The technology of high strength concrete has improved over the last decade. High strength concrete (HSC) is more brittle than normal strength concrete. The brittleness increases with the use of over-reinforced section, which fails suddenly without warning. Use of over reinforced sections is restricted in codes of practice of concrete design. This paper presents an experimental study of the behaviour of five HSC beams confined with helical reinforcement. Concrete compressive strength in the range 72–95 MPa and tensile reinforcement ratio in the range 5.24–7.86% were used. The main results indicate that as the concrete compressive strength increases the displacement ductility index decreases and the load at spalling-off the concrete cover increases. Also, the displacement ductility index increases as the longitudinal reinforcement ratio increases and the load at spalling-off the concrete cover decreases. 相似文献
Ground vibration is the most detrimental effect induced by blasting in surface mines. This study presents an improved bagged support vector regression (BSVR) combined with the firefly algorithm (FA) to predict ground vibration. In other words, the FA was used to modify the weights of the SVR model. To verify the validity of the BSVR–FA, the back-propagation neural network (BPNN) and radial basis function network (RBFN) were also applied. The BSVR–FA, BPNN and RBFN models were constructed using a comprehensive database collected from Shur River dam region, in Iran. The proposed models were then evaluated by means of several statistical indicators such as root mean square error (RMSE) and symmetric mean absolute percentage error. Comparing the results, the BSVR–FA model was found to be the most accurate to predict ground vibration in comparison to the BPNN and RBFN models. This study indicates the successful application of the BSVR–FA model as a suitable and effective tool for the prediction of ground vibration.
Piles are widely applied to substructures of various infrastructural buildings. Soil has a complex nature; thus, a variety of empirical models have been proposed for the prediction of the bearing capacity of piles. The aim of this study is to propose a novel artificial intelligent approach to predict vertical load capacity of driven piles in cohesionless soils using support vector regression (SVR) optimized by genetic algorithm (GA). To the best of our knowledge, no research has been developed the GA-SVR model to predict vertical load capacity of driven piles in different timescales as of yet, and the novelty of this study is to develop a new hybrid intelligent approach in this field. To investigate the efficacy of GA-SVR model, two other models, i.e., SVR and linear regression models, are also used for a comparative study. According to the obtained results, GA-SVR model clearly outperformed the SVR and linear regression models by achieving less root mean square error (RMSE) and higher coefficient of determination (R2). In other words, GA-SVR with RMSE of 0.017 and R2 of 0.980 has higher performance than SVR with RMSE of 0.035 and R2 of 0.912, and linear regression model with RMSE of 0.079 and R2 of 0.625.
International Journal of Computer Vision - Nowadays, how to effectively evaluate visual properties has become a popular topic for fine-grained visual comprehension. In this paper we study the... 相似文献