http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Elastic modulus of ASR-affected concrete: An evaluation using Artificial Neural Network
Thuc Nhu Nguyen,Yang Yu,Jianchun Li,Nadarajah Gowripalan,Vute Sirivivatnanon 사단법인 한국계산역학회 2019 Computers and Concrete, An International Journal Vol.24 No.6
Alkali-silica reaction (ASR) in concrete can induce degradation in its mechanical properties, leading to compromised serviceability and even loss in load capacity of concrete structures. Compared to other properties, ASR often affects the modulus of elasticity more significantly. Several empirical models have thus been established to estimate elastic modulus reduction based on the ASR expansion only for condition assessment and capacity evaluation of the distressed structures. However, it has been observed from experimental studies in the literature that for any given level of ASR expansion, there are significant variations on the measured modulus of elasticity. In fact, many other factors, such as cement content, reactive aggregate type, exposure condition, additional alkali and concrete strength, have been commonly known in contribution to changes of concrete elastic modulus due to ASR. In this study, an artificial intelligent model using artificial neural network (ANN) is proposed for the first time to provide an innovative approach for evaluation of the elastic modulus of ASR-affected concrete, which is able to take into account contribution of several influence factors. By intelligently fusing multiple information, the proposed ANN model can provide an accurate estimation of the modulus of elasticity, which shows a significant improvement from empirical based models used in current practice. The results also indicate that expansion due to ASR is not the only factor contributing to the stiffness change, and various factors have to be included during the evaluation.
The Correlation between Ammonia Emissions and Bedding Materials in a Cow House
Phan, Nhu-Thuc,Sa, Jae-Hwan,Jeon, Eui-Chan,Lee, Sang-Rak Korean Society for Atmospheric Environment 2010 Asian Journal of Atmospheric Environment (AJAE) Vol.4 No.1
Because ammonia from livestock production may substantially contribute to environmental pollution, emissions from all possible sources (housing systems, manure storage, manure application, outside grazing) should be reduced. The purpose of this study was to investigate the effect of different bedding materials on ammonia emissions in a cow house. By applying a combination of four treatment types: treatment $1-T_1$ (sawdust (50%)+sawdust pellets (50%)), treatment $2-T_2$ (sawdust (50%)+corn stalk pellets (50%)), treatment $3-T_3$ (sawdust (100%)), and treatment $4-T_4$ (sawdust (50%)+palm kernel meal pellets(50%)) as bedding materials in a cow house, the effects of such treatments on ammonia flux were assessed in approximately one month. The magnitude of ammonia emissions (mg $m^{-2}\;min^{-1}$) varied in the following order: $T_1$(2.226), $T_4$(2.052), $T_2$(1.845), and $T_3$(1.712). The patterns of pH had a decreasing trend for all bedding treatments during the experiment, and there was no significant relationship with ammonia flux. The results reveal that the most important factor influencing ammonia emissions is the physical structure of the bedding types.