School of Civil Engineering, Shenyang Jianzhu University, Shenyang, China.
PLoS One. 2024 May 13;19(5):e0303327. doi: 10.1371/journal.pone.0303327. eCollection 2024.
This study applied the pull-out test to examine the influence of freeze-thaw cycles and hybrid fiber incorporation on the bond performance between BFRP bars and hybrid fiber-reinforced concrete. The bond-slip curves were fitted by the existing bond-slip constitutive model, and then the bond strength was predicted by a BP neural network. The results indicated that the failure mode changed from pull-out to splitting for the BFRP bar ordinary concrete specimens when the freeze-thaw cycles exceeded 50, while only pull-out failure occurred for all BFRP bar hybrid fiber-reinforced concrete specimens. An increasing trend was shown on the peak slip, but a decreasing trend was shown on the bond stiffness and bond strength when freeze-thaw cycles increased. The bond strength could be increased significantly by the incorporation of basalt fiber (BF) and cellulose fiber (CF) under the same freezing and thawing conditions as compared to concrete specimens without fibers. The Malvar model and the Continuous Curve model performed best in fitting the ascending and descending sections of the bond-slip curves, respectively. The BP neural network also accurately predicted the bond strength, with relative errors of predicted bond strengths ranging from 3.75% to 13.7%, and 86% of them being less than 10%.
本研究采用拔出试验研究了冻融循环和混杂纤维掺入对 BFRP 筋与混杂纤维增强混凝土之间粘结性能的影响。通过现有的粘结滑移本构模型对粘结滑移曲线进行拟合，然后通过 BP 神经网络预测粘结强度。结果表明，当冻融循环超过 50 次时，BFRP 普通混凝土试件的破坏模式由拔出转为劈裂，而所有 BFRP 混杂纤维增强混凝土试件均发生拔出破坏。随着冻融循环次数的增加，峰值滑移呈上升趋势，粘结刚度和粘结强度呈下降趋势。与无纤维混凝土试件相比，在相同的冻融条件下，掺入玄武岩纤维（BF）和纤维素纤维（CF）可显著提高粘结强度。Malvar 模型和连续曲线模型分别在拟合粘结滑移曲线的上升段和下降段方面表现最佳。BP 神经网络也能准确地预测粘结强度，预测粘结强度的相对误差在 3.75%至 13.7%之间，其中 86%的误差小于 10%。
