Non-Cantilever Fatigue Remaining-Life Simulation Software Using Probabilistic Wind Model for All Counties in Kansas

Al Shboul, Khalid W; Rasheed, Hayder A. · 2025 · ROSA P / Kansas. Dept. of Transportation. Bureau of Research

archive: archived pipeline: cataloged verified

Get this paper ↗ (full text — opens at the source; we link to it, we don't host it)

Summary

This study addresses the critical need for accurate fatigue life prediction in full-span overhead highway sign support structures, which are susceptible to failure from sustained wind fluctuations. Traditional inspection methods are often costly and may miss unnoticed fatigue cracking, posing safety risks. The research aimed to develop a comprehensive, computationally affordable simulation tool capable of predicting the remaining fatigue life of non-cantilever sign structures across all counties in Kansas. The motivation was to replace deterministic wind models with a probabilistic approach that accounts for long-term, irregular wind loading scenarios, thereby improving the reliability of structural integrity assessments. The methodology involved creating synthetic wind time-histories using the Kaimal spectrum, superimposing cosine waves across frequencies of 3–300 Hz with random phase angles. This generated a database of wind histories for each daily mean wind speed in Kansas over a 45-year period (1975–2019). The study utilized finite element modeling via Staad Pro, automated through custom C# software, to simulate axial truss members made of 6061 aluminum alloy. Static stress analyses were performed for various wind speeds, and stresses were amplified using an average dynamic amplification factor (DAF) to account for wind’s dynamic nature. The rainflow counting technique extracted cycle counts from the synthetic wind data, and Miner’s rule, combined with experimental S-N curves for welded aluminum, calculated cumulative fatigue damage. The software was validated using a four-chord box truss structure in Wichita, Kansas, modeled with 161 members. The simulation predicted that two specific members (258 and 259) had reached the end of their fatigue lives, indicated by a damage index exceeding unity. Subsequent physical inspections confirmed the presence of severe, previously unnoticed fatigue cracks in these exact members, while adjacent members showed no distress. A comparison with an older deterministic 1-Hz model revealed that the new probabilistic model produced lower cycle counts but higher pseudo-stress due to DAF application, resulting in more nuanced damage indices. The new model demonstrated that while total damage trends remained similar to older methods, the probabilistic approach provided a more realistic representation of wind-induced fatigue cycles. The significance of this work lies in the development of a user-friendly, automated software package that enables transportation agencies to efficiently evaluate the fatigue status of sign structures without exhaustive manual calculations. By integrating spatial wind interpolation and probabilistic wind modeling, the tool allows for future projections of fatigue life based on historical data. The successful validation against field inspections confirms the tool’s accuracy in identifying critical structural members prone to failure. This approach offers a cost-effective solution for maintaining highway infrastructure safety, allowing for targeted inspections and timely repairs before catastrophic failures occur.

Key finding

The developed simulation software accurately predicted the end-of-fatigue-life for specific structural members, which was confirmed by field inspections revealing severe fatigue cracks in those locations.

Methodology

modeling

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed. Discovered via bulk_ingest_rosap on 2026-05-23 (6 acquisition events logged).

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• drowsiness detection algorithms