Predicting post-fire mechanical properties of grade 8.8 and 10.9 steel bolts

被引:44
作者
Ketabdari, Hesamoddin [1 ]
Daryan, Amir Saedi [1 ]
Hassani, Nemat [1 ]
机构
[1] Shahid Beheshti Univ, Fac Civil Water & Environm Engn, Tehran, Iran
关键词
Steel bolts; Mechanical properties; Post-fire; Gene expression programming; Practical equation; HIGH-STRENGTH BOLTS; ANGLE CONNECTIONS; STRUCTURAL-STEEL; SHEAR-STRENGTH; FIRE; BEHAVIOR; TEMPERATURE; CONCRETE; COLUMNS;
D O I
10.1016/j.jcsr.2019.105735
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Structural fire safety is one of the primary considerations in the design of high-rise buildings where steel is often a popular material for structural members selection. Therefore, predicting post-fire mechanical properties of steel bolts as a crucial element in steel structures is highly valuable. In this paper, the behavior of the High-Strength Steel Bolts (HSSB), after exposing to fire, is investigated and practical equations for the mechanical properties, including the ultimate strength, the yield strength, and the modulus of elasticity, are proposed as well. Accordingly, Grade 8.8 and Grade 10.9 steel bolts are employed in a variety of sizes, from M6 to M24, experiencing six different target temperatures. After natural cooling, a tensile test is applied to all the bolts, the corresponding stress-strain curves are derived, and finally all required data for each specimen are obtained by means of these curves. Results from these curves indicate that at 400 degrees C or fewer temperatures, more than 80% of the mechanical properties are recovered. Between 400 degrees C and 500 degrees C, the features began to reduce, however, above 500 degrees C, a sudden drop was noticeable. Besides, by using both the obtained data and the Gene Expression Programming (GEP) as a branch of Genetic Algorithm, equations for mechanical properties of HSSB are derived. The validation results indicate that the relative error of GEP-based models is less than 10%. All in all, the minimum error in the GEP-based models demonstrates favorable equations for post-fire mechanical properties of HSSB and also better predictions than the traditional models. (C) 2019 Elsevier Ltd. All rights reserved.
引用
收藏
页数:10
相关论文
共 32 条
[1]  
Al-Jabri K.S., 1998, J CONSTR STEEL RES, V46
[2]  
Al-Jabri KS, 2007, INT J STEEL STRUCT, V7, P209
[3]  
[Anonymous], THESIS
[4]  
[Anonymous], 1986, 9311M1 D DIN
[5]  
ASTM American Society for Testing and Materials, 2009, STAND TEST METH TENS
[6]   Estimating Shear Strength of Short Rectangular Reinforced Concrete Columns Using Nonlinear Regression and Gene Expression Programming [J].
Aval, S. B. Beheshti ;
Ketabdari, H. ;
Gharebaghi, S. Asil .
STRUCTURES, 2017, 12 :13-23
[7]   Fire resistance of concrete: prediction using artificial neural networks [J].
Chan, YN ;
Jin, P ;
Anson, M ;
Wang, JS .
MAGAZINE OF CONCRETE RESEARCH, 1998, 50 (04) :353-358
[8]   Mechanical Properties of Steel Bolts with Different Diameters after Exposure to High Temperatures [J].
Daryan, Amir Saedi ;
Ketabdari, Hesam .
JOURNAL OF MATERIALS IN CIVIL ENGINEERING, 2019, 31 (10)
[9]   Predicting the behavior of welded angle connections in fire using artificial neural network [J].
Daryan, Amir Saedi ;
Yahyai, Mahmood .
JOURNAL OF STRUCTURAL FIRE ENGINEERING, 2018, 9 (01) :28-52
[10]   Behavior of Khorjini connections in fire [J].
Daryan, Amir Saedi ;
Bahrampoor, Hesam .
FIRE SAFETY JOURNAL, 2009, 44 (04) :659-664