Journal of Shanghai Jiao Tong University ›› 2024, Vol. 58 ›› Issue (11): 1707-1715.doi: 10.16183/j.cnki.jsjtu.2022.526

• Naval Architecture, Ocean and Civil Engineering • Previous Articles     Next Articles

Experimental Study and Prediction Model of Low Temperature Mechanical Properties of High-Strength Steel

CAI Ao1,2, CHEN Mantai1,2(), ZUO Wenkang1,2, DUAN Liping1,2, ZHAO Jincheng1,2   

  1. 1. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
    2. Department of Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2022-12-19 Revised:2023-01-12 Accepted:2023-02-13 Online:2024-11-28 Published:2024-12-02

Abstract:

The application of high-strength steel in extremely cold polar regions can reduce steel consumption and save the cost of fabrication, transportation, and installation of steel structures in the harsh low-temperature environment. In order to study the mechanical properties of HG785 high-strength steel under polar low-temperature conditions, uniaxial tensile tests were conducted on high-strength steel coupons by considering two thicknesses and five low-temperature cases. It was found that the elastic modulus, yield strength, and ultimate tensile strength of HG785 high-strength steel in polar low-temperature environment are higher than those at an ambient temperature of 25 ℃. All tensile coupon specimens failed by traditional necking in a ductile manner without brittle failure tendency. Based on the test results, accurate prediction models for mechanical properties of HG785 high-strength steel in polar low-temperature environment were established by the best subset regression analysis. This will facilitate the application of high-strength steel in the design of structural members, joints, and systems in an efficient manner, and provide theoretical support for the promotion of high-strength steel structures in polar low-temperature regions.

Key words: high-strength steel, uniaxial tensile test, low temperature mechanical properties, prediction model

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