Size Optimization Of Structures Research Articles

A novel binomial-based fuzzy type-2 approach for topology and size optimization of skeletal structures

The current work introduces a new probability-based fuzzy type-2 decision mechanism to adjust the optimization process during the simultaneous size and topology optimization of the Skeletal structural systems. For the probabilistic part a binomial module is developed that feeds the fuzzy mechanism by forecasting success probability for future topological actions. The proposed fuzzy decision mechanism permanently monitors the optimization process and attends to dynamically tune the balance between size and topology actions. The presented strategy, by reducing the number of ineffective iterations, significantly enhances the efficiency of the optimization process. Since the proposed decision mechanism is designed as an auxiliary separate module it can be integrated with different optimization methods. Accordingly, in this study, it is integrated with four different optimization algorithms and applied to solve distinct size and topology problems. To comprehensively evaluate the effect of the proposed strategy a new performance index is defined and employed. The acquired results demonstrate that the proposed decision mechanism considerably enhances the search performance of the algorithms on handling the structural size and topology optimization problems.

Dynamic optimization design of thin plate structure based on surrogate model

Abstract To enhance dynamic optimization efficiency in traditional thin plate structures, surrogate models are integrated for structural dynamic size optimization, reducing dependence on high-precision finite element simulations. This paper focuses on optimizing the aluminum alloy sheet as the subject of research. The parameters of the specimen are optimized through sensitivity analysis, based on the comparison between the free mode Finite Element Analysis (FEA) and Experimental Modal Analysis (EMA) results. The modified specimen undergoes validation under a four-edge clamped boundary condition. Subsequently, random vibration analysis is conducted to determine the root mean square (RMS) value of the acceleration response. Employing surrogate model technology, four models are constructed using parametric modeling and experimental design methods. These models are then compared for their fitting effectiveness. The results indicate that the RBF model exhibits the highest accuracy in fitting each response, effectively replacing dynamic simulation. Finally, the RBF combined with the NSGA-II method, optimizes the specimen’s structural size, resulting in a 21.6% reduction in mass and an 18.60% decrease in acceleration response. These outcomes demonstrate satisfactory optimization results, ensuring accuracy while enhancing the dynamic characteristics of the specimen structure. This research offers valuable insights for related engineering applications.

Optimization of Composite Wing Structure with Static, Buckling, and Flutter Constraints using the Finite Element Method

In airplane structural design, creating a strong yet lightweight structure is crucial. During the preliminary design phase, structural sizing optimization is necessary to achieve an efficient and lightweight structure that meets safety standards. Composite materials offer a high strength-to-weight ratio, enabling a lighter structure without compromising strength. This work focuses on optimizing composite structure wings by considering laminae thicknesses as design variables and minimizing structure weight as the objective, using the gradient-based method. The optimization constraints include the Tsai-Hill criterion, buckling factor, and flutter speed to ensure the structure's safety against static load, buckling phenomena, and flutter phenomena. The optimization results indicate that the buckling factor is the most critical constraint, carrying the highest weight. The weight of the wing structure decreased by 53% and 79% after optimizing with static and flutter constraints, respectively. Despite minimal changes in weight after optimizing with the buckling constraint, the structure now meets the buckling safety criteria and is safe from buckling.

Hybrid optimization algorithm based on chaos game and differential evolution for constrained optimization of structures

ABSTRACT A novel hybrid optimization algorithm, chaos game optimization–differential evolution (CGO-DE), is developed in this study for the size optimization of structures. The key contribution lies in the synergistic combination of chaos game optimization (CGO) and differential evolution (DE), using their respective strengths in exploration and exploitation to effectively identify global optima. The innovative approach independently applies CGO and DE to subpopulations, preventing premature convergence and enhancing overall performance while ensuring accuracy, accounting for design factors and respecting constraints. Comprehensive numerical evaluations on established structural benchmarks (three-bar truss, welded beam and cantilever beam) and large-scale structures (272-bar and 942-bar truss tower) demonstrate the versatility and efficiency of CGO-DE in solving complex optimization problems across various engineering applications. The results underscore the ability of the algorithm to achieve optimal solutions and improve designs compared to other optimization techniques, highlighting its potential as a powerful tool for engineering optimization.

