Computational modeling of structures subjected to extreme static and dynamic loads (such as snow, wind, impact, and earthquake) using finite-element software are part of mainstream structural engineering curricula in universities (at least at graduate level), and many experts can be found in industry who routinely undertake such analyses. However, only a handful or institutions around the world teach structural response to fire (at any level) and only a few of the top consulting engineers in the world truly specialize in this niche area. Among the reasons for this are the lack of cheap and easily accessible software to carry out such analyses and the highly tedious nature of modeling the full (often coupled) sequence of a realistic fire scenario, heat transfer to structure and structural response (currently impossible using a single software). The authors in this paper describe how finite-element software can be extended to include the modeling of structures under fire load. The added advantage of...

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OpenSees Software Architecture for the Analysis of Structures in Fire

Mechanisms for improving tensile properties at elevated temperature in fire-resistant steel with Mo and Nb

Mechanical properties of conventional structural steel and fire-resistant steel at elevated temperatures

Performance of steel moment resisting frames with RBS connections under fire loading

In this study, we carried out nonlinear finite element simulations to predict the performance of a column-tree moment connection (CTMC) under fire and static loads. We also conducted a detailed parameter study based on five input variables, including the applied temperature, number of flange bolts, number of web bolts, length of the beam, and applied static loads. The first variable is changed among seven levels, whereas the other variables are changed among three levels. Employing the Taguchi method for variables 2–5 and their levels, 9 samples were designed for the parameter study, where each sample was exposed to 7 different temperatures yielding 63 outputs. The related variables for each output are imported for the training and testing of different surrogate models. These surrogate models include a multiple linear regression (MLR), multiple Ln equation regression (MLnER), an adaptive network-based fuzzy inference system (ANFIS), and gene expression programming (GEP). 44 samples were used for training randomly while the remaining samples were employed for testing. We show that GEP outperforms MLR, MLnER, and ANFIS. The results indicate that the rotation and deflection of the CTMC depend on the temperature. In addition, the fire resistance increases with a decrease in the beam length; thus, a shorter beam can increase the fire resistance of the building. The numbers of flanges and web bolts slightly affect the rotation and displacement of the CTMCs at temperatures of above 400°C.

An innovative model for predicting the displacement and rotation of column-tree moment connection under fire

Application of fire-resistant steel to beam-to-column moment connections at elevated temperatures

Numerical modeling of the fire–structure behavior of steel beam-to-column connections

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Application of fire-resistant steel to beam-to-column moment connections at elevated temperatures

Numerical modeling of the fire–structure behavior of steel beam-to-column connections