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김두기(Dookie Kim),이종재(Jong-Jae Lee),장성규(SeongKyu Chang),임병용(Byung-Yong Lim) 한국구조물진단유지관리학회 2004 한국구조물진단학회 학술발표회논문집 Vol.- No.-
The compressive strength of concrete is commonly used criterion in producing concrete However, the tests on the compressive strength are complicated and time-consuming. More importantly, It IS too late to make improvement even If the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of concrete at the construction site Therefore, accurate and realistic strength estimation before the placement of concrete is being highly required. In this study, the estimation of the compressive strength of concrete was performed by probabilistic neural network (PNN) on the basis of concrete mix proportions. The estimation performance of PNN was improved by considering the correlation between input data and targeted output value Adaptive probabilistic neural network (APNN) was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment algorithm The conventional PNN and APNN were applied to predict the compressive strength of concrete using actual test data of a concrete company. APNN showed better results than the conventional PNN in predicting the compressive strength of concrete
김두기 ( Dookie Kim ),이종재 ( Jong-jae Lee ),장성규 ( Seongkyu Chang ),임병용 ( Byung-yong Lim ) 한국구조물진단유지관리공학회 2004 한국구조물진단유지관리공학회 학술발표대회 논문집 Vol.8 No.2
The compressive strength of concrete is commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time-consuming. More importantly, it is too late to make improvement even if the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is being highly required. In this study, the estimation of the compressive strength of concrete was performed by probabilistic neural network (PNN) on the basis of concrete mix proportions. The estimation performance of PNN was improved by considering the correlation between input data and targeted output value. Adaptive probabilistic neural network (APNN) was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment algorithm. The conventional PNN and APNN were applied to predict the compressive strength of concrete using actual test data of a concrete company. APNN showed better results than the conventional PNN in predicting the compressive strength of concrete.
김두기 ( Dookie Kim ),류희룡 ( Heeryong Ryu ),장성규 ( Seongkyu Chang ),서형렬 ( Hyeongyeoi Seo ) 한국구조물진단유지관리공학회 2004 한국구조물진단유지관리공학회 학술발표대회 논문집 Vol.8 No.2
The seismic analysis results of the seismic coefficient method, added mass method and fluid-structure-soil interaction(FSSI) method are compared for a port structure. In the FSSI analysis, the fluid is modeled by the 4-node element which is a modification of a structural plane element, and the port structure and foundation is modelled by the plane strain elements. Since the present method directly models the fluid-structure-soil interaction system by finite elements, it can be easily applied to the dynamic analysis of a 2-D fluid-port-soil with complex geometry.
김두기 ( Kim Dookie ),김시범 ( Kim Sibeum ) 한국구조물진단유지관리공학회 2019 한국구조물진단유지관리공학회 학술발표대회 논문집 Vol.23 No.2
내진보강사업 대가산정 가이드라인(행정안전부, 2019)의 일반사항, 대가산정 방법 및 액상화 평가에 관해 설명하고, 건축시설물, 토목시설물 및 산업시설물 각각에 관한 일반사항, 내진성능평가 대가산정, 내진보강설계 대가산정, 내진보강공사 대가산정에 대해 소개하였다.