Abstract
In the realm of daily life, ensuring the safety of building structures and civil engineering projects remains a paramount research focus. The creep properties of materials significantly influence their long-term loading process. Specifically, creep load and creep time are pivotal factors that impact material creep damage, thereby playing a crucial role in assessing the safety of engineering endeavors and estimating aspects such as housing construction. This study undertakes creep damage tests on cement-based materials, subjecting them to varying creep loads and creep times, and subsequently conducts uniaxial compression tests on the specimens post-creep damage. The refined Nishihara model is employed for data fitting, facilitating the construction of a creep damage time-stress model. Concurrently, a Neural Network model is utilized to validate the experimental data. The findings indicate that both steady-state creep strain and steady-state creep rate exhibit discernible trends relative to creep load and creep time, effectively mirroring the alterations in creep damage experienced by the specimens. The refined Nishihara model proves adept at predicting and equating creep damage under diverse creep loads and creep times. Similarly, the trained Neural Network model demonstrates capability in measuring and estimating various creep damages. The study successfully explored the correlation between creep time and creep load, enabling the simulation of long-term creep damage within a shorter creep time and facilitating an analysis of its physical and mechanical properties, which is pivotal in predicting the safety of large-scale engineering projects. Concurrently, it advances research on material damage equivalence, offering insights and theoretical groundwork for developing a system to assess material damage equivalence under various damage conditions.
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