Review of smart production techniques for the entire self-compacting concrete production process
LÜ Miao1, AN Xuehui1, LI Pengfei2, ZHANG Jingbin3, BAI Hao1
1. State Key Laboratory of Hydroscience and Engineering, Tsinghua University, Beijing 100084, China; 2. School of Hehai, Chongqing Jiaotong University, Chongqing 400074, China; 3. College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
Abstract：Self-compacting concrete (SCC) has good fluidity that can fill the voids without vibrations, but the concrete performance is very sensitive to material property changes. Existing SCC production methods have material quality management problems with production discontinuities leading to inaccurate material information and poor test results. New image recognition and artificial intelligence methods are enabling intelligent systems for the entire production process, including material property tests, mix design, and mixing production, and rheological property tests. Smart material property tests improve material quality management. The mix proportions can be accurately determined by intelligent mix design methods that cope with material property changes. Real-time monitoring of the mixing can optimize mix proportions during mixing. Smart rheological property tests can monitor the rheological properties of the self-compacting mixtures during mixing. This paper reviews the research on smart production for the entire SCC production process and summarizes current problems and future development prospects.
吕淼, 安雪晖, 李鹏飞, 张京斌, 白皓. 自密实混凝土全过程智能生产研究进展[J]. 清华大学学报（自然科学版）, 2022, 62(8): 1270-1280.
LÜ Miao, AN Xuehui, LI Pengfei, ZHANG Jingbin, BAI Hao. Review of smart production techniques for the entire self-compacting concrete production process. Journal of Tsinghua University(Science and Technology), 2022, 62(8): 1270-1280.
[1] OKAMURA H, OUCHI M. Self-compacting concrete[J]. Journal of Advanced Concrete Technology, 2003, 1(1):5-15. [2] 刘小洁, 余志武. 自密实混凝土的研究与应用综述[J]. 铁道科学与工程学报, 2006, 3(2):6-10. LIU X J, YU Z W. Research and application of self-compacting concrete[J]. Journal of Railway Science and Engineering, 2006, 3(2):6-10. (in Chinese) [3] 纪建林, 胡竟贤, 王毅. 自密实混凝土性能及其在三峡三期工程中的应用[J]. 西北水电, 2005(4):33-36. JI J L, HU J X, WANG Y. Self-dense concrete and its application in the third-stage construction of the Three Gorges Project[J]. Northwest Hydropower, 2005(4):33-36. (in Chinese) [4] 朱效荣, 李迁. 自密实大体积混凝土在国家大剧院超长环梁中的研究与应用[J]. 辽宁建材, 2005(1):8-10. ZHU X R, LI Q. Research and application of self-compacting mass concrete in super long ring beam of National Grand Theater[J]. LiaoNing Building Materials, 2005(1):8-10. (in Chinese) [5] 范灵晨. C50自密实混凝土在北京南站工程中的应用[J]. 建设科技, 2008(7):129. FAN L C. Application of self-compacting concrete in Beijing South Railway Station Project[J]. Construction Science and Technology, 2008(7):129. (in Chinese) [6] 金峰, 安雪晖, 石建军, 等. 堆石混凝土及堆石混凝土大坝[J]. 水利学报, 2005, 36(11):1347-1352. JIN F, AN X H, SHI J J, et al. Study on rock-fill concrete dam[J]. Journal of Hydraulic Engineering, 2005, 36(11):1347-1352. (in Chinese) [7] 刘运华, 谢友均, 龙广成. 自密实混凝土研究进展[J]. 硅酸盐学报, 2007, 35(5):671-678. LIU Y H, XIE Y J, LONG G C. Progress of research on self-compacting concrete[J]. Journal of the Chinese Ceramic Society, 2007, 35(5):671-678. (in Chinese) [8] 刘思国, 朱德华, 丁一宁. 基于流变学特性的SCC配合比设计研究进展[J]. 