The construction industry is constantly evolving and improving leading to an era where the complexity of the structure is tremendously increasing. As a result, the effectiveness of the design and planning phase is critical in determining the success of a construction project. Due to the vast amount of design components, traditional methods have a difficult time producing cost-effective designs of reinforced concrete structures. Furthermore, the design must conform to all of the designated code provision requirements, which have a large number of constraints. Thus, this study focuses on the development of the design optimization framework to assist structural engineers in conducting better design and planning phases. Two key components in the proposed framework are the Symbiotic Organism Search (SOS) algorithm and structural analysis software. In the present study, ETABS software, as the most commonly used structural analysis software for designing reinforced concrete, is utilized. An API is developed to maintain the connection between the SOS and ETABS software. The SOS algorithm is tested by comparing with other robust metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Charged System Search (CSS) in the same framework. The experiment shows that SOS algorithm is superior to all the other three algorithms, in terms of optimality and consistency. Furthermore, the performance of the proposed framework is presented in two real-world study cases. The analysis shows that the proposed framework, although achieves a slightly worst result than the original design, can ensure feasibility by satisfying all the constraints imposed by design codes.

The construction industry is constantly evolving and improving leading to an era where the complexity of the structure is tremendously increasing. As a result, the effectiveness of the design and planning phase is critical in determining the success of a construction project. Due to the vast amount of design components, traditional methods have a difficult time producing cost-effective designs of reinforced concrete structures. Furthermore, the design must conform to all of the designated code provision requirements, which have a large number of constraints. Thus, this study focuses on the development of the design optimization framework to assist structural engineers in conducting better design and planning phases. Two key components in the proposed framework are the Symbiotic Organism Search (SOS) algorithm and structural analysis software. In the present study, ETABS software, as the most commonly used structural analysis software for designing reinforced concrete, is utilized. An API is developed to maintain the connection between the SOS and ETABS software. The SOS algorithm is tested by comparing with other robust metaheuristic algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Charged System Search (CSS) in the same framework. The experiment shows that SOS algorithm is superior to all the other three algorithms, in terms of optimality and consistency. Furthermore, the performance of the proposed framework is presented in two real-world study cases. The analysis shows that the proposed framework, although achieves a slightly worst result than the original design, can ensure feasibility by satisfying all the constraints imposed by design codes.

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