| INORGANIC MATERIALS AND CERAMIC MATRIX COMPOSITES |
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| Research Progress of the Use of Machine Learning Algorithm in Performance Design of Self-compacting Concrete |
| LONG Wujian1,2, LUO Shengyu1,2, CHENG Boyuan1,2, FENG Ganlin1,2, LI Lixiao1,2,*
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1 College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, Guangdong, China
2 Guangdong Provincial Key Laboratory of Durability for Marine Civil Engineering, Shenzhen 518060, Guangdong, China |
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Abstract In order to clarify the complex coupling mechanisms between the components of self-compacting concrete materials and working, mechanical and durability performances, machine learning methods have been increasingly applied to the design and optimization of self-compacting concrete mixes and performance analysis in recent years. Machine learning methods have the ability to learn intrinsic patterns or mapping relationships between data in complex data sets, and are considered a promising technical tool in the field of self-compacting concrete design for mode-ling mapping relationships between concrete raw material mixes and performances. However, machine learning methods face a number of challenges in the field of self-compacting concrete performance design based on machine learning due to the limitations of its reliance on unsatisfiable data sets and poor interpretability of algorithm architectures. This paper systematically summarizes and compares the current applications of machine learning in the design of self-compacting concrete performances, focusing on the main technical difficulties faced by data-driven machine learning algorithms when applied to the field of self-compacting concrete design:the challenges of high dimensionality and small sample data, the challenges of multi-objective optimization of performances, and the challenges of complex and uninterpretable models. The paper also summarizes the development trends and future directions of machine learning applications in the field of self-compacting concrete material performances design.
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Published:
Online: 2024-06-25
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| Fund:National Natural Science Foundation of China-Shandong United Fund (U2006223), Shenzhen Science and Technology Plan Project (JCYJ20190808151011502), the Key-Area Research and Development Program of Guangdong Province (2019B111107003). |
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1 Lyu X J, Ding Y B, Cao M L. Concrete, 2013, 286(8), 105(in Chinese).
吕兴军, 丁言兵, 曹明莉. 混凝土, 2013, 286(8), 105.
2 Li S Y. Research on the performance of fiber-reinforced self-compacting lightweight concrete and analysis of its application. Master's Thesis, Henan University, China, 2022(in Chinese).
李衫元, 纤维增强自密实轻质混凝土性能研究及应用分析. 硕士学位论文, 河南大学, 2022.
3 Liu Y J. Research on the workability testing and mix design method of fresh concrete and its application in the design of self-compacting concrete. Master's Thesis, China Building Materials Academy, China, 2000(in Chinese).
刘艳军, 新拌混凝土工作性测试和配合比设计方法的研究及其在自密实混凝土设计中的应用. 硕士学位论文, 中国建筑材料科学研究院, 2000.
4 Nie D. Research on the design method of self-compacting concrete ratio based on net paste rheology. Ph. D. Thesis, Tsinghua University, China, 2013(in Chinese).
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. 博士学位论文, 清华大学, 2013.
5 Wu X. Mechanical properties of self-compacting light-aggregate concrete. Ph. D. Thesis, Dalian University of Technology, China, 2014(in Chinese).
吴熙. 自密实轻骨料混凝土的力学性能研究. 博士学位论文, 大连理工大学, 2014.
6 Hong J. Study on the long-term performance of high-strength self-compacting concrete with mechanism sand, Master's Thesis, Guizhou University, China, 2021(in Chinese).
洪靖. 机制砂高强自密实混凝土长期性能研究. 硕士学位论文, 贵州大学, 2021.
7 Tao H L. Study on physical and mechanical properties of steel fiber high strength self-compacting concrete. Master's Thesis, Zhongyuan University of Technology, China, 2022(in Chinese).
陶宏利, 钢纤维高强自密实混凝土物理力学性能研究. 硕士学位论文, 中原工学院, 2022.
8 Liu S G, Zhu D H, Wan Y N. China Harbour Engineering, 2011, 177(6), 82(in Chinese).
刘思国, 朱德华, 万一宁. 中国港湾建设, 2011, 177(6), 82.
9 Lyu M. Study on the test method for optimizing the workability of self-compacting concrete. Master's Thesis, North China University of Technology, China, 2019(in Chinese).