混凝土, 2012(3):38-42. LIU S G, ZHU D H, DING Y N. Advances of the research on self-compacting concrete mixture design based on rheological characteristics[J]. Concrete, 2012(3):38-42. (in Chinese) [9] 曹旗, 程银亮, 王晓峰. 纤维自密实混凝土综述[J]. 混凝土, 2016(1):123-126. CAO Q, CHENG Y L, WANG X F. Summary of fiber-reinforced self-consolidating concrete[J]. Concrete, 2016(1):123-126. (in Chinese) [10] 王坤云, 胡迪勇, 杨竹. 矿物掺合料对自密实混凝土工作性能影响研究综述[J]. 人民珠江, 2021, 42(8):46-54. WANG K Y, HU D Y, YANG Z. Study for the influence of mineral admixtures on the workability of self-compacting concrete[J]. Pearl River, 2021, 42(8):46-54. (in Chinese) [11] 安雪晖, 黄绵松, 大内雅博, 等. 自密实混凝土技术手册[M]. 北京:中国水利水电出版社, 2008. AN X H, HUANG M S, OUCHI M H, et al. Technical manual of self-compacting concrete[M]. Beijing:China Water & Power Press, 2008. (in Chinese) [12] 王春华. 混凝土生产的智能化和自动化应用技术[J]. 中国住宅设施, 2020(8):90-91. WANG C H. Intelligent and automatic application technology of concrete production[J]. China Housing Facilities, 2020(8):90-91. (in Chinese) [13] 林彬, 沈福良, 黄腾. 全环保智能化搅拌楼应用[J]. 建设机械技术与管理, 2019, 32(1):24-26. LIN B, SHEN F L, HUANG T. The application of an environmental protection and intelligent mixing plant[J]. Construction Machinery Technology & Management, 2019, 32(1):24-26. (in Chinese) [14] 夏小刚. 智能建造技术在预制T梁生产线的应用[J]. 建筑经济, 2020, 41(9):121-125. XIA X G. Application of intelligent construction technology in prefabricated T-beam production line[J]. Construction Economy, 2020, 41(9):121-125. (in Chinese) [15] BEHNOOD A, GOLAFSHANI E M. Artificial intelligence to model the performance of concrete mixtures and elements:A review[J]. Archives of Computational Methods in Engineering, 2021. DOI:10.1007/s11831-021-09644-0. [16] HU X X, LI B X, MO Y L, et al. Progress in artificial intelligence-based prediction of concrete performance[J]. Journal of Advanced Concrete Technology, 2021, 19(8):924-936. [17] ZHENG J X, HRYCIW R D. Identification and characterization of particle shapes from images of sand assemblies using pattern recognition[J]. Journal of Computing in Civil Engineering, 2018, 32(3):04018016. [18] SCHWEIZER S A, BUCKA F B, GRAF-ROSENFELLNER M, et al. Soil microaggregate size composition and organic matter distribution as affected by clay content[J]. Geoderma, 2019, 355:113901. [19] FAN W J, CHEN Z Q, LUO Z, et al. An aggregate gradation detection method based on multi-view information fusion[J]. Powder Technology, 2021, 388:7-16. [20] 郭美虹, 周新刚, 秦绪祥. 图像处理技术在混凝土骨料形状参数分析中的应用研究[J]. 烟台大学学报(自然科学与工程版), 2019, 32(4):382-390. GUO M H, GUO X G, QIN X X. Application study on image processing in analysis of shape parameters of concrete aggregate[J]. Journal of Yantai University (Natural Science and Engineering Edition), 2019, 32(4):382-390. (in Chinese) [21] RECH J, ALTHOFF K D. Artificial intelligence and software engineering:Status and future trends[J]. Themenschwerpunkt KI & SE, KI, 2004, 3:5-11. [22] SOLOY A, TURKI I, FOURNIER M, et al. A deep learning-based method for quantifying and mapping the grain size on pebble beaches[J]. Remote Sensing, 2020, 12(21):3659. [23] 熊越晗, 刘东燕, 刘东升, 等. 基于岩样细观图像深度学习的岩性自动分类方法[J]. 吉林大学学报(地球科学版), 2021, 51(5):1597-1604. XIONG Y H, LIU D Y, LIU D S, et al. Automatic lithology classification method based on deep learning of rock sample meso-image[J]. Journal of Jilin University (Earth Science Edition), 2021, 51(5):1597-1604. (in Chinese) [24] 李嘉俊. 