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新型负泊松比钢筋埋入超高性能混凝土中的粘结性能及临界锚固长度预测

Bond Behavior and Critical Anchorage Length Prediction of Novel Negative Poisson's Ratio Bars Embedded in Ultra-High-Performance Concrete.

作者信息

Xu Zhao, Xu Chang-Ze, Rong Xian-Liang, Wang Jun-Yan, Ma Xue-Yuan

机构信息

College of Civil Engineering, Tongji University, Shanghai 200092, China.

Shandong Provincial Communications Planning and Design Institute Group Co., Ltd., Jinan 250101, China.

出版信息

Materials (Basel). 2025 Jul 4;18(13):3182. doi: 10.3390/ma18133182.

DOI:10.3390/ma18133182
PMID:40649670
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12250885/
Abstract

Negative Poisson's ratio (NPR) reinforcement offers a novel solution to the usual trade-off between strength gains and ductility loss. Incorporating NPR into ultra-high-performance concrete (UHPC) effectively overcomes the ductility limitations of structural elements. However, the interfacial bonding between NPR reinforcement and UHPC is not sufficiently studied, especially its patterns and mechanisms, impeding the application of the materials. In this paper, the effects of nine design parameters (rebar type, prestrain, etc.) on the bond performance of NPR-UHPC through eccentric pull-out tests are investigated, and a quantitative discriminative indicator for NPR-UHPC bond failure modes is established. The results showed that when ≤ 4.3, 4.3 < ≤ 5.64, and ≥ 5.6, the NPR-UHPC specimens undergo splitting failure, splitting-pull-out failure, and pull-out failure, respectively. In terms of bonding with UHPC, the NPR bars outperform the HRB400 bars, and the HRB400 bars outperform the helical grooved (HG) bars. For the NPR bars, prestrain levels of 5.5%, 9.5%, and 22.0% decrease by 5.07%, 7.79%, and 17.01% and by 7.00%, 15.88%, and 30.54%, respectively. Bond performance deteriorated with increasing rib spacing and decreasing rib height. Based on the test results, an artificial neural network (ANN) model is developed to accurately predict the critical embedded length and ultimate embedded length between NPR bars and UHPC. Moreover, the MAPE of the ANN model is only 53.9% of that of the regression model, while the RMSE is just 62.0%.

摘要

负泊松比(NPR)增强材料为解决强度增加与延性损失之间常见的权衡问题提供了一种新的解决方案。将NPR纳入超高性能混凝土(UHPC)中可有效克服结构构件的延性限制。然而,NPR增强材料与UHPC之间的界面粘结尚未得到充分研究,尤其是其模式和机理,这阻碍了该材料的应用。本文通过偏心拉拔试验研究了九个设计参数(钢筋类型、预应变等)对NPR-UHPC粘结性能的影响,并建立了NPR-UHPC粘结破坏模式的定量判别指标。结果表明,当≤4.3、4.3<≤5.64和≥5.6时,NPR-UHPC试件分别发生劈裂破坏、劈裂-拉拔破坏和拉拔破坏。在与UHPC的粘结方面,NPR钢筋优于HRB400钢筋,而HRB400钢筋优于螺旋肋(HG)钢筋。对于NPR钢筋,5.5%、9.5%和22.0%的预应变水平分别使降低了5.07%、7.79%和17.01%,使降低了7.00%、15.88%和30.54%。粘结性能随肋间距增加和肋高减小而恶化。基于试验结果,建立了人工神经网络(ANN)模型,以准确预测NPR钢筋与UHPC之间的临界锚固长度和极限锚固长度。此外,ANN模型的平均绝对百分比误差(MAPE)仅为回归模型的53.9%,而均方根误差(RMSE)仅为62.0%。

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Enhancing Sustainability of Corroded RC Structures: Estimating Steel-to-Concrete Bond Strength with ANN and SVM Algorithms.提高锈蚀钢筋混凝土结构的可持续性:使用人工神经网络和支持向量机算法估算钢筋与混凝土的粘结强度。
Materials (Basel). 2022 Nov 22;15(23):8295. doi: 10.3390/ma15238295.
2
Application of artificial neural networks and multiple linear regression on local bond stress equation of UHPC and reinforcing steel bars.人工神经网络与多元线性回归在超高性能混凝土(UHPC)与钢筋局部粘结应力方程中的应用。
Sci Rep. 2021 Jul 23;11(1):15061. doi: 10.1038/s41598-021-94480-2.