Du Changqing, Liu Xiaofan, Liu Yinying, Tong Teng
State Gird Jiangsu Electric Power Engineering Consulting Co., Ltd., Nanjing 210003, China.
School of Civil Engineering, Southeast University, Nanjing 210018, China.
Materials (Basel). 2021 Sep 30;14(19):5707. doi: 10.3390/ma14195707.
The bond strength between ultra-high-performance concrete (UHPC) and normal-strength concrete (NC) plays an important role in governing the composite specimens' overall behaviors. Unfortunately, there are still no widely accepted formulas targeting UHPC-NC interfacial strength, either in their specifications or in research papers. To this end, this study constructs an experimental database, consisting of 563 and 338 specimens for splitting and slant shear tests, respectively. Moreover, an additional 35 specimens for "improved" slant shear tests were performed, which could circumvent concrete crushing and trigger interfacial debonding. Additionally, for the first time in our tests, the effect of casting sequence on UHPC-NC bond strength was identified. Based on the database, an artificial neural network (ANN) model is proposed with the following inputs: namely, the normal stress perpendicular to the interface, the interface roughness, and the compressive strengths of the UHPC and NC materials. Based on the ANN analyses, the explicit expression of UHPC-NC bond strength is proposed, which significantly lowers the prediction error. To be fully compatible with the specifications, the conventional shear-friction formula is modified. By splitting the total force into adhesion and friction forces, the modified formula additionally takes the casting sequence into account. Although sacrificing accuracy to some extent compared to the ANN model, the modified formula relies on a solid physical basis and its accuracy is enhanced significantly compared to the existing formulas in specifications or research papers.
超高性能混凝土（UHPC）与普通强度混凝土（NC）之间的粘结强度在控制复合试件的整体性能方面起着重要作用。遗憾的是，无论是在规范中还是在研究论文中，仍没有被广泛接受的针对UHPC-NC界面强度的公式。为此，本研究构建了一个试验数据库，分别包含563个和338个用于劈裂和斜剪试验的试件。此外，还进行了另外35个用于“改进”斜剪试验的试件，其可以避免混凝土破碎并引发界面脱粘。此外，在我们的试验中首次确定了浇筑顺序对UHPC-NC粘结强度的影响。基于该数据库，提出了一个人工神经网络（ANN）模型，其输入如下：即垂直于界面的正应力、界面粗糙度以及UHPC和NC材料的抗压强度。基于人工神经网络分析，提出了UHPC-NC粘结强度的显式表达式，显著降低了预测误差。为了与规范完全兼容，对传统的剪摩擦公式进行了修改。通过将总力分解为粘聚力和摩擦力，修改后的公式还考虑了浇筑顺序。虽然与人工神经网络模型相比在一定程度上牺牲了精度，但修改后的公式基于坚实的物理基础，并且与规范或研究论文中的现有公式相比其精度有显著提高。
