Concrete Strength Prediction of the Three Gorges Dam Based on Machine Learning Regression Model
XU Xiaohang1, HU Zhangli1, LIU Jiaping1,*, LI Wenwei2, LIU Jianzhong3
1 School of Materials Science and Engineering, Southeast University, Nanjing 211189, China 2 China Three Gorges Corporation, Beijing 100038, China 3 Jiangsu Sobute New Materials Co., Ltd., Nanjing 211103, China
Abstract: Artificial neural network, decision tree and support vector machine are commonly used machine learning algorithms for concrete strength prediction. For learning and applying of the experience about the raw material selection and mixture design of Three Gorges Dam mass concrete, and providing guidance for dam maintenance and construction of other water conservancy projects, this study established a relationship between material properties, mixture design and concrete strength based on the 28-day compressive strength data of concrete used in the main project of Three Gorges Dam. Combined with random forest characteristic weight ranking and statistical analysis, cement dosage, concrete temperature and water-to-cement ratio were found to be the three key characteristic parameters dominating the compressive strength of the Three Gorges Dam concrete. Meanwhile, the prediction efficiency of the commonly used machine learning algorithms on 28-day concrete strength of the Three Gorges Dam were discussed. According to the prediction results of the three different kinds of machine learning algorithms after comprehensive optimization of the fixed characteristic parameters, general parameters and hyper-parameters, the Epsilon-support vector regression (SVR) algorithm with systematic comprehensive optimization was found to be the best in prediction.
徐潇航, 胡张莉, 刘加平, 李文伟, 刘建忠. 基于机器学习回归模型的三峡大坝混凝土强度预测[J]. 材料导报, 2023, 37(2): 22010068-9.
XU Xiaohang, HU Zhangli, LIU Jiaping, LI Wenwei, LIU Jianzhong. Concrete Strength Prediction of the Three Gorges Dam Based on Machine Learning Regression Model. Materials Reports, 2023, 37(2): 22010068-9.
1 Young B A, Hall A, Pilon L, et al. Cement and Concrete Research, 2019, 115, 379. 2 Yeh I C. Cement and Concrete Research, 1998, 28(12), 1797. 3 Feng D C, Liu Z T, Wang X D, et al. Construction and Building Materials, 2020, 230, 117000. 4 Fan L, Liu G Y, Jin D Z, et al. Concrete, 2017(3), 97 (in Chinese). 樊磊, 刘光焰, 金大智, 等. 混凝土, 2017(3), 97. 5 Fang T, Wang J F, Zhang X Y, et al. Concrete, 2016(12), 38 (in Chinese). 方涛, 王俊锋, 张鑫月, 等. 混凝土, 2016(12), 38. 6 Cao F, Zhou Y, Wang C X, et al. Bulletin of the Chinese Ceramic Society, 2021, 40(1), 90 (in Chinese). 曹斐, 周彧, 王春晓, 等. 硅酸盐通报, 2021, 40(1), 90. 7 Deng F, He Y, Zhou S, et al. Construction and Building Materials, 2018, 175, 562. 8 Hu Y, Zhang L S, Yuan F Y, et al. Construction Technology, 2020, 49(17), 89 (in Chinese). 胡毅, 张陆山, 袁福银, 等. 施工技术, 2020, 49(17), 89. 9 Jaafari A, Panahi M, Pham B T, et al. Catena, 2019, 175, 430. 10 Yuan Z, Wang L N, Ji X. Advances in Engineering Software, 2014, 67, 156. 11 Van Dao D, Ly H B, Trinh S H, et al. Materials, 2019, 12(6), 983. 12 Behnood A, Golafshani E M. Journal of Cleaner Production, 2018, 202, 54. 13 Bui D K, Nguyen T, Chou J S, et al. Construction and Building Materials, 2018, 180, 320. 14 Pham A D, Hoang N D, Nguyen Q T. Journal of Computing in Civil Engineering, 2016, 30(3), 06015002. 15 Yu Y, Li W, Li J, et al. Construction and Building Materials, 2018, 184, 229. 16 Tuan N S, Wakim J, To Q D, et al. Construction and Building Materials, 2020, 260, 119757. 17 Friedman J H. Annals of Statistics, 2001, 29(5), 1189. 18 Chen T Q, Guestrin C, Assoc Comp M. In:Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Francisco, 2016, pp. 785. 19 Zhou J C. Artificial neural network, the realization of the sixth generation computer, China Popular Science Press, China, 1993, pp. 47 (in Chinese). 周继成. 人工神经网络, 第六代计算机的实现, 科学普及出版社, 1993, pp. 47. 20 Ben Chaabene W, Flah M, Nehdi M L. Construction and Building Materials, 2020, 260, 119889. 21 Zhou Z H. Machine learning, Tsinghua University Press, China, 2016, pp. 179 (in Chinese). 周志华. 机器学习, 清华大学出版社, 2016, pp. 179. 22 Zhang Y K, Li Y L, Song J Z, et al. International Journal of Electrical Power & Energy Systems, 2020, 116, 105526. 23 José U. Python for Probability, Statistics and Machine Learning, Cham, Springer, GER, 2019, pp. 259. 24 Behnood A, Behnood V, Gharehveran M M, et al. Construction and Building Materials, 2017, 142, 199. 25 Behnood A, Golafshani E M. Construction and Building Materials, 2020, 243, 118152. 26 Chou J S, Chiu C K, Farfoura M, et al. Journal of Computing in Civil Engineering, 2011, 25(3), 242. 27 Dantas A T A, Batista Leite M, de Jesus Nagahama K. Construction and Building Materials, 2013, 38, 717. 28 Khademi F, Jamal S M, Deshpande N, et al. International Journal of Sustainable Built Environment, 2016, 5(2), 355. 29 Naderpour H, Rafiean A H, Fakharian P. Journal of Building Engineering, 2018, 16, 213. 30 Atici U. Expert Systems with Applications, 2011, 38(8), 9609. 31 Wang Y, Zhang Y T, Hu H X, et al. Journal of Building Materials, 2014, 17(4), 672 (in Chinese). 王岩, 张友桃, 胡鸿翔, 等. 建筑材料学报, 2014, 17(4), 672. 32 Xia K W, Li C B, Shen J Y. Computer Science, 2005, 32(10), 143 (in Chinese). 夏克文, 李昌彪, 沈钧毅. 计算机科学, 2005, 32(10), 143. 33 Chen B, Guo X M, Liu G H. Journal of Hydroelectric Engineering, 2011, 30(6), 251 (in Chinese). 陈斌, 郭雪莽, 刘国华. 水力发电学报, 2011, 30(6), 251. 34 Liu P, Fang C. Science Technology and Engineering, 2012, 12(18), 4549 (in Chinese). 刘萍, 方崇. 科学技术与工程, 2012, 12(18), 4549. 35 Han Q, Gui C, Xu J, et al. Construction and Building Materials, 2019, 226, 734.
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2023, Vol. 37 