基于深度学习的砂颗粒特征图像识别方法研究[D]. 北京:清华大学, 2019. LI J J. Research on optimizing mix proportion of self-compacting concrete based on paste rheological theory[D]. Beijing:Tsinghua University, 2019. (in Chinese) [25] 谢涛. 基于深度学习的微细粒矿物识别研究[D]. 徐州:中国矿业大学, 2020. XIE T. Research on recognition of fine-grained minerals based on deep learning[D]. Xuzhou:China University of Mining and Technology, 2020. (in Chinese) [26] 中国工程建设标准化协会. 自密实混凝土应用技术规程:CECS 203-2006[S]. 北京:中国计划出版社, 2006. China Association for Engineering Construction. Technical specification for self compacting concrete application:CECS 203-2006[S]. Beijing:China Planning Press, 2006. (in Chinese) [27] SU N, HSU K C, CHAI H W. A simple mix design method for self-compacting concrete[J]. Cement and Concrete Research, 2001, 31(12):1799-1807. [28] KHAYAT K H. Workability, testing, and performance of self-consolidating concrete[J]. ACI Materials Journal, 1996, 96(3):346-353. [29] SAAK A W, JENNINGS H M, SHAH S P. New methodology for designing self-compacting concrete[J]. ACI Materials Journal, 2001, 98(6):429-439. [30] BUI V K, AKKAYA Y, SHAH S P. Rheological model for self-consolidating concrete[J]. ACI Materials Journal, 2002, 99(6):549-559. [31] FERRARIS C F, BROWER L E. Comparison of concrete rheometers[J]. Concrete International, 2003, 25(8):41-47. [32] WU Q, AN X H. Development of a mix design method for SCC based on the rheological characteristics of paste[J]. Construction and Building Materials, 2014, 53:642-651. [33] 吴琼. 基于净浆流变性的自密实混凝土配合比设计方法研究[D]. 北京:清华大学, 2013. WU Q. The development of mix design method for self-compacting concrete based on the rheological characteristics of paste[D]. Beijing:Tsinghua University, 2013. (in Chinese) [34] LI L G, KWAN A K H. Concrete mix design based on water film thickness and paste film thickness[J]. Cement and Concrete Composites, 2013, 39:33-42. [35] 张京斌. 原材料特性对自密实混凝土净浆流变阈值的影响研究[D]. 北京:清华大学, 2020. ZHANG J B. Research on the effects of the raw material properties on paste rheological thresholds of self-compacting concrete[D]. Beijing:Tsinghua University, 2020. (in Chinese) [36] ROUSSEL N, STEFANI C, LEROY R. From mini-cone test to Abrams cone test:Measurement of cement-based materials yield stress using slump tests[J]. Cement and Concrete Research, 2005, 35(5):817-822. [37] CHIDIAC S E, MAADANI O, RAZAQPUR A G, et al. Controlling the quality of fresh concrete-A new approach[J]. Magazine of Concrete Research, 2000, 52(5):353-363. [38] TOUTOU Z, ROUSSEL N. Multi scale experimental study of concrete rheology:From water scale to gravel scale[J]. Materials and Structures, 2006, 39(2):189-199. [39] BANFILL P F G. Rheological methods for assessing the flow properties of mortar and related materials[J]. Construction and Building Materials, 1994, 8(1):43-50. [40] ZHANG J B, AN X H, YU Y Z, et al. Effects of coarse aggregate content on the paste rheological thresholds of fresh self-compacting concrete[J]. Construction and Building Materials, 2019, 208:564-576. [41] ZHANG J B, AN X H, NIE D. Effect of fine aggregate characteristics on the thresholds of self-compacting paste rheological properties[J]. Construction and Building Materials, 2016, 116:355-365. [42] ZHANG J B, AN X H, LI P F. Research on a mix design method of