• Computers and Concrete
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• 제29권 6호
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• pp.433-444
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• 2022

Concrete's compressive strength is widely studied in order to understand many qualities and the grade of the concrete mixture. Conventional civil engineering tests involve time and resources consuming laboratory operations which results in the deterioration of concrete samples. Proposing efficient non-destructive models for the prediction of concrete compressive strength will certainly yield advancements in concrete studies. In this study, the efficiency of using radial basis function neural network (RBFNN) which is not common in this field, is studied for the concrete compressive strength prediction. Complementary studies with back propagation neural network (BPNN), which is commonly used in this field, have also been carried out in order to verify the efficiency of RBFNN for compressive strength prediction. A total of 13 input parameters, including novel ones such as cement's and fly ash's compositional information, have been employed in the prediction models with RBFNN and BPNN since all these parameters are known to influence concrete strength. Three different train: test ratios were tested with both models, while different hidden neurons, epochs, and spread values were introduced to determine the optimum parameters for yielding the best prediction results. Prediction results obtained by RBFNN are observed to yield satisfactory high correlation coefficients and satisfactory low mean square error values when compared to the results in the previous studies, indicating the efficiency of the proposed model.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 2004년도 추계학술발표회 발표논문집
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• pp.277-278
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• 2004

• 한국재난정보학회 논문집
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• 제13권3호
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• pp.332-339
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• 2017

노후화된 구조물의 경우 준공 후 시간이 지나 구조물의 정보가 유실되는 경우가 많으며, 시공관련 자료에 대한 정보 부족으로 인해 구조물의 잔존수명을 예측하는데 큰 어려움이 있다. 본 연구에서는 현장에서 채취한 코어 시험체를 기반으로 각종 현장 및 실내시험법을 통한 내구성을 평가하고 이를 토대로 FEM 해석기법을 활용하여 콘크리트 구조물의 내구수명을 예측하였다. 그 결과, 중성화 속도계수는 $5.38E-6(cm^2/day)$로 매우 진행 속도가 낮은 것으로 나타났으며, 탄산화 및 염해에 의한 복합열화의 발생 가능성은 매우 낮은 것으로 확인되었다

• 기본간호학회지
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• 제18권3호
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• pp.317-327
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• 2011

Purpose: The purpose of this study was to contribute data toward prevention from advancing dementia and also prevention of deterioration in cognitive impairment by constructing an optimal prediction model and verifying factors influencing cognitive impairment in elders with dementia who reside at home. Methods: The participants in this study were 351 elders who were registered at dementia day care centers in 11 regions of Metropolitan Incheon. Collected data were analyzed using SPSS Statistics 17.0 and SAS 9.1. Bootstrap method using the Clementine program 12.0 was applied to build an optimum prediction model. Results: Gender and education (general characteristics), alcohol, urinary/fecal incontinence, exercise, weight, and ADL (state of health), and depression (psychological state) were found to have an affect on cognitive impairment in these elders. Conclusion: Study results indicate nine key factors that affect cognitive impairment of elders with dementia who reside at home and that could be useful in prevention and management nursing plans. These factors could also be used to expand the role of nurses who are working in community day care centers, and can be applied in the development and provision of various programs to aid retention and improve cognitive function as well as preventing deterioration of cognition.

• Advances in concrete construction
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• 제1권3호
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• pp.201-213
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• 2013

While recognizing the problem of reinforcement corrosion and premature structural deterioration of reinforced concrete (RC) structures as a combined effect of mechanical and environmental actions (carbonation, ingress of chlorides), emphasis is given on the effect of the latter, as most severe and unpredictable action. In this study, a simulation tool, based on proven predictive models utilizing principles of chemical and material engineering, for the estimation of concrete service life is applied on an existing reinforced concrete bridge (${\O}$resund Link) located in a chloride environment. After a brief introduction to the structure of the models used, emphasis is given on the physicochemical processes in concrete leading to chloride induced corrosion of the embedded reinforcement. By taking under consideration the concrete, structural and environmental properties of the bridge investigated, an accurate prediction of its service life is taking place. It was observed that the proposed, and already used, relationship of service lifetime- cover is almost identical with a mean line between the lines derived from the minimum and maximum critical values considered for corrosion initiation. Thus, an excellent agreement with the project specifications is observed despite the different ways used to approach the problem. Furthermore, different scenarios of concrete cover failure, in the case when a coating is utilized, and extreme deicing salts attack are also investigated.

