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낙엽송 집성재 Glued-in-rod 접합부의 인장성능
방성준 ( Sung-jun Pang ),안경선 ( Kyung-sun Ahn ),김지용 ( Ji Yong Kim ),이한식 ( Han Shik Lee ),오정권 ( Jung-kwon Oh ) 한국목재공학회 2021 한국목재공학회 학술발표논문집 Vol.2021 No.1
In this study, the tensile stiffness and strength of glued-in-rod connections were investigated. A hole (ø20×300mm) was drilled on the cross-section of larch glulam (moisture content: 10%). A steel bar (ø16×500mm) was inserted 300mm into the hole of the glulam. The gap between the hole of glulam and the steel bar was filled with epoxy. Six specimens were prepared and the tensile test was evaluated with UTM (100kN, Zwick) by KS F ISO 9087. The test speed was 3mm/min and a Linear Variable Differential Transformer (LVDT) was installed between the glulam and steel bar. Five of the six specimens were not failed at the maximum capacity of the UTM, and one specimen (No. 3) was destroyed at 89.3kN. When the steel bar was pulled out, the load decreased sharply, and the epoxy and wood were attached around the steel bar. The tensile stiffness was a range of 41.7 kN/mm ~ 54.1 kN/mm, and the average value was 45.8 kN/mm. The tensile strength was 0.3 kN/mm (91.5kN/300mm).
이한식 서강대학교 경제연구소 2000 시장경제연구 Vol.29 No.2
While many researchers have presented evidence that imposing correct number of cointegrating relations improves forecasts, we show in this paper that the forecasting performance can further be enhanced by applying nonlinear error correction models. That is, we can improve our forecasts by using more information on the strength of an attraction, which can be different depending on the direction as well as the size of equilibrium errors. In doing so, we first propose simple estimation and testing procedures to measure the strength of an attraction. These procedures are then applied to a few economic data sets, and evidence is presented for the nonlinearity and asymmetry of the adjustment process. Based on simulation experiments as well as real data sets, forecasting performances of the alternative econometric models are compared, and it is found that nonlinear specifications of the error-correcting mechanism outperform the usual linear error correction models.