吕淼, 自密实混凝土工作性能优化测试方法研究. 硕士学位论文, 北方工业大学, 2019.
10 Khaleel O R, Razak H A. Materials and Design, 2014, 53, 691.
11 Dinakar P, Sethy K P, Sahoo U C. Materials and Design, 2013, 43, 161.
12 Kanadasan J, Razak H A. Materials & Design, 2014, 56, 9.
13 Bouziani T. Construction and Building Materials, 2013, 49, 308.
14 Ferrara L, Park Y D, Shah S P. Cement and Concrete Research, 2007, 37, 957.
15 Sun Z J. Concrete, 2011, 262(8), 30(in Chinese).
孙仲健. 混凝土, 2011, 262(8), 30.
16 Shang L. Research on the design of self-compacting concrete ratio of mechanism sand based on artificial neural network technology. Master's Thesis, Shenzhen University, China, 2020(in Chinese).
尚莉. 基于人工神经网络技术的机制砂自密实混凝土配合比设计研究. 硕士学位论文, 深圳大学, 2020.
17 Shi C J, Wu Z M, Xiao J F, et al. Construction and Building Materials, 2015, 101, 741.
18 Huang Y M, Zhang J F, Ann F T, et al. Construction and Building Materials, 2020, 260, 120457.
19 Fan D Q, Yu R, Fu S Y, et al. Cement & Concrete Composites, 2021, 122, 104171.
20 Purnell P, Black L. Cement and Concrete Research, 2012, 42, 874.
21 Zhou J K. Public Communication of Science & Technology, 2019, 11(6), 153(in Chinese).
周昀锴. 科技传播, 2019, 11(6), 153.
22 Liu Y, Zhou X X, Yang Z W, et al. Journal of the Chinese Ceramic So-ciety, 2022, 50(3), 863(in Chinese).
刘悦, 邹欣欣, 杨正伟, 等. 硅酸盐学报, 2022, 50(3), 863.
23 Nehdi M, El Chabib H, El Naggar M. Aci Materials Journal, 2001, 98, 394.
24 Zhou S Q, Shi J J, Yang X F, et al. Journal of Water Resources and Architectural Engineering, 2005(4), 44(in Chinese).
周绍青, 石建军, 杨晓锋, 等. 水利与建筑工程学报, 2005(4), 44.
25 Ben Aicha, Al Asri, Zaher, et al. Powder Technology, 2022, 401, 117345.
26 Siddique R. Computers and Concrete, 2008, 5, 461.
27 Sonebi M, Cevik A, Grunewald S, et al. Construction and Building Materials, 2016, 106, 55.
28 Azimi-Pour M, Eskandari-Naddaf H, Pakzad A. Construction and Buil-ding Materials, 2020, 230, 117021.
29 Zhang J F, Ma G W, Huang Y M, et al. Construction and Building Materials, 2019, 210, 713.
30 Liu H B, Zhang F, Chen Z H, et al. Journal of Civil and Environmental Engineering, 2022(1), 14(in Chinese).
刘红波, 张帆, 陈志华, 等. 土木与环境工程学报, 2022(1), 14.
31 Xiao Q N. Multi-objective optimization of concrete mix ratio based on artificial intelligence. Master's Thesis, Guangzhou University, China, 2022(in Chinese).
肖祁南. 基于人工智能的混凝土配合比多目标优化研究. 硕士学位论文, 广州大学, 2022.
32 Black J E, Kueper J K, Williamson T S. ,Family Practice, 2022, 40(1), 200.
33 Sun D G. Information Technology and Informatization, 2022, 266(5), 190(in Chinese).
孙德刚. 信息技术与信息化, 2022, 266(5), 190.
34 Zhao C W. Shanxi Electronic Technology, 2020, 210(3), 94(in Chinese).
赵崇文. 山西电子技术, 2020, 210(3), 94.
35 Cortes C, Vapnik V. Machine Learning, 1995, 20, 273.
36 Ying H H. Support vector machine based concrete performance study. Master's Thesis, Hunan University, China, 2012(in Chinese).
殷宏辉. 基于支持向量机的混凝土性能研究. 硕士学位论文, 湖南大学, 2012.
