Predicting crack growth in viscoelastic bitumen under a rotational shear fatigue load

材料科学 粘弹性 沥青 动态剪切流变仪 复合材料 开裂 剪切(地质) 裂纹扩展阻力曲线 裂缝闭合 流变仪 裂纹尖端张开位移 巴黎法 断裂力学 结构工程 流变学 车辙 工程类
作者
Yuqing Zhang,Yangming Gao
出处
期刊:Road Materials and Pavement Design [Informa]
卷期号:22 (3): 603-622 被引量:55
标识
DOI:10.1080/14680629.2019.1635516
摘要

This study develops a damage mechanics-based crack growth model to predict crack length in a typical viscoelastic material (i.e. bitumen) under a rotational shear fatigue load. This crack growth model was derived using torque and dissipated strain energy equilibrium principles. The crack length was predicted using bitumen’s shear moduli and phase angles in the undamaged and damaged conditions, measured by linear amplitude sweep (LAS) tests and time sweep (TS) tests, respectively. The two tests were both performed using Dynamic Shear Rheometer (DSR), thus the crack growth model was named as a DSR-C model. To validate the DSR-C model, the crack lengths after the TS tests were measured using digital visualisation of cracking surfaces for one virgin bitumen and one polymer-modified bitumen at two temperatures (15, 20°C), two frequencies (10, 20 Hz) and two strain levels (5%, 7%) under unaged and aged conditions. Results show that the DSR-C model can accurately predict the crack length in the viscoelastic bitumen under the rotational shear fatigue load at different loading and material conditions. The crack growth includes initial transition period, steady growth period and rapid growth period under a controlled strain loading mode. The degradation of the material property results from the crack growth that initiates from the outer edge toward the centre of the sample under the rotational shear load.
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
科研通AI5应助文艺水蜜桃采纳,获得10
刚刚
刚刚
刚刚
科研通AI5应助BILNQPL采纳,获得10
1秒前
流白完成签到,获得积分10
1秒前
1秒前
Yolo完成签到,获得积分20
1秒前
YY应助胖豆采纳,获得10
2秒前
2秒前
jagger发布了新的文献求助10
2秒前
2秒前
3秒前
ChemistryZyh完成签到,获得积分10
3秒前
3秒前
3秒前
3秒前
充电宝应助朴素的士晋采纳,获得10
4秒前
4秒前
6秒前
调研昵称发布了新的文献求助10
6秒前
6秒前
6秒前
十万大山兵大大给十万大山兵大大的求助进行了留言
6秒前
6秒前
CodeCraft应助Mumu采纳,获得10
7秒前
飘逸数据线完成签到,获得积分10
7秒前
111发布了新的文献求助10
7秒前
Gauss完成签到,获得积分0
7秒前
丘奇完成签到,获得积分10
7秒前
木子发布了新的文献求助10
7秒前
标致的方盒完成签到,获得积分10
7秒前
8秒前
致橡树完成签到,获得积分20
8秒前
Yolo发布了新的文献求助10
8秒前
yyy完成签到,获得积分20
9秒前
9秒前
9秒前
yoon发布了新的文献求助10
9秒前
脑洞疼应助香蕉静芙采纳,获得10
9秒前
JTB完成签到,获得积分10
9秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527723
求助须知:如何正确求助?哪些是违规求助? 3107826
关于积分的说明 9286663
捐赠科研通 2805577
什么是DOI,文献DOI怎么找? 1539998
邀请新用户注册赠送积分活动 716878
科研通“疑难数据库(出版商)”最低求助积分说明 709762