Gate-Based Variational Quantum Algorithm for Truss Structure Size Optimization Problem

Quantum computing has become a pivotal innovation in computational science, offering novel avenues for tackling the increasingly complex and high-dimensional optimization challenges inherent in engineering design. This paradigm shift is particularly pertinent in the domain of structural optimization, where the intricate interplay of design variables and constraints necessitates advanced computational strategies. In this vein, the gate-based variational quantum algorithm utilizes quantum superposition and entanglement to improve search efficiency in large solution spaces. This paper delves into the gate-based variational quantum algorithm for the discrete variable truss structure size optimization problem. By reformulating this optimization challenge into a quadratic, unconstrained binary optimization framework, we bridge the gap between the discrete nature of engineering optimization tasks and the quantum computational paradigm. A detailed algorithm is outlined, encompassing the translation of the truss optimization problem into the quantum problem, the initialization and iterative evolution of a quantum circuit tailored to this problem, and the integration of classical optimization techniques for parameter tuning. The proposed approach demonstrates the feasibility and potential of quantum computing to transform engineering design and optimization, with numerical experiments validating the effectiveness of the method and paving the way for future explorations in quantum-assisted engineering optimizations.

Size and topology optimization of truss-like structures under seismic excitations incorporating probabilistic aspects

Size and topology optimization of truss-like structures under seismic excitations incorporating probabilistic aspects

A novel binomial strategy for simultaneous topology and size optimization of truss structures

The current work introduces a new probability-based regulatory mechanism for the simultaneous size and topology optimization of truss structures. The proposed mechanism, by leveraging the Boolean nature of the topological variables, attempts to forecast the behaviour of the search algorithm and emphasizes either topology or size actions to reduce the number of ineffective iterations. The importance of this task becomes more evident in further iterations since the optimal (or nearly optimal) structure topology is identified, and improper elimination of any member can result in infeasible mechanisms and lead to a waste of several iterations. To assess the effectiveness of the proposed auxiliary module, it is integrated with different optimization algorithms to solve distinct size and topology problems. The results demonstrate that this not only enhances the accuracy of the algorithms but also significantly reduces the required computational cost.

Efficient Sizing and Layout Optimization of Truss Benchmark Structures Using ISRES Algorithm

This paper presents a comprehensive investigation into the application of the Improved Stochastic Ranking Evolution Strategy (ISRES) algorithm for the sizing and layout optimization of truss benchmark structures. Truss structures play a crucial role in engineering and architecture, and optimizing their designs can lead to more efficient and cost-effective solutions. The ISRES algorithm, known for its effectiveness in multi-objective optimization, is adapted for the single-objective optimization of truss designs with multiple design constraints. This study encompasses a wide range of truss benchmark structures, including 10-bar, 15-bar, 18-bar, 25-bar, and 72-bar configurations, each subjected to distinct loading conditions and stress constraints. The objective is to minimize the truss weight while ensuring stress and displacement limits are met. Through extensive experimentation, the ISRES algorithm demonstrates its ability to efficiently explore the solution space and converge to optimal solutions for each truss benchmark structure. The algorithm effectively handles the complexity of the problems, which involve numerous design variables, stress constraints, and nodal displacement limits. A comparative analysis is conducted to assess the performance of the ISRES algorithm against other state-of-the-art optimization methods reported in the literature. The comparison evaluates the quality of the solutions and the computational efficiency of each method. Furthermore, the optimized truss designs are subjected to finite element analysis to validate their structural integrity and stability. The verification process confirms that the designs adhere to the imposed constraints, ensuring the safety and reliability of the final truss configurations. The results of this study demonstrate the efficacy of the ISRES algorithm in providing practical and reliable solutions for the sizing and layout optimization of truss benchmark structures. The algorithm’s competitive performance and robustness make it a valuable tool for structural engineers and designers, offering a versatile and powerful approach for complex engineering optimization tasks. Overall, the findings contribute to the advancement of optimization techniques in structural engineering, promoting the development of more efficient and cost-effective truss designs for a wide range of engineering and architectural applications. The study’s insights empower practitioners to make informed decisions in selecting appropriate optimization strategies for complex truss-design scenarios, fostering advancements in structural engineering and sustainable design practices.