37 Leo B. Machine Learning, 2001, 45, 5.
38 Wang X S, Huang M. Water Conservancy Science and Technology and Economy, 2020, 26(5), 17(in Chinese).
王香帅, 黄铭. 水利科技与经济, 2020, 26(5), 17.
39 Xu Z Y. Research on bridge data monitoring based on machine learning and deep learning. Master's Thesis, Guizhou University, China, 2022(in Chinese).
徐峥匀. 基于机器学习与深度学习的桥梁数据监测研究. 硕士学位论文, 贵州大学, 2022.
40 Lyu X L, Song J. Big data mining and statistical machine learning, China Renmin University Press, 2016, pp.239 (in Chinese).
吕晓玲, 宋捷. 大数据挖掘与统计机器学习, 中国人民大学出版社, 2016, pp.239.
41 Khayat K H, Ghezal A. Materials and Structures/Materiaux et Constructions, 1999, 32, 679.
42 Su N, Hsu K C, Chai H W. Cement and Concrete Research, 2001, 31, 1799.
43 Bui V K, Akkaya Y, Shah S P. ACI Materials Journal, 2002, 99, 549.
44 Li T, Wang H. Journal of ASTM International, 2006, 3,30.
45 Ozbay E, Oztas A, Baykasoglu A, et al. Construction and Building Materials, 2009, 23, 694.
46 Kheder G F, Al-Jadiri R S. ACI Materials Journal, 2010, 107, 490.
47 Abo Dhaheer M S, Al-Rubaye M M, Alyhya W S, et al. Journal of Sustainable Cement-Based Materials, 2016, 5, 199.
48 Huang Y J, Li Y, Yi J, et al. Construction Quality, 2014, 32(8), 67(in Chinese).
黄亚均, 李珏, 易继刚, 等. 工程质量, 2014, 32(8), 67.
49 Zhao Q X, Sun W, Jiang G Q, et al. Industrial Construction, 2006(S1), 850(in Chinese).
赵庆新, 孙伟, 姜国庆, 等. 工业建筑, 2006(S1), 850.
50 Abu Yaman M, Abd Elaty M, Taman M. Alexandria Engineering Journal, 2017, 56, 523.
51 Prashant B, Ankur B. Materials Today:Proceedings, 2022, 65, 603.
52 Annlin J, Ramesh K. Lecture Notes in Civil Engineering, 2022, 194, 461.
53 Yu F, Wang J X. Building Structure, 2021, 51(S1), 1316(in Chinese).
于方, 王嘉雄. 建筑结构, 2021, 51(S1), 1316.
54 Liu H L, Gu Y S, Shi J J, et al. Concrete, 2010, 247(5), 10(in Chinese).
刘华良, 顾杨圣, 石建军, 等. 混凝土, 2010, 247(5), 10.
55 Xiong E. Testing and numerical analysis of rheological properties of fresh self-compacting concrete with different aggregates. Master's Thesis,University of South China, China, 2011(in Chinese).
熊恩. 不同骨料新拌自密实混凝土流变性能测试和数值分析. 硕士学位论文, 南华大学, 2011.
56 Ben Aicha M, Al Asri Y, Zaher M, et al. Powder Technology, 2022, 401, 117345.
57 El Asri Y, Ben Aicha M, Zaher M, et al. Materials Today:Procee-dings, 2022, 58, 1114(in Chinese).
58 Zuo W Q. Study on robustness and shrinkage properties of self-compacting concrete. Ph. D. Thesis, Southeast University, China, 2018(in Chinese).
左文强. 自密实混凝土鲁棒性与收缩性能研究. 博士学位论文, 东南大学, 2018.
59 Ding Z C. Research on real-time adjustment method of self-compacting concrete working performance based on deep learning. Ph. D. Thesis, Tsinghua University, China, 2018(in Chinese).
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. 博士学位论文, 清华大学, 2018.
