混凝土表面孔洞的检测和量化是混凝土表观质量评价的重要内容之一,仅依据孔洞尺寸不足以全面评价混凝土的表观质量。考虑孔洞个数对混凝土表观质量评价的影响,基于模糊数学方法,以孔洞最大直径、孔洞面积比和孔洞个数为因素集,采用灰色关联度分析法计算因素集权重建立评价模型,综合评估混凝土表观质量,并根据评价结果探究不同比例消泡剂和引气剂对混凝土表观质量的影响规律。结果表明:① CIB分级与孔洞个数的相关系数为0.929 74,通过回归分析提出了孔洞个数分级指标,因素集对模筑混凝土表观质量的影响程度不同,对孔洞最大直径影响最大,其次为孔洞个数,最后是孔洞面积比,因此需将孔洞个数纳为影响模筑混凝土表观质量的重要因素;② 消泡剂掺量<0.3‰时,孔洞尺寸和数量随消泡剂掺量增加而减小,随引气剂掺量增加而增大,消泡剂掺量>0.5‰时,相应的值反而随消泡剂掺量增加而增大,随引气剂掺量增加而减小,合适的外加剂比例能有效提高模筑混凝土表观质量;③ 基于模糊数学的分级方法能实现混凝土表观质量评价,依据综合评价结果,当消泡剂和引气剂掺量分别为0.5‰和0.15‰时,混凝土表观质量最好。研究结果可为模筑混凝土表观质量的优化提供参考。
Abstract
Detecting and quantifying surface bugholes on concrete is a crucial aspect of evaluating the appearance quality of concrete. Relying solely on the size of bughole is insufficient to comprehensively assess the appearance quality of concrete. In this study we consider the influence of bughole quantity on the evaluation of concrete’s appearance quality. Based on fuzzy mathematical methods, we employed a grey relational analysis approach to establish an evaluation model with the maximum diameter, area ratio, and number of bugholes as the factor set. This model comprehensively evaluates the appearance quality of concrete and explores the impact of different proportions of defoamers and air-entraining agents on the appearance quality of concrete based on the evaluation results. Results demonstrate that 1) the coefficient of correlation between the CIB grade and the number of bugholes is 0.929 74. Through regression analysis, we propose a grading index for the number of bugholes. Factors in the factor set have different degrees of influence on the appearance quality of the formed concrete, with the maximum diameter of bugholes having the greatest impact, followed by the number of bugholes, and finally, the area ratio. Therefore, the number of bugholes should be considered as an important factor affecting the appearance quality of formed concrete. 2) When the dosage of defoamer is less than 0.3‰, the size and number of bugholes decrease with an increase in the defoamer dosage and increase with an increase in the air-entraining agent dosage. When the dosage of defoamer exceeds 0.5‰, the corresponding values increase with an increase in the defoamer dosage while decrease with an increase in the air-entraining agent dosage. An appropriate proportion of additives can effectively improve the appearance quality of the formed concrete. 3) The grading method based on fuzzy mathematics can accomplish the evaluation of concrete’s appearance quality. According to the comprehensive evaluation results, the best appearance quality of concrete is achieved when the dosages of defoamer and air-entraining agent are 0.5‰ and 0.15‰, respectively.
关键词
混凝土 /
表观质量 /
回归分析 /
模糊数学 /
消泡剂 /
引气剂 /
模糊综合评价
Key words
concrete /
appearance quality /
regression analysis /
fuzzy mathematics /
defoamer /
air-entraining agent /
fuzzy comprehensive evaluation
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] ZHU Z H, BRILAKIS I. Defects Detection and Assessment of Concrete Surfaces[C]∥Proceedings of the Joint US-European Workshop on Intelligent Computing in Engineering. Plymouth, UK. July 2-4, 2008: 441-450.
[2] DA SILVA W R L,TEMBERK P.Expert System Applied for Classifying Self-compacting Concrete Surface Finish[J]. Advances in Engineering Software,2013,64(9):47-61.
[3] LIU B, YANG T. Image Analysis for Detection of Bugholes on Concrete Surface[J]. Construction and Building Materials, 2017, 137: 432-440.
[4] YOSHITAKE I, MAEDA T, HIEDA M. Image Analysis for the Detection and Quantification of Concrete Bugholes in a Tunnel Lining[J]. Case Studies in Construction Materials, 2018, 8: 116-130.
[5] WEI F, YAO G, YANG Y,et al. Instance-Level Recognition and Quantification for Concrete Surface Bughole Based on Deep Learning[J]. Automation in Construction, 2019, 107: 102920.
[6] WEI W, DING L, LUO H,et al. Automated Bughole Detection and Quality Performance Assessment of Concrete Using Image Processing and Deep Convolutional Neural Networks[J]. Construction and Building Materials, 2021, 281: 122576.