• 대한토목학회논문집
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• 제44권1호
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• pp.19-32
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• 2024

정밀안전진단보고서를 분석한 결과 국내 고속도로 교량은 결함, 열화, 물리력에 의한 손상이 주요하게 발생한다. 특히 열화는 시간이 경과함에 따라 다양한 환경 영향인자와 외부적 요인에 의해 발생하는 필연적인 손상이다. 교량 바닥판의 경우 열화가 가장 빠른 부재로, 균열부를 중심으로 철근부식, 박리/박락 등의 여러 가지 유형의 이종손상이 함께 발생하는 것으로 분석된다. 따라서 교량의 이종손상과 열화환경 간의 상관관계를 밝히고 이를 통해 교량의 이종손상 발생 원인을 규명해야 한다. 본 연구에서는 랜덤포레스트를 활용하여 교량의 이종손상 예측 모델을 개발하였으며, 개발된 모델을 통해 이종손상 발생에 영향을 미치는 상위 5가지 영향인자를 도출하였다. 이를 통해 장래 교량의 유지관리 및 예산을 추정하는 분야에 활용하는 기초자료가 될 수 있을 것으로 판단된다.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2010년도 춘계 학술논문 발표대회 1부
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• pp.229-230
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• 2010

The Carbonation, one of the main deterioration factors of concrete, reduces capacity of members with providing rebar corrosion environment. Consequently it suggested standards of all countries of world, carbonation depth prediction equation of respective researchers and time to rebar corrosion initiation. As a result of carbonation depth prediction equation calculation, difference of time to rebar corrosion initiation is 149 years and difference of carbonation depth prediction equation is 162 years when water cement ratio is 50%. So a study on rebar corrosion with carbonation depth will need existing reliable data and verifications by experiment.

• Computers and Concrete
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• 제4권1호
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• pp.1-18
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• 2007

Over the past decades, an enormous amount of effort has been expended in laboratory and field studies on concrete durability estimation. The results of this research are still either widely scattered in the journal literature or mentioned briefly in the standard textbooks. Moreover, the theoretical approaches of deterioration mechanisms with a predictive character are limited to some complicated mathematical models not widespread in practice. A significant step forward could be the development of appropriate software for computer-based estimation of concrete service life, including reliable mathematical models and adequate experimental data. In the present work, the basis for the development of a computer estimation of the concrete service life is presented. After the definition of concrete mix design and structure characteristics, as well as the consideration regarding the environmental conditions where the structure will be found, the concrete service life can be reliably predicted using fundamental mathematical models that simulate the deterioration mechanisms. The prediction is focused on the basic deterioration phenomena of reinforced concrete, such as carbonation and chloride penetration, that initiate the reinforcing bars corrosion. Aspects on concrete strength and the production cost are also considered. Field observations and data collection from existing structures are compared with predictions of service life using the above model. A first attempt to develop a database of service lives of different types of reinforced concrete structure exposed to varying environments is finally included.

• Computers and Concrete
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• 제2권4호
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• pp.325-343
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• 2005

Chloride ingress is a common cause of deterioration of reinforced concrete located in coastal zone. Modeling the chloride ingress is an important basis for designing reinforced concrete structures and for assessing the reliability of an existing structure. The modeling is also needed for predicting the deterioration of a reinforced structure. The existing deterministic solution for prediction model of corrosion initiation cannot reflect uncertainties which input variables have. This paper presents an approach to the fuzzy arithmetic based modeling of the chloride-induced corrosion of reinforcement in concrete structures that takes into account the uncertainties in the physical models of chloride penetration into concrete and corrosion of steel reinforcement, as well as the uncertainties in the governing parameters, including concrete diffusivity, concrete cover depth, surface chloride concentration and critical chloride level for corrosion initiation. There are a lot of prediction model for predicting the time of reinforcement corrosion of structures exposed to chloride-induced corrosion environment. In this work, RILEM model formula and Crank's solution of Fick's second law of diffusion is used. The parameters of the models are regarded as fuzzy numbers with proper membership function adapted to statistical data of the governing parameters instead of random variables of probabilistic modeling of Monte Carlo Simulation and the fuzziness of the time to corrosion initiation is determined by the fuzzy arithmetic of interval arithmetic and extension principle. An analysis is implemented by comparing deterministic calculation with fuzzy arithmetic for above two prediction models.

• 환경영향평가
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• 제20권5호
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• pp.705-716
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• 2011

For the efficient discovery of knowledge and information from the observed systems, data mining techniques can be an useful tool for the prediction of water quality at intake station in rivers. Deterioration of water quality can be caused at intake station in dry season due to insufficient flow. This demands additional outflow from dam since some extent of deterioration can be attenuated by dam reservoir operation to control outflow considering predicted water quality. A seasonal occurrence of high ammonia nitrogen ($NH_3$-N) concentrations has hampered chemical treatment processes of a water plant in Geum river. Monthly flow allocation from upstream dam is important for downstream $NH_3$-N control. In this study, prediction models of water quality based on multiple regression (MR), artificial neural network and data mining methods were developed to understand water quality variation and to support dam operations through providing predicted $NH_3$-N concentrations at intake station. The models were calibrated with eight years of monthly data and verified with another two years of independent data. In those models, the $NH_3$-N concentration for next time step is dependent on dam outflow, river water quality such as alkalinity, temperature, and $NH_3$-N of previous time step. The model performances are compared and evaluated by error analysis and statistical characteristics like correlation and determination coefficients between the observed and the predicted water quality. It is expected that these data mining techniques can present more efficient data-driven tools in modelling stage and it is found that those models can be applied well to predict water quality in stream river systems.