|本期目录/Table of Contents|

[1]周金枝,吴 学,钟楚珩,等.基于改进MGM(1,1)模型的再生混凝土疲劳寿命预测[J].建筑科学与工程学报,2024,41(02):31-38.[doi:10.19815/j.jace.2022.08052]
　ZHOU Jinzhi,WU Xue,ZHONG Chuheng,et al.Fatigue life prediction of recycled concrete based on improved MGM(1,1)model[J].Journal of Architecture and Civil Engineering,2024,41(02):31-38.[doi:10.19815/j.jace.2022.08052]
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基于改进MGM(1,1)模型的再生混凝土疲劳寿命预测(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02期
页码:
31-38
栏目:
建筑材料
出版日期:
2024-03-30

文章信息/Info

Title:
Fatigue life prediction of recycled concrete based on improved MGM(1,1)model
文章编号:
1673-2049(2024)02-0031-08
作者:
周金枝1,2,吴 学1,3,钟楚珩1,石赐明1,施佳楠1
(1. 湖北工业大学 土木建筑与环境学院,湖北 武汉 430068; 2. 桥梁结构健康与安全国家重点实验室,湖北 武汉 430034; 3. 中建三局集团(深圳)有限公司,广东 深圳 518000)
Author(s):
ZHOU Jinzhi1,2, WU Xue1,3, ZHONG Chuheng1, SHI Ciming1, SHI Jianan1
(1. College of Civil Architecture and Environment, Hubei University of Technology, Wuhan 430068, Hubei, China; 2. State Key Laboratory of Bridge Structure Health and Safety, Hubei University of Technology, Wuhan 430034, Hubei, China; 3. China Construction Third Engineering Bureau Group(Shenzhen)Co., Ltd, Shenzhen 518000, Guangdong, China)
关键词:
再生混凝土;  灰色理论;  马尔科夫模型;  新陈代谢理论;  粒子群算法;  疲劳寿命
Keywords:
recycled concrete;  grey theory;  Markov model;  metabolic theory;  particle swarm algorithm;  fatigue life
分类号:
TU528
DOI:
10.19815/j.jace.2022.08052
文献标志码:
A
摘要:
准确预测再生混凝土(RC)疲劳寿命对其应用于路面及桥梁等项目具有重要意义。在灰色马尔科夫模型(MGM)的基础上,引入新陈代谢理论不断更新原始数列中的疲劳寿命数据,并结合粒子群算法优化状态区间的取值,提高其预测精度以适应混凝土疲劳寿命预测,再以不同应力水平的RC疲劳寿命试验结果作为原始数据,建立了基于改进的灰色马尔科夫模型的RC疲劳寿命预测模型,并对改进前后模型的精度以及预测结果进行对比分析。结果表明:RC疲劳寿命N服从两参数威布尔分布; 将预测值从lg(N)转化为疲劳寿命后进行误差分析可知,应力-疲劳寿命(S-N)曲线的预测精度较低,最大相对误差达到201.43%; MGM(1,1)模型相比于S-N曲线预测精度有所提升,但平均相对误差仍达到102.20%; 经两种算法理论改进后的MGM(1,1)模型预测精度有较大的提高,平均相对误差仅为5.62%; 引入其他文献试验数据进行验证并与原文献中模型进行对比分析发现,改进MGM(1,1)模型的误差波动幅度小且平均误差小于原文献中模型,平均相对误差仅为1.01%,说明改进的MGM(1,1)模型在对RC疲劳寿命预测上具有更高的精确度与可靠性。
Abstract:
Accurate prediction of the fatigue life of recycled concrete(RC)is important for the application of RC in pavements and bridges and other projects. Based on the grey Markov model(MGM), the fatigue life data in the original series were continuously updated by using the metabolic theory, and combined with the particle swarm algorithm to optimize the value of the state interval to improve its prediction accuracy for concrete fatigue life prediction. Then the fatigue life test results of RC with different stress levels were used as the original data to establish the fatigue life prediction model of RC based on the improved gray Markov model, and the accuracy of the model and the prediction results before and after improvement were compared and analyzed. The results show that the RC fatigue life N obeys the two-parameter Weibull distribution. The error analysis after converting the predicted values from lg(N)to fatigue life shows that the prediction accuracy of the stress-fatigue life (S-N) curve is low, with maximum relative error of 201.43%. The prediction accuracy of the MGM(1,1)model is improved compared with that of the S-N curve, but the average relative error still reaches 102.20%. The prediction accuracy of the MGM(1,1)model improved by the theoretical improvement of both algorithms has improved considerably, and the average relative error is only 5.62%. The improved MGM(1,1)model is found to have smaller error fluctuations and smaller mean errors than the model in the literature when the experimental data from other literature are introduced for validation and comparative analysis with the model in the literature, and the average relative error is only 1.01%, indicating that the improved MGM(1,1)model has higher accuracy and reliability in predicting the fatigue life of RC.

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备注/Memo

备注/Memo:
收稿日期:2023-08-16
基金项目:桥梁结构健康与安全国家重点实验室开放课题(BHSKL19-04-KF); 湖北省教研项目(2017314); 湖北工业大学博士启动基金项目(BSQD2020051)
作者简介:周金枝(1964-),女,工学博士,教授,博士生导师,E-mail:hgzhougzhou@126.com。
通信作者:钟楚珩(1989-),男,工学博士,讲师,E-mail:chuheng.zhong@hbut.edu.cn。
更新日期/Last Update: 2024-03-25