The tensile behavior of hybrid fiber reinforced concrete (HFRC) is important to the design of HFRC and HFRC structure. This study used an artificial neural network (ANN) model to describe the tensile behavior of HFRC. This ANN model can describe well the tensile stress-strain curve of HFRC with the consideration of 23 features of HFRC. In the model, three methods to process output features (no-processed, mid-processed, and processed) are discussed and the mid-processed method is recommended to achieve a better reproduction of the experimental data. This means the strain should be normalized while the stress doesn’t need normalization. To prepare the database of the model, both many direct tensile test results and the relevant literature data are collected. Moreover, a traditional equation-based model is also established and compared with the ANN model. The results show that the ANN model has a better prediction than the equation-based model in terms of the tensile stress-strain curve, tensile strength, and strain corresponding to tensile strength of HFRC. Finally, the sensitivity analysis of the ANN model is also performed to analyze the contribution of each input feature to the tensile strength and strain corresponding to tensile strength. The mechanical properties of plain concrete make the main contribution to the tensile strength and strain corresponding to tensile strength, while steel fibers tend to make more contributions to these two items than PVA fibers.

混合纤维混凝土的拉伸性能对于HFRC和HFRC结构的设计很重要。本研究使用人工神经网络（ANN）模型来描述HFRC的拉伸行为。考虑到HFRC的23个特征，该ANN模型可以很好地描述HFRC的拉伸应力-应变曲线。在模型中，讨论了三种处理输出特征的方法（未处理，中间处理和已处理），建议使用中间处理方法以更好地再现实验数据。这意味着应将应变归一化，而应力不需要归一化。为了准备模型的数据库，同时收集了许多直接的拉伸试验结果和相关的文献数据。此外，还建立了传统的基于方程的模型并将其与ANN模型进行比较。结果表明，在HFRC的拉伸应力-应变曲线，拉伸强度和应变方面，ANN模型比基于方程的模型具有更好的预测。最后，还进行了ANN模型的灵敏度分析，以分析每个输入特征对拉伸强度和对应于拉伸强度的应变的贡献。普通混凝土的机械性能对抗拉强度和与抗拉强度相对应的应变起主要作用，而钢纤维比PVA纤维对这两项的贡献更大。拉伸强度和对应于HFRC拉伸强度的应变。最后，还进行了ANN模型的灵敏度分析，以分析每个输入特征对拉伸强度和对应于拉伸强度的应变的贡献。普通混凝土的机械性能对抗拉强度和与抗拉强度相对应的应变起主要作用，而钢纤维比PVA纤维对这两项的贡献更大。拉伸强度和对应于HFRC拉伸强度的应变。最后，还进行了ANN模型的灵敏度分析，以分析每个输入特征对拉伸强度和对应于拉伸强度的应变的贡献。普通混凝土的机械性能对抗拉强度和与抗拉强度相对应的应变起主要作用，而钢纤维比PVA纤维对这两项的贡献更大。