可控粗糙度新老混凝土交界面氯离子传输性能研究

doi:10.16552/j.cnki.issn1001-1625.2025.0704

摘要/Abstract

摘要： 新老混凝土交界面粗糙度及氯离子传输性能是影响装配式混凝土结构交界面性能的关键因素。本文以分形维数为整体粗糙度指标,结合均方根高度、标准差、相关长度和Hurst系数4个分项指标,采用傅里叶变换生成二维高斯曲面,通过随机数矩阵赋值重复构造1 000次,利用线性回归和多种权重方法建立粗糙度综合评价模型,并结合数值模拟系统分析了不同粗糙度交界面对氯离子传输及钢筋腐蚀行为的影响。结果表明:当均方根高度由0.6增加至1.8时,钢筋所处交界面的氯离子浓度达到临界值0.4%(质量分数)的时间由107 d延长至120 d;标准差由0.6增加至1.8时,钢筋所处交界面的氯离子浓度达到临界值0.4%(质量分数)的时间由108 d延长至121 d,均显示粗糙度的提升显著减缓了氯离子渗透与腐蚀速率。总体上,曲面交界面相较于平面交界面表现出更强的抗氯离子侵蚀性能,新老混凝土交界面粗糙度越高,钢筋抗氯离子侵蚀性能越强,结构耐久性与安全性显著提高。研究成果为可控粗糙度交界面的设计、寿命预测模型修正及混凝土结构耐久性理论完善提供了重要依据。

关键词: 新老混凝土, 分形维数, 交界面粗糙度, 可控粗糙度, 氯离子传输性能, 钢筋锈蚀

Abstract: Interface roughness and chloride transport at the new-to-old concrete interface are key factors governing the interface performance of precast concrete structures. In this paper, the fractal dimension was used as the overall roughness index, combined with the four sub-indexes of root mean square height (RMS), standard deviation (S_td), correlation-length and Hurst coefficient. The two-dimensional Gaussian surface was generated by Fourier transform, and the random-number matrix assignment was used to repeatedly construct 1 000 times. The comprehensive evaluation model of roughness was established by linear regression and multiple weight methods, and the influences of different roughness interfaces on chloride ion transport and corrosion behavior of rebars were analyzed by numerical simulation system. The results show that when RMS increase from 0.6 to 1.8,the time for chloride concentration at the rebar to reach the critical value of 0.4% (mass fraction) increases from 107 d to 120 d; when S_td increase from 0.6 to 1.8, this time for chloride concentration at the rebar to reach the critical value of 0.4% (mass fraction) increases from 108 d to 121 d, which shows that the increase of roughness significantly slows down the chloride ion penetration and corrosion rate. Overall, curved interface exhibits significantly stronger resistance to chloride ion erosion than plane interface. The higher the interface roughness between new-to-old concrete, the stronger the resistance of rebars to chloride ion erosion, and the structural durability and safety are significantly improved. The research results provide an important basis for the design of controllable roughness interface, the correction of life prediction model and the improvement of concrete structure durability theory.

Key words: new-to-old concrete, fractal dimension, interface roughness, controlled roughness, chloride transport performance, rebar corrosion

中图分类号:

TU528.1

赵江航, 陈邦辉, 韩慧璇, 徐俊. 可控粗糙度新老混凝土交界面氯离子传输性能研究[J]. 硅酸盐通报, 2025, 44(11): 4000-4027.

ZHAO Jianghang, CHEN Banghui, HAN Huixuan, XU Jun. Chloride Transport Performance at New-to-Old Concrete Interfaces with Controlled Roughness[J]. BULLETIN OF THE CHINESE CERAMIC SOCIETY, 2025, 44(11): 4000-4027.

