|本期目录/Table of Contents|

[1]王海英,李子彤,张英治,等.基于拌和生产数据的BP神经网络混凝土抗压强度预测[J].建筑科学与工程学报,2024,41(03):18-25.[doi:10.19815/j.jace.2023.12082]
　WANG Haiying,LI Zitong,ZHANG Yingzhi,et al.BP neural network prediction of concrete compressive strength based on mixing production data[J].Journal of Architecture and Civil Engineering,2024,41(03):18-25.[doi:10.19815/j.jace.2023.12082]
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基于拌和生产数据的BP神经网络混凝土抗压强度预测(PDF)
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《建筑科学与工程学报》[ISSN:1673-2049/CN:61-1442/TU]

卷:
41卷
期数:
2024年03期
页码:
18-25
栏目:
建筑材料
出版日期:
2024-05-20

文章信息/Info

Title:
BP neural network prediction of concrete compressive strength based on mixing production data
文章编号:
1673-2049(2024)03-0018-08
作者:
王海英1,李子彤1,张英治2,王晨光1
(1. 长安大学 工程机械学院,陕西 西安 710064; 2.陕西交通控股集团有限公司,陕西 西安 710075)
Author(s):
WANG Haiying1, LI Zitong1, ZHANG Yingzhi2, WANG Chenguang1
(1. School of Construction Machinery, Chang'an University, Xi'an 710064, Shaanxi, China; 2. Shaanxi Transportation Holding Group Co., Ltd., Xi'an 710075, Shaanxi, China)
关键词:
混凝土;  预测模型;  BP神经网络;  抗压强度;  拌和生产监控数据
Keywords:
concrete;  prediction model;  BP neural network;  compressive strength;  production monitoring data
分类号:
U415.12
DOI:
10.19815/j.jace.2023.12082
文献标志码:
A
摘要:
为解决混凝土生产中抗压强度试验周期长及工程管理存在滞后性的问题,提出了一种基于混凝土拌和生产实时监控数据的BP神经网络混凝土抗压强度预测模型。以混凝土拌和生产中的8项物料生产称重数据和5项生产配比数据作为预测输入变量,建立200组混凝土拌和站生产监控数据和对应的抗压强度试验数据样本集,按照6:2:2比例划分为训练集、验证集和测试集; 分别以C40配比混凝土拌和生产的8项物料称重数据和全部13项数据作为输入变量,进行混凝土28 d抗压强度预测,将预测结果与实际试验结果进行比较,验证所提出BP神经网络模型的预测效果。结果表明:所提出的BP神经网络混凝土强度预测模型能较好地实时预测混凝土28 d抗压强度,且相对误差优于利用7 d抗压强度试验数据估算值; 8项物料称重数据作为输入变量的BP神经网络预测模型预测精度更好,平均绝对百分比误差为0.82%,均方根误差为0.52 MPa; 利用不同拌和站C20配比、C30配比混凝土拌和生产监控数据对8项输入变量BP神经网络混凝土抗压强度预测模型进行适应性验证可知,其预测平均绝对误差均在0.5 MPa之内,平均绝对百分比误差均小于2%,与C40配比预测误差一致; 该预测模型充分挖掘了混凝土拌和站生产实时监控数据的价值,实现了传统混凝土抗压试验结果提前化,对提高工程建设质量水平具有重要意义。
Abstract:
In order to solve the problems of long testing period and lagging of project management in concrete production, a BP neural network model for predicting concrete compressive strength based on real-time monitoring data of concrete mixing production was proposed. Taking the 8 items of material production weighing data and 5 items of production mixing ratio data in concrete mixing production as input variables, 200 sets of production monitoring data and test data sample sets were established, which were divided into training set, verification set, and test set according to the ratio of 6:2:2. The 8 items of material weighing data and all 13 items of data of C40 ratio concrete mixing and production were used as input variables for 28 d compressive strength prediction of concrete, and the predicted results were compared with the actual test results to verify the effectiveness of the proposed BP neural network prediction model. The results show that the proposed BP neural network concrete strength prediction model can effectively predict the 28 d compressive strength of concrete in real time, and the prediction relative error is better than the estimated value using 7 d compressive strength test data. The BP neural network prediction model with 8 items of material weighing data as input variables has better prediction accuracy, with a mean absolute percentage error of 0.82% and a root mean square error of 0.52 MPa. Furthermore, the adaptability of the BP neural network concrete compressive strength prediction model with 8 items of input variables is verified using production monitoring data of C20 and C30 ratios from different mixing stations, and the prediction mean absolute errors are within 0.5 MPa, and the average absolute percentage errors are less than 2%, consistent with the prediction errors of C40 ratio. The prediction model fully explores the value of real-time monitoring data of concrete mixing station production, realizes the advance of the traditional concrete compressive test results, and has important practical significance for improving the quality level of engineering construction.

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备注/Memo

备注/Memo:
收稿日期:2023-12-19
基金项目:陕西省交通运输厅科技项目(18-33X,21-04X)
作者简介:王海英(1971-),女,工学博士,副教授,E-mail:whying@chd.edu.cn。
更新日期/Last Update: 2024-05-20