Effects of Limiting the Number of Different Cross-Sections Used in Statically Loaded Truss Sizing and Shape Optimization.

This research aims to show the effects of adding cardinality constraints to limit the number of different cross-sections used in simultaneous sizing and shape optimization of truss structures. The optimal solutions for sizing and shape optimized trusses result in a generally high, and impractical, number of different cross-sections being used. This paper presents the influence of constraining the number of different cross-sections used on the optimal results to bring the scientific results closer to the applicable results. The savings achieved using the cardinality constraint are expected to manifest in more than just the minimization of weight but in all the other aspects of truss construction, such as labor, assembly time, total weld length, surface area to be treated, transport, logistics, and so on. It is expected that the optimal weight of the structures would be greater than when not using this constraint; however, it would still be below conventionally sized structures and have the added benefits derived from the simplicity and elegance of the solution. The results of standard test examples for each different cardinality constraint value are shown and compared to the same examples using only a single cross-section on all bars and the overall optimal solution, which does not have the cardinality constraint. An additional comparison is made with results of just the sizing optimization from previously published research where authors first used the same cardinality constraint.

Development of a non-uniform cellular automata framework for sizing, topology and layout optimization of truss structures

This article presents a bi-level non-uniform cellular automata (CA) algorithm for the solution of sizing, topology and layout optimization of truss structures. The non-uniform CA was successfully used in a previous study to solve the weight optimization problem of truss structures for topology and sizing (El Bouzouiki, Sedaghati, and Stiharu 2021. Computers & Structures 242: 106394). In this article, an extended version of the non-uniform CA algorithm is proposed, based on the fully stressed design approach and the distribution of strain energy within the structure, to find the optimal position of the cell’s (joint’s) coordinates. The proposed non-uniform CA algorithm can solve the minimum weight optimization problem of truss structures subjected to both stress and displacement constraints. Several benchmark problems are presented to demonstrate the efficiency and accuracy of the proposed methodology.

Layout and cross-sectional size optimization of truss structures with mixed design variables based on gradient method

The object of the study was truss-type rod structures, which were investigated for the purpose of finding the optimal design solution in a mixed (continuous and discrete) space of variables. The parameters of the geometric scheme of the truss, as well as the dimensions of the cross-sections of its elements, were considered as design variables. The stated optimization problem is represented as a nonlinear programming task, in which the objective function and nonlinear constraints of the mathematical model are continuously differentiable functions of the design variables. The system of constraints includes strength and stability inequalities, formulated for the design cross-sections of the rod elements of the structure, which is subject to the effect of the design load combinations of the ultimate limit states. As a part of the system of constraints, the displacement constraints formulated for the specified structural nodes, which is subject to the action of design load combinations of the serviceability limit states, are considered. To solve the stated optimization problem, a method of the objective function gradient on the surface of active constraints was used, with the simultaneous elimination of residuals in the violated restrictions. For design variables, the variation of which must be performed according to a given set of possible discrete values, a discretization procedure for the optimal solution obtained in the continuous space of design variables is proposed. A comparison of the proposed optimization methodology with alternative metaheuristic methods and algorithms reported in the literature was performed. On the considered problem of parametric optimization of a 47-span tower structure, a design solution with a weight of 835,403 kg was obtained, which is 1.53...4.6 % better than the optimal solutions obtained by other authors

Swarm-based search algorithms: A comparative study on sizing optimization of truss structures