参考文献

[1] PAVESE A, BOURNAS D A. Experimental assessment of the seismic performance of a prefabricated concrete structural wall system[J]. Engineering Structures, 2011, 33(6): 2049-2062.
[2] QI X K, WANG Y W, SUN C S. Analysis of factors influencing the application of prefabricated concrete structure based on structure equation modeling[C]//ICCREM 2018. Charleston, South Carolina. American Society of Civil Engineers, 2018: 87-96.
[3] XU J, SU C Y, FU C Q. Cross-scale time-varying prediction model of oxygen diffusion in unsaturated concrete[J]. Construction and Building Materials, 2024, 457: 139374.
[4] LIN K Q, LU X Z, LI Y, et al. Experimental study of a novel multi-hazard resistant prefabricated concrete frame structure[J]. Soil Dynamics and Earthquake Engineering, 2019, 119: 390-407.
[5] TAMRAZYAN A G, KOROTEEV D D. Assessment of the durability of corrosion-damaged prefabricated reinforced concrete structures[J]. Journal of Physics: Conference Series, 2020, 1687(1): 012009.
[6] HE Y, ZHANG X, HOOTON R D, et al. Effects of interface roughness and interface adhesion on new-to-old concrete bonding[J]. Construction and Building Materials, 2017, 151: 582-590.
[7] WALL J S, SHRIVE N G. Factors affecting bond between new and old concrete[J]. ACI Materials Journal, 1988, 85(2): 117-125.
[8] XU J, WANG T, DU D G, et al. Damage model of NC-UHPC interface zone in UHPC wet joint based on DIC[J]. Journal of Building Engineering, 2024, 98: 111045.
[9] CATTANEO S, ZORZATO G, BONATI A. Assessing method of shear strength between old to new concrete interface under cycling loading[J]. Construction and Building Materials, 2021, 309: 125160.
[10] FAN J C, WU L L, ZHANG B. Influence of old concrete age, interface roughness and freeze-thawing attack on new-to-old concrete structure[J]. Materials, 2021, 14(5): 1057.
[11] MA J J, WANG T, LI G J, et al. Concrete surface roughness measurement method based on edge detection[J]. The Visual Computer, 2024, 40(3): 1553-1564.
[12] FENG S, XIAO H G, LI Y F. Influence of interfacial parameters and testing methods on UHPC-NSC bond strength: slant shear vs. direct tensile testing[J]. Cement and Concrete Composites, 2022, 131: 104568.
[13] WU X W, HE J W, TIAN J, et al. Shear behaviors of engineered cementitious composites to seawater sea-sand concrete (ECC-to-SSSC) interfaces cast using 3D-printed pre-grooving formwork: mechanical properties, characterization, and life-cycle assessment[J]. Journal of Building Engineering, 2023, 78: 107636.
[14] 李平先, 赵国藩, 张雷顺. 新老混凝土粘结面的抗冻融劈拉性能试验研究[J]. 土木工程学报, 2006, 39(4): 20-25.
LI P X, ZHAO G F, ZHANG L S. An experimental study on the bond splitting behavior of the interface between new-old cocretes under freeze-and-thaw cycles[J]. China Civil Engineering Journal, 2006, 39(4): 20-25 (in Chinese).
[15] CHEN Z X, LI F M, FANG Y Y, et al. Study on dependencies among evaluation indexes for concrete surface roughness[J]. Build Struct, 2021, 42(12): 193-199.
[16] DUBUC B, QUINIOU J F, ROQUES-CARMES C, et al. Evaluating the fractal dimension of profiles[J]. Physical Review A, General Physics, 1989, 39(3): 1500-1512.
[17] HAN X, WANG B M, FENG J J. Relationship between fractal feature and compressive strength of concrete based on MIP[J]. Construction and Building Materials, 2022, 322: 126504.
[18] ALDEERI A, ALHAMMAD L, ALDUHAM A, et al. Association of orthodontic clear aligners with root resorption using three-dimension measurements: a systematic review[J]. The Journal of Contemporary Dental Practice, 2018, 19(12): 1558-1564.
