基于神经网络与回归分析的多孔混凝土性能预测

doi:10.13229/j.cnki.jdxbgxb.20230239

摘要/Abstract

摘要：

为实现对多孔混凝土28 d抗压强度和透水系数两项关键性能指标的预测，通过相关性分析确定水胶比、胶凝材料用量、骨胶比和实测孔隙率为模型输入参数，然后基于构建的数据集，采用神经网络和回归分析分别建立了4种预测模型。结果表明：两种统计回归模型的预测误差较大，对多变量的响应缺乏敏感性，其拟合优度R²均在0.681以下；两种神经网络模型更适合解决复杂、多变量的性能预测问题，其中经遗传算法优化的神经网络抗压强度预测模型的拟合优度R²达到0.9以上，体现了该预测模型的精准性和稳定性。研究成果可为多孔混凝土的配合比优化设计与性能调控提供参考和指导。

关键词: 道路与铁道工程, 多孔混凝土, 抗压强度, 透水系数, 神经网络, 回归分析

Abstract:

In order to predict the 28d compressive strength and permeability coefficient of porous concrete， the water-cement ratio， cementitious material dosage， bone-cement ratio and measured porosity were determined as model input parameters through correlation analysis. Furthermore， the prediction models for the two indicators are established based on the constructed dataset using artificial neural networks and regression analysis， respectively. The results showed that the statistical regression models had significant prediction errors， lacked sensitivity to multivariate responses， and their goodness-of-fit R² were all below 0.681. The artificial neural network model is more suitable for solving complex and multivariate performance prediction problems. The neural network compressive strength prediction model optimized by the genetic algorithm has a goodness-of-fit R² of more than 0.9， which reflects the accuracy and stability of the prediction model. The research results could provide reference and guidance for the optimal design of the mix ratio and performance regulation of porous concrete.

Key words: highway and railway engineering, porous concrete, compressive strength, permeable coefficient, neural network, regression analysis

中图分类号:

U414

曲广雷,闫宗伟,郑木莲,刘红,袁月明. 基于神经网络与回归分析的多孔混凝土性能预测[J]. 吉林大学学报(工学版), 2025, 55(1): 269-282.

Guang-lei QU,Zong-wei YAN,Mu-lian ZHENG,Hong LIU,Yue-ming YUAN. Performance prediction of porous concrete based on neural network and regression analysis[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(1): 269-282.

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类别	SiO₂	Al₂O₃	Fe₂O₃	CaO	MgO	Na₂O	SO₃	LOI
水泥	21.59	4.57	5.01	64.15	1.98	0.11	2.19	0.95
硅灰	97.74	0.39	0.08	0.20	0.15	0.09	-	-

编号	水胶比	水泥	骨胶比	粗集料	水	硅灰	高效减水剂
1	0.20	450	3.19	1 435	90	-	11.25
2	0.20	405	3.30	1 485	90	45	10.13
3	0.24	495	2.93	1 450	119	-	0.98
4	0.24	445	3.01	1 490	119	50	0.98
5	0.26	480	2.91	1 395	125	-	12.00
6	0.26	432	3.29	1 425	125	48	10.80
7	0.28	420	3.33	1 400	118	-	10.50
8	0.28	378	3.45	1 450	118	42	9.45
9	0.30	390	3.85	1 505	117	-	9.75
10	0.30	351	3.97	1 550	117	39	8.76
11	0.32	460	3.08	1 420	147	-	11.5
12	0.32	414	3.20	1 470	147	46	10.35

水胶比	水泥量/（kg·m^-3）	骨胶比	实测空隙率/%	抗压强度/MPa	透水系数/（mm·s^-1）	数据来源
0.25	480.0	3.26	18.50	22.10	18.61	文献［22］
0.27	320.0	5.06	28.15	11.20	31.21	文献［23］
0.3	416.6	3.64	26.69	8.21	14.00	文献［24］
0.25	590.0	2.34	14.00	24.40	3.95	文献［25］
0.25	569.0	2.67	15.00	24.60	2.70	文献［26］
0.29	419.0	3.91	10.56	25.05	3.15	文献［27］
0.25	754.0	1.89	13.00	27.30	3.20	文献［28］
0.2	400.0	3.94	20.50	17.90	5.02	文献［29］
0.28	310.0	3.79	16.00	29.10	2.99	文献［30］
0.25	571.9	2.69	12.69	31.60	2.03	文献［31］
0.19	494.0	2.97	10.00	22.30	4.66	文献［32］
0.2	630.7	2.21	18.20	26.53	4.27	文献［33］
0.28	475.0	2.5	13.70	35.06	1.44	文献［34］
0.3	447.0	3.44	20.00	19.20	6.01	文献［35］
0.27	481.0	2.50	13.70	18.06	1.47	文献［36］
0.25	528.0	2.70	17.79	25.90	2.93	文献［37］
0.26	510.0	2.90	14.70	14.30	6.30	文献［38］
0.20	782.00	1.88	12.00	17.15	1.38	文献［39］
0.25	568.00	2.87	14.00	16.20	1.33	文献［40］
0.27	461.00	3.33	12.80	15.30	7.60	文献［41］
0.25	364.90	4.10	19.56	8.86	5.09	文献［42］
0.26	390.08	4.39	20.80	16.10	5.80	文献［43］
0.2	286.00	5.58	33.00	8.10	24.0	文献［44］
0.35	326.00	5.00	35.99	10.26	1.85	文献［45］
0.3	375.00	4.00	30.23	9.33	20.93	文献［46］
0.35	200.00	8.00	35.00	2.21	17.90	文献［47］
0.30	479.90	3.13	14.00	22.2	3.91	文献［48］
0.34	478.95	3.37	25.45	10.23	1.01	文献［49］
0.3	521.42	2.88	14.31	21.90	6.97	文献［50］
0.33	388.10	3.62	25.60	12.50	10.50	文献［51］
0.2	775.00	1.91	12.00	13.90	0.65	文献［52］

预测模型		MAE	MSE	RMSE	F值	R²
线性回归	抗压强度	6.483	71.359	8.447	156.68	0.539
线性回归	透水系数	3.328	20.691	4.548	156.87	0.540
非线性回归	抗压强度	4.871	43.125	6.567	83.084	0.681
非线性回归	透水系数	2.668	15.179	3.896	98.512	0.612

预测模型		MAE		RMSE		拟合优度R²
预测模型		训练集	测试集	训练集	测试集	训练集	测试集
BP	抗压强度	3.742	3.145	4.483	4.860	0.869	0.791
BP	透水系数	1.865	2.135	2.280	3.088	0.831	0.806
GA-BP	抗压强度	2.989	2.698	3.859	3.006	0.949	0.907
GA-BP	透水系数	1.895	1.974	2.984	2.620	0.912	0.895