Metaheuristic algorithms belong to the category of non-deterministic optimization methods. Extensive research conducted in this domain has elucidated that each of these methods possesses distinctive merits and demerits. To illustrate, one algorithm may exhibit a notable penchant for exploration, whereas another algorithm may demonstrate exceptional prowess in exploitation. The judicious selection of a suitable and efficient algorithm for a given problem can profoundly influence both the rate of convergence and the level of accuracy. Over the past few decades, various swarm-based metaheuristic algorithms have been introduced in technical literature. Consequently, undertaking a comprehensive comparative evaluation of a subset of these methodologies can furnish researchers with an essential framework to discern the most appropriate algorithm for their objectives. The current investigation focuses on evaluating and comparing four swarm-based metaheuristic algorithms especially on solving size optimization of truss structures for this purpose. To cover the research conducted in the past two decades as comprehensively as possible, a hierarchical selection process was employed for choosing the methods. As a result, the following algorithms were chosen Firefly Algorithm (FA), Drosophila Food-search Algorithm (DFO), Harris Hawk Optimization (HHO), and Butterfly Optimization Algorithm (BOA). Various characteristics of the selected algorithms, such as convergence rate, diversity variation, complexity, and accuracy of the final solutions, were compared. The findings indicate Harris Hawk Optimization (HHO) could nearly outperform the other selected methods on solving structural size optimization problems.

Experimental study on waterproof performance of assembled station joints of Shenzhen Rail Transit Line

With the development of underground prefabricated building structure, the influence of waterproof performance on underground engineering construction is becoming more and more important. Based on the analysis of the current situation of waterproof technology of prefabricated subway station joints, this paper conducts similar experiments on the waterproof mechanism and construction characteristics of the water-swelling rubber water-stop selected at the joint, and analyzes the waterproof performance of the assembly joint and the overall performance of the structure. It lays the foundation for the selection of waterproof materials, structural size optimization and opening design of the prefabricated subway station structure, and analyzes the waterproof performance of the rubber water-stop. In order to grasp and record the overall stability and waterproof performance of the assembled subway station structure in time, on the basis of field investigation, the model joint specimens of the assembled subway station joints were made according to the similar theoretical model experiment, and the joints of the assembled subway station joints were watered. The internal seepage, development and deformation convergence of the specimen structure were monitored in real time. The monitoring results will provide data support for the operation safety of the assembled subway station.

Structural optimization of a small earth remote sensing satellite

Structural sizing optimization is a crucial step in the process of modern satellite structural design. It warrants minimizing the satellite structural mass while improving the overall quality and reliability of such design. This article discusses thoroughly the structural optimization of a small earth remote sensing satellite whose primary structure is based upon aluminum honeycomb sandwich plates. A novel contribution has been conducted in extending the optimization problem to include the main static and dynamic loads acting simultaneously on the satellite during launch phase utilizing the results of static, buckling, modal, and harmonic response analyses. The small satellite optimization process starts initially with formulating the optimization problem based upon the structural analyses results. Subsequently, miscellaneous optimization techniques utilizing ANSYS workbench capabilities represented in “DesignXplorer” module are employed. Finally, the results are compared with other results obtained from a MATLAB code based upon both genetic and sequential quadratic programming algorithms. The optimization process leads to approximately 30% mass reduction in the proposed satellite structure. A special attention is drawn towards meta-models due to their capabilities in solving complex structural models with a reasonable wall-clock time.

Chaotic heterogeneous comprehensive learning PSO method for size and shape optimization of structures

This paper proposes a novel chaotic heterogeneous comprehensive learning particle swarm optimization (CLPSO) method for the simultaneous size and shape design of structures. The heterogeneous CLPSO divides the particles into two explorative and exploitative subpopulations. The exploration performs the global searches for the set of best particles experienced solely within its own subpopulation, whilst the exploitation refines the deep searches learnt from the global best particle over an entire population. In essence, the proposed method maintains a good balance between the global explorative and local exploitative optimization schemes. The global searches within the explorative subpopulation are independent to the exploitative simulations even if the latter scheme prematurely converges to the local swarm position. For both subpopulations, the comprehensive learning approach constructs the particles through a cross-positioning process on the individual variable space and avoids the local optimal pitfall. Moreover, the chaotic logistic map within the exploitative optimization tests the global best particle through the set of diversely generated samples and hence enhances the local search ability. Various enriching techniques, including automatic adaptive (inertial weight and acceleration) parameters with dynamic space reduction, are incorporated to improve the likelihood of finding the optimal solution of practical-scale problems at modest computing efforts. The accuracy and robustness of the proposed method are illustrated through a number of planar and spatial truss design benchmarks subjected to the challenging nonconvex and/or nonsmooth programs.

