Fatigue Life Analysis of Outdoor Large-Scale Test Bench Based on S-N Curve Fitting Using Crack Propagation Life

In engineering, due to the requirements of specific test tasks, the load-bearing structures of outdoor large-scale test benches face harsh environmental conditions during operation. These structures endure significant static loads and external environmental loads before operation, as well as substantial alternating and cyclic loads during testing, leading to cumulative fatigue damage within the metal. Even if the load values are below the static strength limit, failures such as fracture and yielding may occur. Considering the non-negligible impact of accumulated internal damage on the lifespan of load-bearing structures, this study employs the rain-flow counting method to identify fatigue cyclic loads and cycle counts at critical points during a single task. Corrections are made based on material and test characteristics, while accounting for the crack propagation life at welded joints in the load-bearing structure. Additionally, due to the complexity of weld defects in engineering, obtaining S-N curves for such defects is often challenging. This paper fits the S-N curve using acquired load information and corresponding crack propagation life. Finally, fatigue life prediction is conducted using the fatigue cumulative damage theory based on the fitted S-N curve. A novel fatigue life prediction method for outdoor large-scale test bench load-bearing structures is proposed, integrating crack propagation life-based S-N curve fitting and fatigue damage theory. The fitted life curve can also be applied to subsequent test tasks for life assessment or prediction.

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Published
2025-08-03
DOI
https://doi.org/10.1109/icma65362.2025.11120673
Primary Topic
Smart Materials for Construction
Type
article
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Fatigue Life Analysis of Outdoor Large-Scale Test Bench Based on S-N Curve Fitting Using Crack Propagation Life

Xuelong Zhang, Peng Zhang, Lingtao Yu, Xue Yang et al.
Smart Materials for Construction
article

Fatigue Life Analysis of Outdoor Large-Scale Test Bench Based on S-N Curve Fitting Using Crack Propagation Life

Xuelong Zhang, Peng Zhang, Lingtao Yu, Xue Yang, Xinming Tang
article en

Abstract

In engineering, due to the requirements of specific test tasks, the load-bearing structures of outdoor large-scale test benches face harsh environmental conditions during operation. These structures endure significant static loads and external environmental loads before operation, as well as substantial alternating and cyclic loads during testing, leading to cumulative fatigue damage within the metal. Even if the load values are below the static strength limit, failures such as fracture and yielding may occur. Considering the non-negligible impact of accumulated internal damage on the lifespan of load-bearing structures, this study employs the rain-flow counting method to identify fatigue cyclic loads and cycle counts at critical points during a single task. Corrections are made based on material and test characteristics, while accounting for the crack propagation life at welded joints in the load-bearing structure. Additionally, due to the complexity of weld defects in engineering, obtaining S-N curves for such defects is often challenging. This paper fits the S-N curve using acquired load information and corresponding crack propagation life. Finally, fatigue life prediction is conducted using the fatigue cumulative damage theory based on the fitted S-N curve. A novel fatigue life prediction method for outdoor large-scale test bench load-bearing structures is proposed, integrating crack propagation life-based S-N curve fitting and fatigue damage theory. The fitted life curve can also be applied to subsequent test tasks for life assessment or prediction.

Harbin University (CN), Harbin Engineering University (CN)
Responsible consumption and production
Openalex Percentile: Top 84%
Smart Materials for Construction
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Fatigue Life Analysis of Outdoor Large-Scale Test Bench Based on S-N Curve Fitting Using Crack Propagation Life — Xuelong Zhang, Peng Zhang, et al. · (2025) | TGRS Research Map | TGRS