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STRESS-STRAIN MODELING OF CONFINED CONCRETE USING ARTIFICIAL NEURAL NETWORKS

机译:基于人工神经网络的承压混凝土应力-应变模型

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A stress-strain model was proposed based on the artificial neural networks (ANN) to predict the behavior of confined concrete columns under concentric compression. A wide range of previous experimental data including 182 samples were collected for establishing ANN model. Gauge length in the compressive test was used in the input layer of ANN model to take into consideration the difference of compressive fracture energy. The proposed stress-strain model provides good agreement with the test results independent of the compressive strength of concrete, yield strength of tie, and gauge length.
机译:提出了一种基于人工神经网络的应力-应变模型,以预测混凝土在同心压缩下的受力性能。收集了包括182个样本在内的大量先前实验数据,用于建立ANN模型。在ANN模型的输入层中使用了压缩试验中的规长,以考虑压缩断裂能的差异。所提出的应力-应变模型与测试结果保持了良好的一致性,而与混凝土的抗压强度,扎带的屈服强度和标距无关。

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