Structural design optimization

Guest Editorial, Special Issue on Structural Design Optimization, Advances in Structural Engineering, An International Journal, 2007, Vol. 10, No.6

Performance-based optimization of structures: theory and applications

Performance-based Optimization of Structures introduces a method to bridge the gap between optimization theory and its practical applications to structural engineering. The performance-based optimization (PBO) method combines modern structural optimization theory with performance-based design concepts to produce a powerful technique for use in structural design. This book provides the latest PBO techniques for achieving optimal topologies and shapes of continuum structures with stress, displacement and mean compliance constraints. The emphasis is strongly placed on practical applications of automated PBO techniques to the strut-and-tie modeling of structural concrete, which includes reinforced and prestressed concrete structures. Basic concepts underlying the development of strut-and-tie models, design optimization procedure, and detailing of structural concrete are described in detail. The design optimization of lateral load resisting systems for multi-story steel and steel-concrete composite buildings is also presented. Numerous practical design examples are given which illustrate the nature of the load transfer mechanisms of structures

Structural and thermal performances of topological optimized masonry blocks

Structural topology optimization is the most fundamental form of structural optimization and receives an increasing attention from engineers and structural designers. The method enables the exploration of the general topology and shape of structural elements at an early stage of the design process and gives rise to inspiring and innovative improvements. In this paper, topology optimization as a principle is used to design new types of insulating masonry blocks. Two main objectives are addressed: maximizing the structural stiffness and minimizing the thermal transmittance. The first part of this paper uses these objectives to create new block topologies. A general problem is formulated and the influences of boundary conditions, external loading, and filter value on the resulting geometry are discussed. In general, maximizing the stiffness is in strong contrast to minimizing the thermal transmittance. This causes problems not encountered in conventional topology optimization. Nevertheless, by adjusting the interpolation schemes and adding multiple load groups, convergent solutions are found. An isotropic material model with an enforced solid-or-empty distribution is considered as the primary method. The optimized block topologies are then thoroughly analyzed to review their structural and thermal performance using the commercial finite element software Abaqus. The direct compressive strength of the block is a measure of the structural performance and the equivalent thermal conductivity gives an indication of the thermal performance. The second part then gives some thoughts on three-dimensional optimization and the incorporation of mesostructures in the design

Sharp interface limit for a phase field model in structural optimization

We formulate a general shape and topology optimization problem in structural optimization by using a phase field approach. This problem is considered in view of well-posedness and we derive optimality conditions. We relate the diffuse interface problem to a perimeter penalized sharp interface shape optimization problem in the sense of $\Gamma$-convergence of the reduced objective functional. Additionally, convergence of the equations of the first variation can be shown. The limit equations can also be derived directly from the problem in the sharp interface setting. Numerical computations demonstrate that the approach can be applied for complex structural optimization problems

On a New Type of Information Processing for Efficient Management of Complex Systems

It is a challenge to manage complex systems efficiently without confronting NP-hard problems. To address the situation we suggest to use self-organization processes of prime integer relations for information processing. Self-organization processes of prime integer relations define correlation structures of a complex system and can be equivalently represented by transformations of two-dimensional geometrical patterns determining the dynamics of the system and revealing its structural complexity. Computational experiments raise the possibility of an optimality condition of complex systems presenting the structural complexity of a system as a key to its optimization. From this perspective the optimization of a system could be all about the control of the structural complexity of the system to make it consistent with the structural complexity of the problem. The experiments also indicate that the performance of a complex system may behave as a concave function of the structural complexity. Therefore, once the structural complexity could be controlled as a single entity, the optimization of a complex system would be potentially reduced to a one-dimensional concave optimization irrespective of the number of variables involved its description. This might open a way to a new type of information processing for efficient management of complex systems.Comment: 5 pages, 2 figures, to be presented at the International Conference on Complex Systems, Boston, October 28 - November 2, 200

Design optimization applied in structural dynamics

This paper introduces the design optimization strategies, especially for structures which have dynamic constraints. Design optimization involves first the modeling and then the optimization of the problem. Utilizing the Finite Element (FE) model of a structure directly in an optimization process requires a long computation time. Therefore the Backpropagation Neural Networks (NNs) are introduced as a so called surrogate model for the FE model. Optimization techniques mentioned in this study cover the Genetic Algorithm (GA) and the Sequential Quadratic Programming (SQP) methods. For the applications of the introduced techniques, a multisegment cantilever beam problem under the constraints of its first and second natural frequency has been selected and solved using four different approaches

Recent experiences using finite-element-based structural optimization

Structural optimization has been available to the structural analysis community as a tool for many years. The popular use of displacement method finite-element techniques to analyze linearly elastic structures has resulted in an ability to calculate the weight and constraint gradients inexpensively for numerical optimization of structures. Here, recent experiences in the investigation and use of structural optimization are discussed. In particular, experience with the commercially available ADS/NASOPT code is addressed. An overview of the ADS/NASOPT procedure and how it was implemented is given. Two example problems are also discussed

Learning-based ship design optimization approach

With the development of computer applications in ship design, optimization, as a powerful approach, has been widely used in the design and analysis process. However, the running time, which often varies from several weeks to months in the current computing environment, has been a bottleneck problem for optimization applications, particularly in the structural design of ships. To speed up the optimization process and adjust the complex design environment, ship designers usually rely on their personal experience to assist the design work. However, traditional experience, which largely depends on the designer’s personal skills, often makes the design quality very sensitive to the experience and decreases the robustness of the final design. This paper proposes a new machine-learning-based ship design optimization approach, which uses machine learning as an effective tool to give direction to optimization and improves the adaptability of optimization to the dynamic design environment. The natural human learning process is introduced into the optimization procedure to improve the efficiency of the algorithm. Q-learning, as an approach of reinforcement learning, is utilized to realize the learning function in the optimization process. The multi-objective particle swarm optimization method, multiagent system, and CAE software are used to build an integrated optimization system. A bulk carrier structural design optimization was performed as a case study to evaluate the suitability of this method for real-world application