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首页> 外文期刊>Structural Engineering and Mechanics >Neuro-fuzzy optimisation to model the phenomenon of failure by punching of a slab-column connection without shear reinforcement
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Neuro-fuzzy optimisation to model the phenomenon of failure by punching of a slab-column connection without shear reinforcement

机译：神经模糊优化，通过对不带剪切补强的板-柱连接进行冲孔来对失效现象进行建模

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摘要

Two new predictive design methods are presented in this study. The first is a hybrid method, called neuro-fuzzy, based on neural networks with fuzzy learning. A total of 280 experimental datasets obtained from the literature concerning concentric punching shear tests of reinforced concrete slab-column connections without shear reinforcement were used to test the model (194 for experimentation and 86 for validation) and were endorsed by statistical validation criteria. The punching shear strength predicted by the neuro-fuzzy model was compared with those predicted by current models of punching shear, widely used in the design practice, such as ACI 318-08, SIA262 and CBA93. The neuro-fuzzy model showed high predictive accuracy of resistance to punching according to all of the relevant codes. A second, more user-friendly design method is presented based on a predictive linear regression model that supports all the geometric and material parameters involved in predicting punching shear. Despite its simplicity, this formulation showed accuracy equivalent to that of the neuro-fuzzy model.
机译：这项研究提出了两种新的预测设计方法。第一种是基于神经网络的模糊学习的混合方法，称为神经模糊。从文献中获得的关于不带抗剪钢筋的钢筋混凝土平板-柱连接的同心冲剪试验的总共280个实验数据集用于测试模型（实验194个，验证86个），并得到统计验证标准的认可。将神经模糊模型预测的冲压剪切强度与设计实践中广泛使用的当前冲压剪切模型预测的强度进行了比较，例如ACI 318-08，SIA262和CBA93。根据所有相关规范，神经模糊模型显示出较高的抗冲压性预测精度。根据预测线性回归模型，提出了第二种更人性化的设计方法，该模型支持预测冲压剪切所涉及的所有几何和材料参数。尽管其简单性，但此公式显示的准确性与神经模糊模型的准确性相当。

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