JOURNAL ARTICLE
Investigation on the Effect of Initial Damage on the Time‐Dependent Deformation of Rock Materials Under Step Loading.
Published In: Fatigue & Fracture of Engineering Materials & Structures, 2025, v. 48, n. 4. P. 1819 1 of 3
Database: Academic Search Ultimate 2 of 3
Authored By: Zhu, Ling; Li, Tiantao; Pei, Xiangjun; Xue, Peng; Liang, Yufei 3 of 3
Abstract
Investigating the creep properties of damaged rocks is essential for evaluating the long‐term stability of seismically cracked slopes in earthquake‐prone regions. In this study, cyclic loading‐unloading and step creep loading tests were sequentially performed on metamorphic sandstone, granite, and phyllite. Dissipated energy was introduced to establish the initial damage model, and the effect patterns and mechanisms of initial damage on creep properties were analyzed. The experimental results showed that dissipated energy and the damage variable increased linearly with the number of loading‐unloading cycles. The increase in initial damage results in greater creep strain, higher steady‐state creep rate, and increased dissipated energy under the same creep loading, while reducing the long‐term strength of the rock. Prior loading‐unloading promoted the development of microcracks and accelerated the time‐dependent deformation of the rock. This study provides a new understanding of the long‐term stability of seismically cracked slopes in strong‐earthquake mountainous areas. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Fatigue & Fracture of Engineering Materials & Structures. 2025/04, Vol. 48, Issue 4, p1819
- Document Type:Article
- Subject Area:Geology
- Publication Date:2025
- ISSN:8756-758X
- DOI:10.1111/ffe.14586
- Accession Number:184014236
- Copyright Statement:Copyright of Fatigue & Fracture of Engineering Materials & Structures is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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