[7] 住房和城乡建设部. 清水混凝土应用技术规程: JGJ 169—2009[S]. 北京: 中国建筑工业出版社, 2009.
[8] Concrete International Board. Tolerances on Blemishes of Concrete [EB/OL].(2021-12-20) [1973-02-12]. https:∥cibworld.org/.
[9] ZHU Z, BRILAKIS I. Machine Vision-based Concrete Surface Quality Assessment[J]. Journal of Construction Engineering and Management, 2010, 136(2): 210-218.
[10]贾钦基, 吴 立, 彭亚雄, 等. 基于AHP-FUZZY法的水下爆破影响区域危险性评价[J]. 长江科学院院报, 2019, 36(7): 112-117, 136.
[11]冯国平, 高宗军, 蔡五田, 等. 豫北山前修武地区地下水水化学特征及水质评价[J]. 长江科学院院报, 2021, 38(1): 27-34.
[12]饶 军, 沈 简, 唐绪波, 等. 基于信息熵的模糊评价法及其在滑坡危险性评价中的应用[J]. 长江科学院院报, 2017, 34(6): 62-66, 71.
[13]朱平华, 金伟良, 倪国荣. 在役混凝土桥梁结构耐久性评估方法[J]. 浙江大学学报(工学版), 2006, 40(4):658-667.
[14]CHAMPIRI M D, MOUSAVIZADEGAN S H, MOODI F. A Fuzzy Classification System for Evaluating the Health Condition of Marine ConcreteStructures[J]. Journal of Advanced Concrete Technology, 2012, 10(3): 95-109.
[15]HAMDIA K M, ARAFA M, ALQEDRA M. Structural Damage Assessment Criteria for Reinforced Concrete Buildings by Using a Fuzzy Analytic Hierarchy Process[J]. Underground Space, 2018, 3(3): 243-249.
[16]邓聚龙. 灰色控制系统[J].华中科技大学学报(自然科学版), 1982, 10(3): 9-18.
[17]CHANG W C, WEN K L, CHEN H S,et al. The Selection Model of Pavement Material via Grey Relational Grade[C]∥SMC 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. Cybernetics Evolving to Systems, Humans, Organizations, and Their Complex Interactions. Nashville, TN. October 8-11, 2000. New York: IEEE Press, 2002: 3388-3391.
[18]薛鹏飞, 项贻强. 基于灰色关联度和模糊识别的混凝土结构耐久性评估[J]. 混凝土, 2009(2): 14-16.
[19]周世华,杨华全,董 芸,等.水泥品质与抗裂性的灰色关联分析[J].长江科学院院报,2010,27(12):79-82.
[20]徐德儒, 邹春霞, 牛德元, 等. 模袋混凝土抗冻性与孔结构试验研究[J]. 硅酸盐通报, 2019, 38(8) :2631-2636, 2649.
[21]CANNY J. A Computational Approach to Edge Detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(6): 679-698.
[22]OTSU N. A Threshold Selection Method from Gray-Level Histograms[J]. IEEE Transactions on Systems, Man, and Cybernetics, 1979, 9(1): 62-66.
[23]LEE S U, CHUNG S Y, PARK R H. A Comparative Performance Study of Several Global Thresholding Techniques for Segmentation[J]. Computer Vision, Graphics, and Image Processing, 1990, 52(2): 171-190.
[24]MAINI R, AGGARWAL D H. Study and Comparison of Various Image Edge Detection Techniques[J]. International Journal of Image Processing, 2009, 3(1): 1-11.
[25]MA B, WANG H. Rheological Properties of Self-compacting Concrete Paste Containing Chemical Admixtures[J]. Journal of Wuhan University of Technology(Materials Science Editon), 2013, 28(2): 291-297.
[26]黄 海, 元 强, 邓德华, 等. 掺化学外加剂水泥净浆的含气量稳定性与流变性能的关系(英文)[J]. 硅酸盐学报, 2019, 47(11): 1593-1604.
[27]BELMONTE I M, BENITO SAORIN F J, COSTA C P, et al. Quality of the Surface Finish of Self-compacting Concrete[J]. Journal of Building Engineering, 2020, 28: 101068.
[28]KOKADOT, HOSODA T , MIYAGAWA T, et al. Study on a Method of Evaluating a Yield Value of Fresh Concrete with a Slump Flow Value[J]. Doboku Gakkai Ronbunshu, 1997(578): 19-29.
[29]SEDRAN T, DE LARRARD F. Optimization of Self-compacting Concrete Thanks to Packing Model[C]∥Proceedings of 1st International RILEM Symposium on Self- compacting Concrete. Stockholm, September 13-15, 1999: 321-332.
基金
贵州省科技计划项目(黔科合支撑[2020]2Y036号);贵州省科技计划项目(黔科合支撑[2021]一般360)
PDF(1141 KB)
