This paper presents a numerical simulation to assess lateral-torsional buckling (LTB) behavior of steel H-beams exposed to localized fire. The nonlinear buckling analysis was conducted using a finite element program, ABAQUS, to assess LTB of the beams. The steel beams were subjected to pure bending and localized fire. The location of the localized fire was applied below at a one-eighth point, a quarter-point and a half of the beam from the support. The combined effects of initial geometric imperfection and residual stress were also considered. The buckling strength of the beam under localized fire and ISO-834 standard fire were generated and compared. The results showed that the LTB strength degradation of the beam under localized fire was slower than that in ISO-834 standard fire. The fire source location and the length-to-height ratio of the beam have a significant effect on the LTB strength of the beam. The LTB capacity of beams using Eurocode 3 with the uniform heating assumption can give over-conservative results when the real fire scenario is localized fire. The buckling strength of steel beams using Eurocode 3 can give unconservative results when the fire scenario is ISO-834 standard fire.

In this article, experimental studies on the flexural behavior of concrete beams with corroded reinforcement were conducted. The beams were subjected to simultaneous different load levels and immersed in NaCl solution for 30 days with accelerated corrosion. A four-point bending test was subsequently carried out to determine the loading capacity of these beams. Experimental data show that the loading capacity of the beam decreases as the level of preload increases. The mass loss of steel bars in the beams increases as the level of preload increases. The loading capacity and steel mass loss of the beams subjected to 0.8 of failure load of the non-corroded beam decrease by 32.7 and 30.3%, respectively. The numerical investigation was also done to evaluate the loading capacity with various corrosion degrees of steel bars.

Metaheuristic algorithms have been applied to tackle challenging optimization problems in various domains, such as health, education, manufacturing, and biology. In particular, the field of Structural Health Monitoring (SHM) has received significant interest, particularly in the area of damage identification in structures. Popular optimization algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Teaching Learning Based Optimization (TLBO), Artificial Hummingbird Algorithm (AHA), Moth Flame Optimizer (MFO), among others, have been employed to address this problem. However, notwithstanding the wide recognition of the current algorithms, their constraints are commonly acknowledged. Hence, this article advocates for the adoption of innovative hunting-inspired algorithms, namely the Ant Lion Optimizer (ALO), African Vulture Optimization Algorithm (AVOA), Grey Wolf Optimizer (GWO), Marine Predator Algorithm (MPA), and Whale Optimization Algorithm (WOA), which emulate the behaviors of wildlife species, to discern the areas and magnitudes of deterioration in a suspension footbridge. Moreover, in order to reduce computational time, only natural frequencies are applied as objective functions. The obtained results indicate that all the utilized algorithms can accurately detect the damages in the considered structure.

This paper presents an enhanced version of the Artificial Rabbit Optimization (ARO) algorithm designed for identifying structural damage in bridge structures. The original ARO draws inspiration from survival observed in wild rabbits. However, it demands a substantial investment of computational time. Therefore, in this paper, the Improved ARO (IARO) algorithm incorporating elements of the Grey Wolf Optimizer (GWO) through hybridization, is employed to deal with optimization problems. The central concept of this approach involves infusing predator-hunting characteristics into the prey-rabbit during the hunting process, thereby enabling more effective predator evasion. The proposed method is evaluated through a series of simulations related to two real bridges: a simple supported beam structure and a steel truss bridge. The results show a significant improvement in accuracy and efficiency in determining structural damage while considering factors such as damage location, severity, and computation time. These findings underscore the potential of the proposed approach for real-world applications in structural health monitoring and damage detection.