• Wind and Structures
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• v.36 no.6
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• pp.367-377
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• 2023

This study proposes a few-shot learning model for extrapolating the wind pressure of scaled experiments to full-scale measurements. The proposed ML model can use scaled experimental data and a few full-scale tests to accurately predict the remaining full-scale data points (for new specimens). This model focuses on extrapolating the prediction to different scales while existing approaches are not capable of accurately extrapolating from scaled data to full-scale data in the wind engineering domain. Also, the scaling issue observed in wind tunnel tests can be partially resolved via the proposed approach. The proposed model obtained a low mean-squared error and a high coefficient of determination for the mean and standard deviation wind pressure coefficients of the full-scale dataset. A parametric study is carried out to investigate the influence of the number of selected shots. This technique is the first of its kind as it is the first time an ML model has been used in the wind engineering field to deal with extrapolation in wind performance prediction. With the advantages of the few-shot learning model, physical wind tunnel experiments can be reduced to a great extent. The few-shot learning model yields a robust, efficient, and accurate alternative to extrapolating the prediction performance of structures from various model scales to full-scale.

• Korean Journal of Computational Design and Engineering
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• v.21 no.2
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• pp.111-121
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• 2016

The plant equipment usually has a long life cycle. During its O&M (Operation & Maintenance) phase, since the occurrence of an accident of offshore plant equipment causes catastrophic damage, it is necessary to make more efforts for managing critical offshore equipment. Nowadays due to the emerging ICTs (Information Communication Technologies) and sensor technologies, it is possible to gather the health status data of important offshore equipment and their environment data, which leads to much concern on CBM (Condition-Based Maintenance). In this study, we will propose an approach to estimate the remaining lifetime of an offshore plant equipment (pump tower) based on gathered ocean environment data.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.9 s.180
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• pp.2381-2386
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• 2000

Surface replication is playing an important role in the assessment of creep damage and remaining life of high temperature components. As the replication procedures, however, have not been standardized in domestic industry, its standardization is proposed in this study. For this purpose, the 2.25Cr-IMo steel was heat treated(5 min at 1,300 0C and oil quenched) to produce a simulated HAZ microstructure, and crept in air at 575 0C and under 120 MPa to produce artificial cavities. Then, the effect of surface preparation procedures on the quality of replicas was investigated using this sample. As a result, it was demonstrated that the presence of cavities may be observed readily or missed depending on the surface preparation procedures followed. Therefore it is essential to repeat three polishing/etching cycles at least in order to reveal cavitation damage accurately, even though it may be tedious or time-consuming.

• International Journal of Naval Architecture and Ocean Engineering
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• v.12 no.1
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• pp.657-666
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• 2020

A reliable and cost-effective technique for the development of corrosion damage model is introduced to predict nonlinear time-dependent corrosion wastage of steel structures. A detailed explanation on how to propose a generalised mathematical formulation of the corrosion model is investigated in this paper (Part I), and verification and application of the developed method are covered in the following paper (Part II) by adopting corrosion data of a ship's ballast tank structure. In this study, probabilistic approaches including statistical analysis were applied to select the best fit probability density function (PDF) for the measured corrosion data. The sub-parameters of selected PDF, e.g., the largest extreme value distribution consisting of scale, and shape parameters, can be formulated as a function of time using curve fitting method. The proposed technique to formulate the refined time-dependent corrosion wastage model (TDCWM) will be useful for engineers as it provides an easy and accurate prediction of the 1) starting time of corrosion, 2) remaining life of the structure, and 3) nonlinear corrosion damage amount over time. In addition, the obtained outcome can be utilised for the development of simplified engineering software shown in Appendix B.

• Journal of the Korean Society of Safety
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• v.18 no.1
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• pp.108-115
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• 2003

Bridges are rated at two levels by either Load Factor Design (LFD) or Allowable Stress Design (ASD). The lower level rating is called Inventory Rating and the upper level rating is called Operating Rating. To maintain bridges effectively, there is an urgent need to assess actual bridge loading carrying capacity and to predict their remaining life from a system reliability viewpoint. The lifetime functions are introduced and explained to predict the time-dependent failure probability. The bridge studied in this paper was built 30 years ago in rural area. For this bridge, the load test and rehabilitation were conducted. The time-dependent system failure probability is predicted with or without rehabilitation. As a case study, an optional rehabilitation is suggested, and fir this rehabilitation, load rating is computed and the time-dependent system failure probability is predicted. Based on rehabilitation costs and extended service lifes, the optimal rehabilitation is suggested.

