INTERNATIONAL JOURNAL FOR INNOVATIVE RESEARCH IN MULTIDISCIPLINARY FIELD

:Despite being one of the oldest and most conventional engineering specialties, civil engineering is not an exception to the technological advancements in all other engineering fields. Similar to other engineering specialties, this one also requires and benefits from efficiency gains. The concept of combining cutting-edge AI and machine learning techniques with civil engineering to directly influence the sector is not new. For a considerable amount of time, people have been incorporating technology into the building industry, with remarkable outcomes. Using software can be a solid strategy on the lines of development in the hardware industry, such as civil. The optimization problem is one of these strategies where the effects may be seen. Construction requires excellent performance in a limited amount of time more than any other business. The technique by which one's performance requirements can be satisfied in a short period of time is called optimization. Time is used more while utilizing a traditional method to maximize the performance of any criterion, which is where technology enters the picture. Despite having the potential to be a distinct profession, optimization encompasses a variety of strategies and tactics to make tasks simpler and less laborious. Genetic algorithms (GA), particle swarm optimization (PSO), simulated annealing (SA), artificial bee colonies, linear and nonlinear methodologies, and others are some of these methods. A comparative analysis of optimization methods for a steel truss utilizing MATLAB's Simulated Annealing, Particle Swarm Optimization, and Genetic Algorithm will be covered in this article. This is accomplished by creating an algorithm that specifies the steel truss's properties, applying constraints such as displacement and stress constraints to appropriately penalize optimized values, adding various parameters based on the method employed, and comparing the outcomes of the procedures.