Affiliations: [a] National and Provincial Joint Engineering Laboratory of Road & Bridge Disaster Prevention and Control, Lanzhou Jiaotong University, Lanzhou, PR China
| [b] State Key Laboratory of High Preformance in Civil Engineering Materials, Jiangsu Research Institute of Building Science CO., LTD, Nanjing, PR China
| [c] School of Materials Science and Engineering, Southeast University, Nanjing, PR China
| [d] Civil Engineering Department, Lanzhou Jiaotong University, Lanzhou, PR China
Correspondence:
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Corresponding author. Jinpeng Dai, National and Provincial Joint Engineering Laboratory of Road & Bridge Disaster Prevention and Control, Lanzhou Jiaotong University, Lanzhou 730070, PR China. Tel.: +086 139 1949 5834; E-mail: daijp@mail.lzjtu.cn.
Abstract: This study explores nine machine learning (ML) methods, including linear, non-linear and ensemble learning models, using nine concrete parameters as characteristic variables. Including the dosage of cement (C), fly ash (FA), Ground granulated blast furnace slag (GGBS), coarse aggregate (G), fine aggregate (S), water reducing agent (WRA) and water (W), initial gas content (GC) and number of freeze-thaw cycles (NFTC), To predict relative dynamic elastic modulus (RDEM) and mass loss rate (MLR). Based on the linear correlation analysis and the evaluation of four performance indicators of R2, MSE, MAE and RMSE, it is found that the nonlinear model has better performance. In the prediction of RDEM, the integrated learning GBDT model has the best prediction ability. The evaluation indexes were R2 = 0.78, MSE = 0.0041, MAE = 0.0345, RMSE = 0.0157, SI = 0.0177, BIAS = 0.0294. In the prediction of MLR, ensemble learning Catboost algorithm model has the best prediction ability, and the evaluation indexes are R2 = 0.84, MSE = 0.0036, RMSE = 0.0597, MAE = 0.0312, SI = 5.5298, BIAS = 0.1772. Then, Monte Carlo fine-tuning method is used to optimize the concrete mix ratio, so as to obtain the best mix ratio.
Keywords: Machine learning, relative dynamic elastic modulus, mass loss rate, sensitivity analysis, optimization design of mix proportions
