Tailored genetic algorithms for the detailed design optimization of reinforced concrete structures: case study on a flexural beam

This contribution proposes an optimization method for the detailed design of reinforced concrete (RC) structures subject to NF EN 1992–1-1 requirements, integrating various criteria such as economic and sustainability. Traditional methods, focused on strength and durability, leave engineers with an...

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Veröffentlicht in:Structural and multidisciplinary optimization Jg. 68; H. 8; S. 161
Hauptverfasser: Quéva, Paul, Jason, Ludovic, Arnaud, Gilles, Sarazin, Gabriel
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 21.08.2025
Springer Nature B.V
Springer Verlag
Schlagworte:
Case studies
Combinatorial analysis
Complexity
Compliance
Computational Mathematics and Numerical Analysis
Concrete structures
Criteria
Design optimization
Engineering
Engineering Design
Engineering Sciences
Enumeration
Genetic algorithms
Methods
Multiple objective analysis
Optimization algorithms
Optimization techniques
Parameter sensitivity
Parameterization
Reinforced concrete
Research Paper
Searching
Theoretical and Applied Mechanics
Carbon footprint minimization
Cost minimization
Steel reinforcement design
Genetic algorithm
Multiobjective optimization
Reinforced concrete structure
ISSN:1615-147X, 1615-1488
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Zusammenfassung:This contribution proposes an optimization method for the detailed design of reinforced concrete (RC) structures subject to NF EN 1992–1-1 requirements, integrating various criteria such as economic and sustainability. Traditional methods, focused on strength and durability, leave engineers with an infinite range of solutions to account for these criteria. To overcome these difficulties, genetic algorithm (GA)-based approaches have been used to automate the search for optimal trade-offs between competing objectives. However, when applied to the detailed design of RC structures, these approaches face challenges due to the combinatorial nature of the problem. In practice, simplifying assumptions—such as fixed bar diameters or reliance on predefined reinforcement arrangements—are often introduced to reduce the complexity of the search space and formulation. While effective computationally, these assumptions can limit the diversity and novelty of the resulting designs. To address these limitations, the proposed methodology introduces a GA-based optimization framework with a refined parametrization of the design space. Particular attention is given to the selection of variable inputs and the integration of tailored operators to enable efficient exploration of innovative designs. The performance of the proposed methodology is demonstrated on a constrained multiobjective optimization problem applied to the detailed design of an RC beam under three-point bending. The comparison against an exhaustive exploration of the search space reveals that the algorithm successfully converges to complex optimal designs while requiring at least 8 × 10 7 times fewer computations than a complete enumeration of the search space. Finally, a discussion on the method's sensitivity to parameter calibration and its dependence on operator choices is also provided.
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ISSN:1615-147X
1615-1488
DOI:10.1007/s00158-025-04092-x