亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Combination of active sensing method and data-driven approach for rubber aging detection

天然橡胶 刚度 剪切(地质) 时域 计算机科学 人工智能 深度学习 频域 材料科学 结构工程 模式识别(心理学) 复合材料 工程类 计算机视觉
作者
Yi Zeng,Tengsheng Chen,Feng Xiong,Kailai Deng,Yuanqing Xu
出处
期刊:Structural Health Monitoring-an International Journal [SAGE Publishing]
卷期号:23 (4): 2310-2322 被引量:3
标识
DOI:10.1177/14759217231207002
摘要

Rubber bearings are key components of base-isolated structures, and the monitoring of their damage states is an important task. Aging is a primary concern affecting the service life and isolation effect of rubber bearings. Therefore, this study combined an active sensing method and a data-driven approach to detect rubber aging. A shear stiffness, accelerated aging, and active sensing experiments were conducted on a scaled rubber specimen. As the aging level increased, the shear stiffness of the specimens gradually increased from 116.69 to 127.82 N/mm, but this change was not linear. Due to variations in the degree of aging, discrepancies may arise in the time and frequency domain characteristics of detection signals. However, establishing an empirical relationship between the degree of aging and the features of detection signals were highly challenging. A deep-learning-based data-driven method was used to predict the aging level and shear stiffness using detection signals. The deep learning model successfully detected the aging level, and the prediction accuracy on the validation and test sets reached 99.98%. For the deep learning model for aging level prediction, the optimal input vector length is 4096, the recommended number of layers is 3–5, and the recommended number of cells in each layer is 256–2048. Moreover, the deep learning model also detected the shear stiffness of the rubber specimen. The mean absolute error was 0.27 N/mm on the validation set and 0.28 N/mm on the test set. For the deep learning model for shear stiffness prediction, the optimal input vector length is 4096, and the optimal structure is seven layers with 2048 cells in each layer.
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
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
阿巴阿巴完成签到,获得积分10
刚刚
gtgyh完成签到 ,获得积分10
1秒前
王七七发布了新的文献求助10
1秒前
aaaaarfv完成签到,获得积分10
9秒前
11秒前
AlwaysKim完成签到,获得积分20
12秒前
CipherSage应助cyh采纳,获得10
13秒前
孙靓靓靓发布了新的文献求助10
17秒前
18秒前
个性归尘完成签到,获得积分10
24秒前
zhiwei完成签到 ,获得积分10
25秒前
垚祎完成签到 ,获得积分10
27秒前
27秒前
孙靓靓靓完成签到,获得积分10
30秒前
匆匆完成签到 ,获得积分10
31秒前
34秒前
cyh发布了新的文献求助10
38秒前
39秒前
Joie发布了新的文献求助10
44秒前
发C刊的人完成签到 ,获得积分10
48秒前
慕青应助cyh采纳,获得10
52秒前
Joie完成签到,获得积分10
52秒前
Chao发布了新的文献求助10
54秒前
胡图图啦啦完成签到 ,获得积分10
57秒前
adi完成签到,获得积分10
1分钟前
往事吴痕完成签到 ,获得积分10
1分钟前
1分钟前
1分钟前
seol发布了新的文献求助10
1分钟前
1分钟前
seol完成签到,获得积分10
1分钟前
陶醉的钢笔完成签到 ,获得积分10
1分钟前
1分钟前
思源应助鸢翔flybird采纳,获得10
1分钟前
又活了一天完成签到 ,获得积分10
1分钟前
俊逸沛菡完成签到 ,获得积分10
1分钟前
星辰大海应助AlwaysKim采纳,获得10
1分钟前
典雅问寒应助科研通管家采纳,获得10
1分钟前
打打应助科研通管家采纳,获得10
1分钟前
NexusExplorer应助科研通管家采纳,获得10
1分钟前
高分求助中
Mass producing individuality 600
Algorithmic Mathematics in Machine Learning 500
Разработка метода ускоренного контроля качества электрохромных устройств 500
A Combined Chronic Toxicity and Carcinogenicity Study of ε-Polylysine in the Rat 400
Advances in Underwater Acoustics, Structural Acoustics, and Computational Methodologies 300
NK Cell Receptors: Advances in Cell Biology and Immunology by Colton Williams (Editor) 200
Effect of clapping movement with groove rhythm on executive function: focusing on audiomotor entrainment 200
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3827132
求助须知:如何正确求助?哪些是违规求助? 3369487
关于积分的说明 10456400
捐赠科研通 3089248
什么是DOI,文献DOI怎么找? 1699710
邀请新用户注册赠送积分活动 817497
科研通“疑难数据库(出版商)”最低求助积分说明 770251