Optimal design of steel–concrete composite bridge based on a transfer function discrete swarm intelligence algorithm

Abstract

Bridge optimization can be complex because of the large number of variables involved in the problem. In this paper, two box-girder steel–concrete composite bridge single objective optimizations have been carried out considering cost and CO$$_{2}$$ 2 emissions as objective functions. Taking CO$$_{2}$$ 2 emissions as an objective function allows to add sustainable criteria to compare the results with cost. SAMO2, SCA, and Jaya metaheuristics have been applied to reach this goal. Transfer functions have been implemented to fit SCA and Jaya to the discontinuous nature of the bridge optimization problem. Furthermore, a Design of Experiments has been carried out to tune the algorithm to set its parameters. Consequently, it has been observed that SCA shows similar values for objective cost function as SAMO2 but improves computational time by 18% while also getting lower values for the objective function result deviation. From a cost and CO$$_{2}$$ 2 optimization analysis, it has been observed that a reduction of 2.51 kg CO$$_{2}$$ 2 is obtained by each euro reduced using metaheuristic techniques. Moreover, for both optimization objectives, it is observed that adding cells to bridge cross-sections improves not only the section behavior but also the optimization results. Finally, it is observed that the proposed design of double composite action in the supports allows to remove continuous longitudinal stiffeners in the bottom flange in this study.

Optimal Design of Sustainable Reinforced Concrete Precast Hinged Frames

“…The RCPHF design is subject to compliance with the requirements established by the standard regulations [36][37][38]. These, added to a series of technical considerations necessary for the complete verification of the RCPHF, are expressed in general terms through Equation (3). Once the general principles of the considered optimization problem have been detailed, the next step is the complete definition of the problem.…”

“…The growth and evolution of societies are directly related to their infrastructure. Thus, the construction, maintenance and demolition of the structures that support a country's economic and social practices are activities intrinsic to its development [3]. The characteristic consumption of large quantities of materials places construction as one of the industries that produce more greenhouse gas emissions, generating around 5% of the total CO 2 emitted yearly [4].…”

“…Consisting of the application of metaheuristic techniques in the design of structures, these methodologies result in new designs. These, in addition to complying with the relevant limit states, manage to minimize or maximize a series of previously established factors such as economic cost, associated CO 2 emissions or the energy consumed in the construction of the designed structure [3]. These techniques have been highly improved during the last decade, mainly due to the simplicity of the algorithms and their high adaptability, in addition to the ability to avoid convergence to a local optimum.…”

Exploring Initialization Strategies for Metaheuristic Optimization: Case Study of the Set-Union Knapsack Problem

“…Over the past few years, metaheuristic methods have emerged as powerful tools in addressing complex problems, particularly those pertaining to the realm of combinatorial challenges. A wide array of applications across various fields, including biology [1,2], logistics [3], civil engineering [4,5], machine learning [6], and many more, serve as compelling evidence of their effectiveness in problem-solving.…”

Optimal Deep Learning Assisted Design of Socially and Environmentally Efficient Steel Concrete Composite Bridges under Constrained Budgets

“…Two such algorithms that have been proposed are the Sine Cosine Algorithm (SCA), which uses sine and cosine functions to simulate the movements of individuals, and Cuckoo Search (CS), which models the behavior of natural cuckoo populations. Moreover, recent structural optimization studies have shown that adding a hybridization technique, such as K-means clustering, can improve the behavior of these metaheuristics [310], [311]. The SCA is a swarm intelligence method developed by Mirjalili [233] that employs sine and cosine functions to explore the solution space.…”