Numerical Analysis of the Effect of Thread Geometry on the Seismic Behavior of Bolted Connections under Near-Fault Earthquakes

Authors

https://doi.org/10.48314/ijrceai.v2i3.54

Abstract

Bolted connections are critical components in steel structures, and their performance under near-fault earthquake loading is of great importance. This study investigates the effect of thread geometry on the mechanical behavior of high-strength bolts subjected to dynamic seismic loads. Finite Element Analyses (FEA) were performed in ABAQUS under nonlinear conditions using three near-fault earthquake records: 1) Northridge, 2) Loma Prieta, and 3) Imperial Valley. Three thread geometries—triangular (Metric), square, and trapezoidal (ACME)—were examined for bolt diameters of 5 mm, 30 mm, and 56 mm. The results indicate that for small-diameter bolts (5 mm), triangular threads produced the lowest average von Mises stress of 365 MPa and showed approximately 18% greater stress uniformity than other thread types. For medium-diameter bolts (30 mm), square threads exhibited the best dynamic performance by reducing stress concentration by 22% and increasing pressure uniformity by up to 29% compared to triangular threads. In large-diameter bolts (56 mm), trapezoidal (ACME) threads provided superior seismic resistance, with an average von Mises stress of 480 MPa and a 31% reduction in stress fluctuation. Overall, the findings suggest that using square threads for medium-diameter bolts and trapezoidal threads for large bolts can effectively reduce stress concentration, enhance fatigue life, and improve seismic performance under near-fault earthquakes.

Keywords:

Bolted connections, Thread geometry, High-strength bolts, Near-fault earthquakes, Finite element analysis, von Mises stress, Seismic performance

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Published

2025-09-25

How to Cite

Zakizadeh, H. (2025). Numerical Analysis of the Effect of Thread Geometry on the Seismic Behavior of Bolted Connections under Near-Fault Earthquakes. International Journal of Researches on Civil Engineering With Artificial Intelligence , 2(3), 157-181. https://doi.org/10.48314/ijrceai.v2i3.54

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