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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Vydáno v:	Structural and multidisciplinary optimization Ročník 68; číslo 8; s. 161
Hlavní autoři:	Quéva, Paul, Jason, Ludovic, Arnaud, Gilles, Sarazin, Gabriel
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 21.08.2025 Springer Nature B.V Springer Verlag
Témata:	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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Abstract	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.
AbstractList	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 8×107 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. 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 significantly 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. 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. 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 $$8\times {10}^{7}$$ 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.
ArticleNumber	161
Author	Jason, Ludovic Arnaud, Gilles Quéva, Paul Sarazin, Gabriel
Author_xml	– sequence: 1 givenname: Paul orcidid: 0009-0009-3383-2712 surname: Quéva fullname: Quéva, Paul email: paul.queva@cea.fr organization: Université Paris-Saclay, CEA, Laboratoire Energétique Mécanique Electromagnétisme, Université Paris Nanterre – sequence: 2 givenname: Ludovic surname: Jason fullname: Jason, Ludovic organization: Université Paris-Saclay, CEA – sequence: 3 givenname: Gilles surname: Arnaud fullname: Arnaud, Gilles organization: Université Paris-Saclay, CEA, Service de Génie Logiciel pour la Simulation – sequence: 4 givenname: Gabriel surname: Sarazin fullname: Sarazin, Gabriel organization: Université Paris-Saclay, CEA, Service de Génie Logiciel pour la Simulation
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SubjectTerms	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
Title	Tailored genetic algorithms for the detailed design optimization of reinforced concrete structures: case study on a flexural beam
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