• Journal of Energy Engineering
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• v.8 no.4
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• pp.605-611
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• 1999

Conventional methods for assessing remaining life of critical high temperature components in fossil power plants rely on nondestructive inspection practices and accompanying life analysis based on fracture mechanics By using these conventional methods. It has been difficult to perform uninterrupted in-service inspection for life prediction. Thus, efforts have been made for developing on-line remaining life monitoring systems employing information on the shape of structures, operating variables and material properties. In thus study, a software for on-line life monitoring system which performs real-time life evaluation of a high temperature system headers was developed. The software is capable of evaluating creep and fatigue life usage from the real-time stress data calculated by using temperatures/stress transfer Green functions derived in advance for the specific headers. The major benefits of the developed software life in determining future operating schedule, inspection interval, and replacement plan by monitoring real-time life usage based on prior operating history.

• Korean Journal of Metals and Materials
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• v.50 no.8
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• pp.583-589
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• 2012

This work is concerned with a statistical approach to the life prediction on irradiation-assisted stress corrosion cracking (IASCC) of stainless steel (SS) for core internals of a pressurized water reactor (PWR). The previous results of the time-to-failure of IASCC measured on neutron-irradiated stainless steel components were statistically analyzed in terms of stress and irradiation. The accelerating life testing model of IASCC of cold worked Type 316 SS was established based on an inverse power model with two stress-variables, the applied stress and irradiation dose. Considering the variation of the yield strength and applied stress with the irradiation dose in the model, the remaining life of the baffle former bolt was statistically predicted during operation under complex environments of stress and irradiation.

• Journal of IKEEE
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• v.26 no.4
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• pp.531-536
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• 2022

In order to efficeintly manage a battery, it is important to accurately estimate and manage the SOH(State of Health) and RUL(Remaining Useful Life) of the batteries. Even if the batteries are of the same type, the characteristics such as facility capacity and voltage are different, and when the battery for the training model and the battery for prediction through the model are different, there is a limit to measuring the accuracy. In this paper, We proposed the entropy index using voltage distribution and discharge time is generalized, and four batteries are defined as a training set and a test set alternately one by one to predict the health status of batteries through linear regression analysis of machine learning. The proposed method showed a high accuracy of more than 95% using the MAPE(Mean Absolute Percentage Error).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.4A
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• pp.343-352
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• 2010

Recently, many researchers have been carried out to estimate more controlled service life and long-term performance of carbonated concrete structures. Durability analysis and design based on probability have been induced to new concrete structures for design. This paper provides a carbonation prediction model based on the Fick's 1st law of diffusion using statistic data of carbonated concrete structures and the probabilistic analysis of the durability performance has been carried out by using a Bayes' theorem. The influence of concerned design parameters such as $CO_2$ diffusion coefficient, atmospheric $CO_2$ concentration, absorption quantity of $CO_2$ and the degree of hydration was investigated. Using a monitoring data, this model which was based on probabilistic approach was predicted a carbonation depth and a remaining service life at a variety of environmental concrete structures. Form the result, the application method using a realistic carbonation prediction model can be to estimate erosion-open-time, controlled durability and to determine a making decision for suitable repair and maintenance of carbonated concrete structures.

• Nuclear Engineering and Technology
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• v.48 no.5
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• pp.1184-1191
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• 2016

This paper introduces the development of a transient monitoring system to detect the early stage of a transient, to identify the type of the transient scenario, and to inform an operator with the remaining time to turbine trip when there is no operator's relevant control. This study focused on the transients originating from a secondary system in nuclear power plants (NPPs), because the secondary system was recognized to be a more dominant factor to make unplanned turbine-generator trips which can ultimately result in reactor trips. In order to make the proposed methodology practical forward, all the transient scenarios registered in a simulator of a 1,000 MWe pressurized water reactor were archived in the transient pattern database. The transient patterns show plant behavior until turbine-generator trip when there is no operator's intervention. Meanwhile, the operating data periodically captured from a plant computer is compared with an individual transient pattern in the database and a highly matched section among the transient patterns enables isolation of the type of transient and prediction of the expected remaining time to trip. The transient pattern database consists of hundreds of variables, so it is difficult to speedily compare patterns and to draw a conclusion in a timely manner. The transient pattern database and the operating data are, therefore, converted into a smaller dimension using the principal component analysis (PCA). This paper describes the process of constructing the transient pattern database, dealing with principal components, and optimizing similarity measures.