Research on Prediction Model of Concrete Compressive Strength Based on Random Forest
1. Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Hangzhou 310018, China
2. SINOMA Bonyear (Hangzhou) Intelligent Technology Co. Ltd., Hangzhou 310000, China
| Abstract: | Concrete compressive strength prediction is a key part of batching ratio design, the traditional concrete cube compressive strength standard test method is vulnerable to external influences, a random forest algorithm is proposed to predict the compressive strength of concrete, by optimizing the data of cement, blast furnace slag mixing, fly ash mixing, water content, high efficiency water reducer dosage, coarse aggregates, fine aggregates content, and age, etc., as raw material indicators. The optimized data set is used as the input data set, and the data set is divided to construct the model, and then the model parameters are optimized by defining the parameter network and performing the grid search cross-validation to construct the perfect models. The results show that the prediction accuracy of the prediction model based on the random forest algorithm (R²=0.91547) is much higher than that of the support vector regression model (R²=0.76802) and the decision tree model (R²=0.87539) and the error is small (RMSE=5.24087), which is of great significance for the research of compressive strength prediction model. |
| Keywords: | Random Forest; Concrete; Compression Strength; Prediction |
| DOI: | 10.57237/j.cst.2023.04.003 |
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Received: 8 September 2023
Accepted: 24 October 2023
Published Online: 28 October 2023
Funding
| 1. | 2022年度杭州市重大科技创新项目 (2022AIZD0085, 2022AIZD0016) |
References
| [1] | 罗广彬, 洪成雨, 程志良等. 基于BP和GA-BP神经网络的混凝土抗压强度预测研究 [J]. 混凝土, 2023, No. 401(03): 37-41. |
| [2] | 周宜松, 赵传萍, 黄耀明等. 基于机器学习技术的混凝土抗压强度预测研究 [J]. 安阳工学院学报, 2022, 21(06): 91-5. |
| [3] | 邓初晴, 郑夷洲, 刘学等. 基于决策树算法的深圳地区混凝土回弹测强曲线的研究探索 [J]. 建筑监督检测与造价, 2022, 15(03): 36-41. |
| [4] | 田欣. 决策树算法的研究综述 [J]. 现代营销 (下旬刊), 2017, (01): 36. |
| [5] | 陈洪根, 龙蔚莹, 李昕等. 基于BP神经网络的粉煤灰混凝土抗压强度预测研究 [J]. 建筑结构, 2021, 51(S2): 1041-5. |
| [6] | 徐国强, 苏幼坡, 韩佃利等. 基于BP神经网络的绿色混凝土抗压强度预测模型 [J]. 混凝土, 2013, No. 280(02): 33-5+49. |
| [7] | 赵明亮, 水中和, 周华新等. 中低强度等级混凝土抗压强度的BP神经网络模型预测研究 [J]. 混凝土, 2021, No. 377(03): 35-8. |
| [8] | 王继宗, 倪鸿光, 何锦云等. 混凝土强度预测和模拟的智能化方法 [J]. 土木工程学报, 2003, 55(10): 24-9. |
| [9] | 路佳佳. 基于交叉验证的集成学习误差分析 [J]. 计算机系统应用, 2023, 32(01): 302-9. |
| [10] | 张浩, 朱吉鹏, 卓德才等. 基于随机森林和支持向量机的混凝土抗压强度预测模型研究 [J]. 工程与建设, 2022, 36(06): 1784-8+815. |
| [11] | CHOU J S, ANH-DYC P. Smart artificial firefly colony algorithm-based support vector regression for enhanced forecasting in civil engineering [J]. Computer-Aided Civil and Infrastructure Engineering, 2015, 30(9): 715-729. |
| [12] | MOZUMDER R A, ROY B, LASKAR A I. Support Vector Regression Approach to Predict the Strength of FRP Confined Concrete [J]. Arabian Journal for Science and Engineering, 2017, 42(3): 26-27. |
| [13] | 曹斐, 周彧, 王春晓等. 一种改进的支持向量回归的混凝土强度预测方法 [J]. 硅酸盐通报, 2021, 40(01): 90-7. |
| [14] | 王奕森, 夏树涛. 集成学习之随机森林算法综述 [J]. 信息通信技术, 2018, 12(01): 49-55. |
| [15] | 崔晓宁, 王起才, 张戎令等. 基于随机森林的高性能混凝土抗压强度预测 [J]. 兰州交通大学学报, 2021, 40(06): 1-6+14. |
| [16] | 吴贤国, 刘鹏程, 陈虹宇等. 基于随机森林的高性能混凝土抗压强度预测 [J]. 混凝土, 2022, (01): 17-20+4. |
| [17] | YEH I-C. Modeling slump of concrete with fly ash and superplasticizer % J Computers and Concrete [J]. 2008, 5(6): 59-62. |
| [18] | DEROUSSEAU M A, LAFTCHIEV E, KASPRZYK J R, et al. A comparison of machine learning methods for predicting the compressive strength of field-placed concrete [J]. Construction and Building Materials, 2019, 228(C): 10-13. |
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