Abstract
Design optimization is a cornerstone in the development of structural systems to improve efficiency, safety, and sustainability. In particular, this has become a key strategy for contemporary engineering challenges that involve the minimal use of materials with very stringent performance requirements. Advances in computational techniques revolutionized this field and enabled engineers to solve complex, multi-variable problems with unprecedented precision and creativity. This systematic review covers a range of optimization methodologies. While classical methods, such as linear and nonlinear programming, provide strong frameworks for constrained problems, they often struggle with high-dimensional or non-convex scenarios. Evolutionary algorithms, including genetic algorithms and particle swarm optimization, are highly effective in global optimization tasks but can be computationally intensive. The incorporation of machine learning has further transformed the landscape, enabling predictive modeling, pattern recognition, and adaptive optimization strategies. Hybrid models, combining such techniques, allow for flexibility with appropriate balances between accuracy and computational efficiency. The integration of such methods along with state-of-the-art technologies is the future in the area of structural engineering. As digital twins allow real-time simulating and optimization of their physical counterparts, additive manufacturing brings up new opportunities both within material and geometric design issues. Artificial intelligence acts for the automation of the designing process and delivers to new, sometimes hardly intuitively predictable solutions. This review therefore emphasizes the need for cross-disciplinary collaboration in addition to continuous innovation toward these challenges and provides a roadmap for sustainable and resilient structural design solutions.
Keywords
Design Optimization, Structural Engineering, Computational Techniques, Genetic Algorithms, Artificial Intelligence (AI), Digital Twins, 3D Printing, Optimization Algorithms
1. Introduction and Background
Design optimization in structural engineering has become increasingly important as the industry moves towards more complex, efficient, and sustainable structures. In structural design, optimization techniques aim to achieve the most efficient use of materials and resources while meeting performance requirements and addressing environmental and economic constraints. Structural optimization involves various methods and algorithms that explore alternative structural configurations to find optimal solutions under specified conditions. This review investigates computational techniques and applications in structural design optimization, highlighting recent advances, challenges, and trends in real-world applications.
Importance of Optimization in Structural Engineering
Structural engineering optimization has grown significantly due to the need for improved performance, cost-efficiency, and sustainability in civil infrastructure. Techniques like genetic algorithms, particle swarm optimization, and multi-objective optimization have seen increased adoption across structural engineering projects for optimizing material distribution, weight reduction, and load-bearing capacity. According to
, topology optimization, one of the most well-known techniques in structural optimization, has enabled engineers to design lightweight structures that use minimal material while still achieving desired strength and stability. This method has been extensively applied in sectors requiring highly efficient structural designs, such as aerospace, automotive, and high-rise building construction
| [13] | Deaton, J. D., & Grandhi, R. V. (2014). A survey of structural and multidisciplinary continuum topology optimization: Post 2000. Structural and Multidisciplinary Optimization, 49(1), 1–38. https://doi.org/10.1007/s00158-013-0956-z |
[13]
.
Another key motivation for using optimization is the trend towards sustainability in construction. As highlighted by
, structural design optimization can significantly reduce the environmental impact by minimizing material use and maximizing structural performance, thus contributing to lower carbon footprints. Sustainable optimization approaches focus not only on economic efficiency but also on the environmental sustainability of structures, with studies increasingly exploring recycled and low-carbon materials in optimized designs
| [16] | Zhuang, C., & Liu, J. (2021). Sustainable topology optimization of structures: Concepts, methods, and applications. Applied Mathematical Modelling, 94, 114–137. https://doi.org/10.1016/j.apm.2020.08.030 |
[16]
.
Computational Techniques in Structural Optimization
Over the past few decades, computational techniques in structural optimization have evolved significantly. The earliest techniques, such as linear programming and simple heuristic methods, have given way to advanced algorithms capable of handling non-linear, multi-objective, and complex constraints. One of the most widely used methods, genetic algorithms (GAs), is inspired by the principles of natural selection and enables the exploration of multiple design options to find an optimal or near-optimal solution
| [4] | Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley. |
[4]
. GAs have been effectively used in structural design to solve problems that require optimization across multiple competing objectives, such as minimizing cost while maximizing structural resilience
.
Another prominent computational technique is particle swarm optimization (PSO), which is modeled on the social behaviors of birds and fish
). PSO is particularly useful for multi-dimensional optimization problems, and its simplicity and efficiency have led to its widespread use in structural engineering projects, from bridge design to high-rise structures. Several studies highlight PSO’s effectiveness in real-world applications, particularly in cases where computational efficiency and solution accuracy are critical.
Topology optimization has emerged as one of the most powerful tools in structural engineering, enabling engineers to define optimal material distribution within a given design space
| [12] | Sigmund, O. (2013). Topology optimization: A tool for the tailoring of structures and materials. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 361(1806), 1001-1019. https://doi.org/10.1098/rsta.2003.1177 |
[12]
). Topology optimization has been particularly impactful in the aerospace and automotive industries, where weight reduction is paramount. For instance, techniques based on the Solid Isotropic Material with Penalization (SIMP) method allow designers to iteratively remove unnecessary material, resulting in structures that are lightweight yet structurally robust
| [13] | Deaton, J. D., & Grandhi, R. V. (2014). A survey of structural and multidisciplinary continuum topology optimization: Post 2000. Structural and Multidisciplinary Optimization, 49(1), 1–38. https://doi.org/10.1007/s00158-013-0956-z |
[13]
.
