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This paper presents the results of an experimental investigation into the axial load performance of steel-reinforced high-strength concrete short columns. The performance of a total of ten specimens is investigated, including the failure pattern, load-strain response, compressive strength and ductility. The parameters considered in this study are stirrup and structural steel arrangements. The definition of effective strength is developed to measure the compressive strength of columns due to the sudden drop of strength at peak. The ductility of columns is characterised by the ratio of after-peak and before-peak area under the curve of the axial load plotted against axial strain. Based on the experimental research, it is found that stirrups affect ductility and residual strength of columns, but not the peak strength. Structural steel influences the peak and residual strength and ductility of columns. Ductility of columns is approximately proportional to the effective confinement index if stirrups yield after peak. The effect of structural steel on ductility is more obvious when the effective confinement index is larger.

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Near-infrared (NIR) spectroscopy has been widely used in agricultural operations to obtain various crop parameters, such as water content, sugar content, and different indicators of ripeness, as well as other potential information concerning crops that cannot be directly obtained by human observation. The chemical compositions of tobacco play an important role in the quality of cigarettes. The NIR spectroscopy-based chemical composition analysis has recently become one of the most effective methods in tobacco quality analysis. Existing NIR spectroscopy-related solutions either have relatively low analysis accuracy, or are only able to analyze one or two chemical components. Thus, a precise prediction model is needed to improve the analysis accuracy of NIR data. This paper proposes a tobacco chemical component analysis method based on a neural network (TCCANN) to quantitatively analyze the chemical components of tobacco leaves by using NIR spectroscopy, including nicotine, total sugar, reducing sugar, total nitrogen, potassium, chlorine, and pH value. The proposed TCCANN consists of both residual network (ResNet) and long short-term memory (LSTM) neural network. ResNet is applied to the feature extraction of high-dimension NIR spectroscopy, which can effectively avoid the gradient-disappearance issue caused by the increase of network depth. LSTM is used to quantitatively analyze the multiple chemical compositions of tobacco leaves in a simultaneous manner. LSTM selectively allows information to pass through by a gated unit, thereby comprehensively analyzing the correlation between multiple chemical components and corresponding spectroscopy. The experimental results confirm that the proposed TCCANN not only predicts the corresponding values of seven chemical components simultaneously, but also achieves better prediction performance than other existing machine learning methods.