self-compacting concrete based on a paste rheological threshold theory and a powder equivalence model[J]. Construction and Building Materials, 2020, 233:117292. [43] YANG S, ZHANG J B, AN X H, et al. Effects of fly ash and limestone powder on the paste rheological thresholds of self-compacting concrete[J]. Construction and Building Materials, 2021, 281:122560. [44] LI P F, ZHANG T, AN X H, et al. An enhanced mix design method of self-compacting concrete with fly ash content based on paste rheological threshold theory and material packing characteristics[J]. Construction and Building Materials, 2020, 234:117380. [45] LI P F, RAN J, NIE D, et al. Improvement of mix design method based on paste rheological threshold theory for self-compacting concrete using different mineral additions in ternary blends of powders[J]. Construction and Building Materials, 2021, 276:122194. [46] NIE D, AN X H. Optimization of SCC mix at paste level by using numerical method based on a paste rheological threshold theory[J]. Construction and Building Materials, 2016, 102:428-434. [47] 聂鼎. 基于净浆流变理论的自密实混凝土配合比优化方法研究[D]. 北京:清华大学, 2016. NIE D. Research on optimizing mix proportion of self-compacting concrete based on paste rheological theory[D]. Beijing:Tsinghua University, 2016. (in Chinese) [48] JUEZ J M, ARTONI R, CAZACLIU B. Monitoring of concrete mixing evolution using image analysis[J]. Powder Technology, 2017, 305:477-487. [49] DAUMANN B, NIRSCHL H. Assessment of the mixing efficiency of solid mixtures by means of image analysis[J]. Powder Technology, 2008, 182(3):415-423. [50] 李书阳. 基于视觉信息的自密实混凝土工作性能评价方法研究[D]. 北京:清华大学, 2013. LI S Y. Research on workability evaluation of self-compacting concrete based on visual information[D]. Beijing:Tsinghua University, 2013. (in Chinese) [51] LI S Y, AN X H. Method for estimating workability of self-compacting concrete using mixing process images[J]. Computers and Concrete, 2014, 13(6):781-798. [52] DING Z C, AN X H. Deep learning approach for estimating workability of self-compacting concrete from mixing image sequences[J]. Advances in Materials Science and Engineering, 2018, 2018:6387930. [53] YANG L, AN X H. Estimating the workability of self-compacting concrete in different mixing conditions based on deep learning[J]. Computers and Concrete, 2020, 25(5):433-445. [54] YANG L, AN X H, DU S L. Estimating workability of concrete with different strength grades based on deep learning[J]. Measurement, 2021, 186:110073. [55] 丁仲聪. 基于深度学习的自密实混凝土工作性能实时调整方法研究[D]. 北京:清华大学, 2018. DING Z C. Research on real-time adjusting of self-compacting concrete workability based on deep learning[D]. Beijing:Tsinghua University, 2018. (in Chinese) [56] 聂鼎, 安雪晖. 基于图像处理的净浆扩展度测量工具开发[J]. 清华大学学报(自然科学版), 2016, 56(12):1249-1254. NIE D, AN X H. Mini-slump flow measurement tool based on self phase image processing[J]. Journal of Tsinghua University (Science and Technology), 2016, 56(12):1249-1254. (in Chinese) [57] KIM J H, PARK M. Visualization of concrete slump flow using the kinect sensor[J]. Sensors, 2018, 18(3):771. [58] 李继全. 商品混凝土塌落度自动检测技术研究及检测装置开发[D]. 济南:济南大学, 2019. LI J Q. Research on automatic detection technology of commercial concrete slump and development of testing equipment[D]. Jinan:University of Jinan, 2019. (in Chinese)
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