1 School of Materials Science and Engineering, Southeast University, Nanjing 211189, China 2 Research Center of Green Building and Construction Materials, Southeast University, Nanjing 211189, China 3 China Railway Construction Group Co.,Ltd., Beijing 100040, China 4 China Railway Construction Engineering Group Co., Ltd., Beijing 100160, China 5 China Railway 12th Bureau Group Co., Ltd., Taiyuan 030024, China
Abstract: In order to solve the problem of inefficiency and error caused by manual measurement and empirical evaluation in the quality inspection of fair-faced concrete projects, this paper presents a quantitative analysis method for the appearance quality of fair-faced concrete. The method was based on the UAV and the orbital large format scanner to collect the appearance image of concrete efficiently. The enhanced pore edge morphological parameters were extracted accurately by HiGauss, Sobel and Erode filters to accurately identify and to calculate APAR (appearance pore area ratio). SDCA (standard deviation of chromatic aberration)and SDR (standard deviation from RAL)were proposed to characterize the chromatic aberration of fair-faced concrete. The robustness of the method was verified based on light intensity and surface moisture content, and has been successfully applied to the preliminary quality acceptance of fair-faced concrete in Xiong'an Station of Beijing-Xiong'an Intercity Railway and Chaoyang Station of Beijing-Shenyang high-speed Railway. With the variation of light intensity, the average floating deviation of APAR of the same component was 4.82%, and the maximum deviation from manual measurement was 5.84%. The average floating deviation of SDCA of the same component was 8.60%, and the maximum floating deviation was 10.35%. The average floating deviation of SDR of the same component was 8.97%, and the maximum floating deviation was 13.58%. With the variation of surface moisture content, the average floating deviation of APAR of the same component was 9.03%, the maximum floating deviation was 9.16%, and the maximum deviation from manual measurement was 11.24%. The average floating deviation of SDCA of the same component was 6.55%, the maximum floating deviation was 8.27%, and the ave-rage floating deviation of SDR of the same component was 6.80%, and the maximum floating deviation was 10.11%. For the fair-faced concrete images collected by UAV, too much or too little light intensity would adversely affect the accuracy of the appearance quality analysis. As the surface moisture content increased, the three parameters all showed a downward trend, indicating that when drying, the difference in appearance quality was the most obvious. This method could provide timely supervision and feedback to the construction process, and had the advantages of high efficiency, quantification and automation.
1 Wang S, Xu Z Q, Wang T T, et al.Nature Communications, 2018, 9(1), 87. 2 Mesquita B, Boiger M, De Leersnyder J .Current Opinion in Psychology, 2016, 8, 31. 3 Yoo D Y, Yoon Y S .International Journal of Concrete Structures and Materials, 2016, 10(2), 125. 4 刘绍德.科技信息(科学教研), 2008(11), 104. 5 Marie I, Quiasrawi H .Journal of Cleaner Production, 2012, 37, 243. 6 李强, 李辛民, 孟闻远,等. 建筑技术, 2007(1), 6. 7 Marketsand Markets.Northbrook: MarketsandMarkets, 2017, 1, 1. 8 侯明华, 王旭峰, 蒋金生.施工技术, 2005(3), 12. 9 郑玉华, 周鹏华,卢松. 建筑技术, 2004(1), 54 . 10 何锦华, 王守合,杜建峰, 等. 混凝土,2006(6), 77. 11 顾勇新.清水混凝土工程施工技术及工艺, 中国建筑工业出版社, 2006. 12 戴永宁.南京长江第三大桥清水混凝土施工技术, 人民交通出版社, 2006. 13 Bharat Bhushan Jindal.Construction and Building Materials, 2019, 227, 116644. 14 Anahí López, Gastón Alejandro Guzmán, Alejandro Ramón Di Sarli. Construction and Building Materials, 2016, 123, 248. 15 张建雄, 缪昌文, 刘加平,等. 混凝土, 2008(1), 101. 16 Ernest D. Mitchell.In: Proceedings of the 5th International Symposium on Cement and Concrete. Hubei, 2002, pp.7. 17 Hagiwara K, Yamaguchi T.In: 56th Annual Conference of the Society of Instrument and Control Engineers of Japan. Japan, 2017, pp. 986. 18 Liao K W, Lee Y T.Automation in Construction, 2016, 71, 294. 19 Lemaire G, Escadeillas G, Ringot E.Construction and Building Mate-rials, 2005, 19(8), 604. 20 刘海宝. 地铁车站用清水混凝土关键技术研究. 硕士学位论文, 青岛理工大学, 2018. 21 Klovas A, Dauksys M.Materials Science, 2013, 19(3), 343. 22 Liu B, Yang T.Construction and Building Materials, 2017, 137, 432. 23 Da Silva W R L, Stemberk, Petr.Advances in Engineering Software, 2013, 64, 47. 24 Gartner. https://www.gartner.com/en/doc/432920-top-10-strategic-technology-trends-for-2020. 25 Anette E, Andreas K, Carlos C, et al.Earth Surface Dynamics, 2016, 4(2), 359. 26 Telmo A, Jonas H, Luis P, et al.Remote Sensing, 2017, 9(11), 1110. 27 Maser Hossein Motlagh, Miloud Bagaa, Tarik Taled.IEEE Communications Magazine: Articles, News, and Events of Interest to Communications Engineers, 2017, 55(2), 128. 28 Chianucci F, Disperati L, Guzzi D, et al.International Journal of Applied Earth Observation and Geoinformation, 2016, 47, 60. 29 赵来喜. 建材技术与应用, 2003(3), 43. 30 DJI大疆创新.https://www.dji.com/cn/mavic-2?site=brandsite & from=nav. 31 Geusebroek J M, Smeulders A W M, vande Weijer J .IEEE transactions on image processing, 2003, 12(8), 938. 32 Shao X, Dai X, Chen Z, et al.Applied Optics, 2016, 55(4), 696. 33 Gonzalez C I, Melin P, Castro J R, et al.Soft Computing, 2016, 20(2), 773. 34 Talab A M A, Huang Z, Xi F, et al.Optik-International Journal for Light and Electron Optics, 2016, 127(3), 1030. 35 James M R, Robson S, Smith M W .Earth Surface Processes and Landforms, 2017, 42(12), 1769. 36 Han P, Dong D, Zhao X, et al.Computers and Electronics in Agriculture, 2016, 123(C), 232. 37 Yuan F, Wang Z, Li X, et al.Advanced Materials, 2017, 29(3), 1604436. 38 Cha Y J, Choi W, Buyukozturk O.Computer-Aided Civil and Infrastructure Engineering, 2017, 32(5), 361. 39 Prasanna P, Dana K J, Gucunski N, et al.IEEE Transactions on Automation Science and Engineering, 2016, 13(2), 591.
渝公网安备50019002502923号 版权所有：《材料导报》编辑部
2020, Vol. 34 