60 Prasad B K R, Eskandari H, Reddy B V V. Construction and Building Materials, 2009, 23, 117.
61 Uysal M, Tanyildizi H. Construction and Building Materials, 2012, 27, 404.
62 Asteris P G, Kolovos K G, Douvika M G, et al. European Journal of Environmental and Civil Engineering, 2016, 20, 102.
63 Serraye, Kenai, Boukhatem. Civil Engineering Journal (Iran), 2021, 7, 118.
64 Farooq F, Czarnecki S, Niewiadomski P, et al. Materials, 2021, 14, 27.
65 Yousef El A, Mouhcine B A, Mounir Z, et al. Materials Today:Procee-dings, 2022. 57, 859.
66 Aiyer B G, Kim D, Karingattikkal N, et al. KSCE Journal of Civil Engineering, 2014, 18, 1753.
67 Kutanaei S S, Choobbasti A J. Journal of Adhesion Science and Technology, 2015, 29, 487.
68 Mashhadban H, Kutanaei S S, Sayarinejad M A. Construction and Buil-ding Materials, 2016, 119, 277.
69 Xiao Q H, Li C M, Lei S X, et al. Advances in Civil Engineering, 2021, 2021, 6663767.
70 de-Prado-Gil J, Palencia C, Silva-Monteiro N, et al. Case Studies in Construction Materials, 2022, 16, e01046.
71 Jiang H D, Liu G L, Alyami H, et al. Journal of Cleaner Production, 2022, 340, 130580.
72 MashhadbanH, Kutanaei S S, Sayarinejad M A. Construction and Building Materials, 2016, 119, 277.
73 Pazouki G, Pourghorban A. European Journal of Environmental and Civil Engineering, 2022, 26, 5569.
74 Xu W S, Wu X G, Feng Z B, et al. Modern Tunnelling Technology, 2022, 59(6), 1(in Chinese).
徐文胜, 吴贤国, 冯宗宝, 等. 现代隧道技术, 2022, 59(6), 1.
75 Zhang J F, Huang Y M, Wang Y H, et al. Construction and Building Materials, 2020, 253, 713.
76 Prasad B K R, Eskandari H, Reddy B V. Construction and Building Materials, 2009, 23, 117.
77 Uysal M, Tanyildizi H. Construction and Building Materials, 2011, 25, 4105.
78 Uysal M, Tanyildizi H. Construction and Building Materials, 2012, 27, 404.
79 Mazloom M, Yoosefi M. Computers and Concrete, 2013, 12, 285.
80 Aiyer B G, Kim D, Karingattikkal N, et al. Ksce Journal of Civil Engineering, 2014, 18, 1753.
81 Tavakoli H R, Omran O L, Shiade M F, et al. Latin American Journal of Solids and Structures, 2014, 11, 1906.
82 Asteris P G, Kolovos K G, Douvika M G, et al. European Journal of Environmental and Civil Engineering, 2016, 20, s102.
83 Douma O B, Boukhatem B, Ghrici M, et al. Neural Computing & Applications, 2017, 28, S707.
84 Jayaprakash G, Muthuraj M P. Cmc-Computers Materials & Continua, 2018, 54, 83.
85 Asteris P G, Kolovos K G. Neural Computing & Applications, 2019, 31, 409.
86 Zhang J F, Ma G W, Huang Y M, et al. Construction and Buil-ding Materials, 2019, 210, 713.
87 Al-Mughanam T, Aldhyani T H, Alsubari B, et al. Sustainability, 2020, 12, 13.
88 Kaloop M R, Samui P, Shafeek M, et al. Applied Sciences-Basel, 2020, 10, 17.
89 Boudali S, Abdulsalam B, Rafiean A H, et al Sustainability, 2021, 13, 28.
90 Farooq F, Czarnecki S, Niewiadomski P, et al. Materials, 2021, 14, 27.
91 de-Prado-Gil J, Palencia C, Jagadesh P, et al Materials, 2022, 15, 20.
92 de-Prado-Gil J, Palencia C, Silva-Monteiro N, et al. Case Studies in Construction Materials, 2023, 16, 17.
93 Faraj R H, Mohammed A A, Mohammed A, et al. Engineering with Computers, 2022, 38, 2365.
94 Pazouki G, Golafshani E M, Behnood A. Structural Concrete, 2022, 23, 1191.
95 Yuan J, Zhao M, Esmaeili-Falak M. Structural Concrete, 2022, 23, 753.
96 Nasr D, Behforouz B, Borujeni P R, et al. Construction and Building Materials, 2019, 229, 116888.
97 Salami B A, Rahman S M, Oyehan T A, et al. Measurement, 2020, 165, 108141. |
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2024, Vol. 38 