[19] XU J, YIN S C, GONG W, et al. Nonlinear chloride adsorption characteristics and microscopic mechanisms in concrete under coupling effects of static magnetic fields and saline environments[J]. Construction and Building Materials, 2025, 484: 141868.
[20] GUO F Z, ZHANG S Z, HU W L, et al. A numerical study of the droplet impact dynamics on a two-dimensional random rough surface[J]. Physics of Fluids, 2022, 34(12): 123607.
[21] LIN N, YIN J, ZHANG X, et al. Application of fractal theory to study interfacial bond strength of old-new concrete[J]. Build Mater, 2006, 9: 399-403.
[22] TANG Z P, HUANG F L, PENG H. Effect of 3D roughness characteristics on bonding behaviors between concrete substrate and asphalt overlay[J]. Construction and Building Materials, 2021, 270: 121386.
[23] XU J, WANG T, SU C Y, et al. Study of chloride transport pattern in the interface zone of UHPC wet joints under different influencing factors[J]. Journal of Building Engineering, 2024, 95: 110236.
[24] GAO M Z, ZHANG J G, LI S W, et al. Calculating changes in fractal dimension of surface cracks to quantify how the dynamic loading rate affects rock failure in deep mining[J]. Journal of Central South University, 2020, 27(10): 3013-3024.
[25] ZHANG S H, LI Q Y, YUAN Q, et al. Effect of roughness on bonding performance between Portland cement concrete and magnesium phosphate cement concrete[J]. Construction and Building Materials, 2022, 323: 126585.
[26] WU M Y, WANG W S, SHI D, et al. Improved box-counting methods to directly estimate the fractal dimension of a rough surface[J]. Measurement, 2021, 177: 109303.
[27] WOOD D A. Techniques used to calculate shale fractal dimensions involve uncertainties and imprecisions that require more careful consideration[J]. Advances in Geo-Energy Research, 2021, 5(2): 153-165.
[28] YANG L Y, XIE H Z, ZHANG D B, et al. Acoustic emission characteristics and crack resistance of basalt fiber reinforced concrete under tensile load[J]. Construction and Building Materials, 2021, 312: 125442.
[29] GARG A, AGGARWAL P, AGGARWAL Y, et al. Machine learning models for predicting the compressive strength of concrete containing nano silica[J]. Computers and Concrete, 2022, 30(1): 33-42.
[30] 赵峥, 丁飞, 李泽群, 等. 基于限定搜索高斯曲面拟合的星点质心提取算法[J]. 中国测试, 2023, 49(12): 54-59.
ZHAO Z, DING F, LI Z Q, et al. Star centroid extraction algorithm based on limited search Guassian surface fitting[J]. China Measurement & Test, 2023, 49(12): 54-59 (in Chinese).
[31] AHMAD M, HU J-L, AHMAD F, et al. Supervised learning methods for modeling concrete compressive strength prediction at high temperature[J]. Materials, 2021, 14(8): 1983.
[32] BURUD N B, CHANDRA KISHEN J M. Investigation of long memory in concrete fracture through acoustic emission time series analysis under monotonic and fatigue loading[J]. Engineering Fracture Mechanics, 2023, 277: 108975.
[33] WEI D H, HURLEY R C, POH L H, et al. The role of particle morphology on concrete fracture behaviour: a meso-scale modelling approach[J]. Cement and Concrete Research, 2020, 134: 106096.
[34] 李富民, 武晓辉, 陈志祥. 新老混凝土界面区氯离子传输特征与模型[J]. 东南大学学报(自然科学版), 2023, 53(3): 425-435.
LI F M, WU X H, CHEN Z X. Transport characteristics and models of chloride ions in the interfacial zone of new-old concrete[J]. Journal of Southeast University (Natural Science Edition), 2023, 53(3): 425-435 (in Chinese).
[35] REIZER R. Simulation of 3D Gaussian surface topography[J]. Wear, 2011, 271(3/4): 539-543.