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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 ),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.
Kim, Dookie,Chaudhary, Sandeep,Nocete, Charito Fe,Wang, Feng,Lee, Do Hyung s.n.] 2011 Latin american journal of solids and structures Vol.8 No.3
<P> This paper presents a probabilistic capacity spectrum strategy for the reliability analysis of a bridge pile shaft, accounting for uncertainties in design factors in the analysis and the soil-structure interaction (SSI). Monte Carlo simulation method (MCS) is adopted to determine the probabilities of failure by comparing the responses with defined limit states. The analysis considers the soil structure interaction together with the probabilistic application of the capacity spectrum method for different types of limit states. A cast-in-drilledhole (CIDH) extended reinforced concrete pile shaft of a bridge is analysed using the proposed strategy. The results of the analysis show that the SSI can lead to increase or decrease of the structure’s probability of failure depending on the definition of the limit states. </P>
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(HyeongYeol Seo) 한국구조물진단유지관리학회 2004 한국구조물진단학회 학술발표회논문집 Vol.- No.-
The seismic analysis results of the seismic coefficient method, added mass method and fluid-structure-soil interaction(FSSI) method are compared for a part 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 tire 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.