Study on Dynamic Amplification Coefficient of U-Shaped Girder Based on Vehicle-Bridge Coupling Dynamics

In this paper, a vehicle-bridge coupling dynamic model is established considering the vertical wheel-rail tight contact and the lateral simplified wheel-rail creeping. Through the site test and the numerical calculation, the dynamic deflections and the stresses of the U-shaped girder in the Nanjing S6 Urban Rail Transit in China are obtained, and the effectiveness of the established numerical model is verified. By changing the vehicle types and speeds, the dynamic amplification coefficients (DAFs) of the vertical deflections and the biaxial stresses at the girder bottom including the key points at the plate and section intersections are calculated. The research shows that the distribution of the lateral stresses is more complex than that of the deflection and the longitudinal stresses. Based on the calculation results considering various vehicle types, it is suggested that the DAFs of the vertical deflections and the longitudinal stresses are taken as 1.30, and the DAF of the lateral stresses remains at 1.40 as stipulated by the code. The research of this paper is to provide a reference for the structural design and size optimization of the U-shaped girders for urban rail transit.

Design and test a pneumatically actuated microgripper based on structural stiffness.

In response to the problem that the actual amplification ratio of the compliant motion amplification mechanism cannot be further improved, this paper introduces a two-stage amplification microgripper based on structural stiffness that is driven by pneumatics. The mechanism not only has the advantages of good symmetry, compact structure, and large output displacement but can also reduce the relative error of the theoretical and experimental amplification ratios. The first-stage mechanism selects high-stiffness mechanisms and high-stiffness flexure hinges, and the second-stage mechanism uses low-stiffness mechanisms and low-stiffness flexure hinges. The arrangement order of the mechanism is determined by the working mode analysis. The specific dimensions of the mechanism and flexure hinges are determined through structural size optimization so that the amplification performance of the mechanism will be optimal. The experimental results show that the displacement amplification ratio of both the opening and closing of the microgripper is 41.8.

A comparison performance analysis of eight meta-heuristic algorithms for optimal design of truss structures with static constraints

Metaheuristics have been successfully used for solving complex structural optimization problems. Many algorithms are proposed for truss structure size and shape optimization under some constraints. This study considers eight population-based meta-heuristic methods: African Vultures Optimization Algorithm (AVOA), Flow Direction Algorithm (FDA), Arithmetic Optimization Algorithm (AOA), Generalized Normal Distribution Optimization (GNDO), Stochastic Paint Optimizer (SPO), Chaos Game Optimizer (CGO), Crystal Structure Algorithm (CRY) and Material Generation Algorithm (MGO). These meta-heuristics methods are used to optimize the size of three aluminum truss structures. Optimization aims to reduce the weight of the truss members while meeting a set of displacement and stress constraints. The performance of these methods is assessed by solving and optimizing three well-known truss structure benchmarks under some constraints. The results show that the Stochastic Paint Optimizer (SPO) outperforms the other algorithms in terms of accuracy and convergence rate.

Memory-assisted adaptive multi-verse optimizer and its application in structural shape and size optimization

Memory-assisted adaptive multi-verse optimizer and its application in structural shape and size optimization

A first-order equivalent static loads algorithm for optimization of nonlinear static response

The Equivalent Static Loads (ESL) algorithm for nonlinear static response structural optimization is modified to promote convergence to designs satisfying first-order optimality conditions. The modifications involve first-order estimates of the equivalent static loads considered in the sub-problems and algorithmic stabilization through a trust-region approach. The practical convergence properties of the original and modified algorithms are assessed through numerical experiments on a set of reproducible structural size optimization problems. The results demonstrate the capabilities of the proposed algorithm in finding optimized designs that satisfy first-order optimality conditions numerically with modest computational resources.