http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
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.