models and to determine the most effective model in predicting the ACS of CFDST columns. Predictive results given by some performance metrics reveal that the MFO-CAT model provides superior accuracy compared to other considered models. The accuracy of the MFO-CAT model is validated by comparing its predictive results with existing design codes and formulae. Moreover, the significance and contribution of each feature in the dataset are examined by employing the SHapley Additive exPlanations (SHAP) method. A comprehensive uncertainty quantification on probabilistic characteristics of the ACS of CFDST columns is conducted for the first time to examine the models' responses to variations of input variables in the stochastic environments. Finally, a web-based application is developed to predict ACS of the CFDST column, enabling rapid practical utilization without requesting any programing or machine learning expertise.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.579-584
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• 2024

Currently, large-scale, deep construction is being carried out adjacent to subway tracks in korea. when excavating adjacent to each other, it is very important to ensure the safety of earth retaining structures and underground structures. therefore, we are managing the safety of the subway by introducing an automated measurement system. deformation of the subway track during adjacent excavation may affect train running stability. this is a factor that can be linked to train derailments. however, current subway track safety evaluation using automated measurement systems relies only on the maximum value of measured data. therefore, a method to improve the usability of automated measurement system results is needed. in this study, we utilized a technique that can quantitatively evaluate the measurement results of a large amount of subway track deformation. a safety evaluation was conducted on subway track deformation due to adjacent excavation using a vast amount of data using probabilistic statistical analysis techniques.

• Journal of the Korean Institute of Landscape Architecture
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• v.45 no.6
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• pp.10-27
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• 2017

Recently, the importance of recognizing the natural environment and the need for its conservation are increasing due to rapid urbanization. Suncheon Bay, designated as Scenic Site No. 41 and one of the World's Five Greatest Coastal Wetlands, is the only tideland among the tidal flats in Korea, which has salt marsh reserves. It has high conservation value from the ecological aspect. In addition to the Suncheon Bay National Garden, it provides various benefits not only to visitors but to local residents as well in terms of economics, environmental issues, and history and cultural aspects. Two million tourists visit the site annually, which has constantly highlighted the limits of ecological capacity. The valuation of the Suncheon Bay wetland is more important for the sustainability of the Suncheon Bay wetland than for its value as a tourism resource for the activation of the local economy. This study used the Logit model, which is commonly used among probabilistic choice models, to evaluate the economic value of Suncheon Bay wetland with the contingent valuation method(CVM). Applying the conservation value of the Suncheon Bay wetland to the benefit of KRW 8,200 for 1 person and 1 day, the benefit from exploration is KRW 2,050, the management and conservation value is KRW 3,034, and the heritage value is KRW 3,116. The results of this study are that benefit from the annual exploration of Suncheon Bay wetland was KRW 44.3 in billion, the management and conservation value was KRW 6.55 in billion, and the heritage value was KRW 6.73 in billion. When converted to the number of paying visitors per year, the conservation value is about KRW 177.1 billion. This study was conducted to evaluate the use and conservation aspects of the economic value of Suncheon Bay wetland. Based on the latent value of the Suncheon Bay wetland, it provides basic data about the efficient management and policy establishment of Suncheon Bay wetland. The study is significant in that the ecological sustainability of the Suncheon bay wetland and the value of non-marketable were evaluated based on the recognition of 'benefit through exploration', 'management and conservation value' and 'value of heritage'. It can be used as policy decision data on the integrated collection of the admission fee of the Suncheon Bay wetland and Suncheon Bay National Garden.

• Economic and Environmental Geology
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• v.36 no.3
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• pp.191-200
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• 2003

Investigations on the contaminated lands due to heavy metals from mining activities or hydrocarbons from oil spillage for example, should be planned based on specific fitness-for-purpose criteria(FFP criteria). A FFP criterion is site specific or varies with situation, based on which not only the data quality but also the decision quality can be determined. The limiting factors on the qualities can be, for example, the total budget for the investigation, regulatory guidance or expert's subjective fitness-for-purpose criterion. This paper deals with planning of investigation methods that can satisfy each suggested FFP criterion based on economic factors and the data quality. To this aim, a probabilistic loss function was applied to derive the cost effective investigation method that balances the measurement uncertainty, which estimates the degree of the data quality, with the decision quality. In addition, investigation planning methods when the objectives of investigations do not lie in the classification of the land but simply in producing the estimation of the mean concentration of the contaminant at the site(e.g. for the use in risk assessment), were also suggested. Furthermore, the efficient allocation of resources between sampling and analysis was also devised. These methods were applied to the two contaminated sites in the UK to test the validity of each method.