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Dookie Kim
Researcher at Kongju National University
Publications - 201
Citations - 2289
Dookie Kim is an academic researcher from Kongju National University. The author has contributed to research in topics: Probabilistic neural network & Finite element method. The author has an hindex of 20, co-authored 192 publications receiving 1704 citations. Previous affiliations of Dookie Kim include KAIST & University of California, Irvine.
Papers
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Application of Neural Networks for Estimation of Concrete Strength
TL;DR: In this paper, back-propagation neural networks were used to predict the compressive strength of concrete based on concrete mix proportions using data from two ready-mixed concrete companies.
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Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models
TL;DR: It is advocated that the RVM model can be employed as a promising machine learning tool for the prediction of evaporative loss.
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Baseline Models for Bridge Performance Monitoring
TL;DR: In this paper, a neural network-based system identification technique was applied to establish and update a baseline finite element model of an instrumented highway bridge based on the measurement of its traffic-induced vibrations.
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Application of Probabilistic Neural Networks for Prediction of Concrete Strength
Dookie Kim,Dookie Kim,Jong-Jae Lee,Jong-Jae Lee,Jong Han Lee,Jong Han Lee,Seongkyu Chang,Seongkyu Chang +7 more
TL;DR: This study presents the probabilistic technique for predicting the compressive strength of concrete on the basis of concrete mix proportions and it has been found that the present methods are very efficient and reasonable in predicting theCompressive Strength of concrete probabilistically.
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Modelling the energy performance of residential buildings using advanced computational frameworks based on RVM, GMDH, ANFIS-BBO and ANFIS-IPSO
TL;DR: Four advanced computational frameworks including relevance vector machine (RVM), group method of data handling (GMDH), hybridization of adaptive neuro-fuzzy interface system (ANFIS) and biogeography-based optimisation (BBO) are proposed as novel approaches to predict the heating load (HL) and cooling load (CL) of residential buildings.