An intelligent model to predict the mechanical properties of defected concrete drainage pipes

平均绝对百分比误差 粒子群优化 均方误差 遗传算法 支持向量机 体积热力学 管道(软件) 超参数优化 结构工程 算法 材料科学 工程类 计算机科学 数学 人工智能 统计 机器学习 机械工程 物理 量子力学
作者
Kangjian Yang,Hongyuan Fang,Hongjin Liu,Bin Li,Xijun Zhang,Yangyang Xia,Kejie Zhai
出处
期刊:International Journal of Mechanical Sciences [Elsevier]
卷期号:260: 108665-108665 被引量:5
标识
DOI:10.1016/j.ijmecsci.2023.108665
摘要

Corrosion and cracks are common issues in drainage pipelines. To investigate the mechanical properties of pipes with defects, a series of pipeline bearing capacity tests were carried out. In addition, a prediction model using a combination of self-organizing maps, genetic algorithms, and support vector machines (SOM-GA-SVM) was developed to predict the bearing capacity and circumferential strain of the pipeline. The prediction results obtained using this model were compared with those obtained using three other optimization algorithms. Furthermore, the influence of loading speed and defect volume on the prediction accuracy of the model was analyzed. The results indicated that the MAPE of the prediction results was less than 7%, the RMSE was less than 8, and the R2 was greater than 0.98; Additionally, the prediction accuracy of the SOM-GA algorithm was significantly higher than that of the genetic algorithm, particle swarm optimization algorithm, and grid search method; It was found that removing the loading speed from the variables and changing the defect depth and width to defect volume can improve the prediction accuracy of the model. After removing the loading speed, the average MAPE and average RMSE of the prediction model were reduced by 7.357% and 8.385%, respectively. After changing the defect depth and width to defect volume, the average MAPE and average RMSE of the prediction model were reduced by 4.925% and 5.054%, respectively.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
无题的海发布了新的文献求助20
刚刚
刚刚
JFP完成签到,获得积分10
刚刚
刚刚
刚刚
完美世界应助liu采纳,获得10
1秒前
Mzhao应助文静三颜采纳,获得10
1秒前
英勇的薯片应助Juice采纳,获得10
1秒前
2秒前
2秒前
3秒前
3秒前
科研通AI2S应助李朔星采纳,获得10
3秒前
3秒前
mere发布了新的文献求助10
3秒前
ff发布了新的文献求助10
5秒前
1234发布了新的文献求助10
5秒前
代维健的大黑完成签到 ,获得积分10
5秒前
5秒前
feng完成签到,获得积分10
5秒前
田睿完成签到,获得积分10
5秒前
猫尾巴完成签到,获得积分10
6秒前
马爱林完成签到,获得积分10
7秒前
vv的平行宇宙完成签到,获得积分10
7秒前
7秒前
瑞仔发布了新的文献求助10
7秒前
8秒前
Doctor完成签到 ,获得积分10
8秒前
8秒前
纯真以晴完成签到,获得积分10
8秒前
瓦罐汤完成签到 ,获得积分10
8秒前
早睡完成签到,获得积分10
9秒前
123完成签到,获得积分10
9秒前
阳光芫完成签到,获得积分10
10秒前
半糖去冰小丫丫完成签到,获得积分20
10秒前
所所应助一文字豪树采纳,获得10
11秒前
田様应助1234采纳,获得30
12秒前
12秒前
liushan发布了新的文献求助10
12秒前
落寞臻完成签到,获得积分20
13秒前
高分求助中
Sustainability in ’Tides Chemistry 2000
Sustainability in ’Tides Chemistry 1500
The ACS Guide to Scholarly Communication 1000
TM 5-855-1(Fundamentals of protective design for conventional weapons) 1000
Ethnicities: Media, Health, and Coping 800
Treatise on Geomorphology(2nd Edition - March 1, 2022) 520
Gerard de Lairesse : an artist between stage and studio 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3070075
求助须知:如何正确求助?哪些是违规求助? 2724068
关于积分的说明 7483773
捐赠科研通 2371206
什么是DOI,文献DOI怎么找? 1257323
科研通“疑难数据库(出版商)”最低求助积分说明 609889
版权声明 596879