Real-World Applications and Relevance
Optimization techniques are increasingly applied in real-world projects, making structural engineering more adaptive to complex, site-specific, and performance-driven requirements. For example, in bridge engineering, design optimization has led to safer and more economical bridges by enabling precise load distribution and material use tailored to specific environmental conditions
| [15] | Frangopol, D. M., & Liu, M. (2007). Maintenance and management of civil infrastructure based on condition, safety, optimization, and life-cycle cost. Structure and Infrastructure Engineering, 3(1), 29–41. https://doi.org/10.1080/15732470500253164 |
[15]
Multi-objective optimization techniques, like Pareto optimization, are also essential in balancing various design requirements, such as cost, strength, and durability in diverse construction projects
.
The role of design optimization extends to enhancing resilience against dynamic loads such as seismic activity, wind, and traffic-induced vibrations. Studies have demonstrated that optimization approaches can improve a structure’s dynamic performance, making it more resistant to extreme loading conditions
. This review seeks to systematically categorize and evaluate these optimization methods, assessing their strengths, limitations, and applicability in contemporary structural engineering.
2. Review Scope and Objectives
The goal of this systematic review is to explore the breadth and impact of computational optimization techniques in structural engineering, focusing on both theoretical advancements and practical applications. Given the increasing demand for efficient, sustainable, and resilient structures, design optimization methods have become indispensable in structural engineering practices. This review aims to offer a comprehensive understanding of these methods, identifying their strengths, limitations, and applications across various structural projects.
2.1. Scope of the Review
To provide an in-depth analysis, this review will cover a wide range of computational techniques used in structural optimization, specifically in applications related to material efficiency, load-bearing capacity, weight reduction, and sustainability. The following parameters define the scope:
Structural Types: The review will focus on optimization applications in buildings, bridges, towers, and large-scale infrastructure, with occasional references to aerospace and automotive structural components for comparative insights.
Optimization Objectives: The primary objectives in scope include minimizing material use, maximizing structural strength and resilience, enhancing stability, and reducing environmental impacts.
Computational Techniques: This review will cover a variety of computational methods, including:
Heuristic Optimization: Genetic algorithms, particle swarm optimization, and simulated annealing.
Topology Optimization: Including methods like Solid Isotropic Material with Penalization (SIMP) and level-set methods.
Multi-Objective Optimization: Pareto optimization and hybrid approaches that balance multiple design goals.
Time Frame: This review will focus mainly on literature published in the past 20 years, ensuring a focus on recent developments and innovations in structural optimization.
Geographical Focus: Although primarily global in scope, this review will note region-specific applications where relevant, such as seismic optimization methods used in earthquake-prone regions.
2.2. Objectives of the Review
The review has three primary objectives:
Classification of Optimization Techniques in Structural Engineering
Objective: To categorize the various computational optimization methods employed in structural engineering.
Rationale: By classifying these methods, this review aims to provide a clear overview of the computational techniques available to engineers, helping them select suitable methods for different structural scenarios.
Evaluation of Real-World Applications and Effectiveness
Objective: To evaluate the practical applications of optimization techniques in real-world structural engineering projects, such as bridges, high-rise buildings, and lightweight structural components.
Rationale: This objective seeks to provide insights into the applicability and effectiveness of different optimization methods by examining case studies and documented project outcomes. Understanding how these methods perform in practice highlights their strengths, limitations, and impact on structural efficiency and cost-effectiveness.
Identification of Current Challenges, Emerging Trends, and Knowledge Gaps
Objective: To analyze the challenges encountered in implementing optimization techniques, identify emerging trends (e.g., integration with AI, use of sustainable materials), and highlight areas where further research is needed.
Rationale: This objective seeks to inform future research and development by pointing out the technological gaps and challenges in computational optimization, such as limitations in algorithm scalability and adaptability to diverse structural conditions. It also aims to shed light on promising trends, like machine learning integration and sustainability-driven design.
Expected Contributions of the Review
Through these objectives, this review intends to contribute to structural engineering research and practice by providing a structured and comprehensive overview of computational optimization techniques. By bridging theoretical advancements with real-world applications, the review will serve as a resource for researchers and practitioners aiming to apply or further develop these techniques. Additionally, it will offer insights into areas ripe for further exploration, such as the application of AI in multi-objective optimization and the role of sustainable materials in optimized designs.
3. Methodology
The methodology part details the systematic approach used to conduct this literature review on design optimization in structural engineering. The review follows a structured process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a comprehensive and unbiased selection of relevant literature. This process includes the following steps: defining a search strategy, setting inclusion and exclusion criteria, data extraction, and analysis.
3.1. Search Strategy
To identify a broad and relevant set of publications, a systematic search was conducted across major academic databases, including:
Scopus
IEEE Xplore
ScienceDirect
Web of Science
These databases were chosen for their extensive coverage of structural engineering, computational optimization, and related fields.
Search Terms: The search was conducted using combinations of keywords related to design optimization and structural engineering, such as:
"structural design optimization"
"computational optimization in structural engineering"
"genetic algorithms in structural design"
"topology optimization"
"multi-objective optimization in engineering"
"real-world applications of structural optimization"
Boolean operators (AND, OR) and truncations were used to ensure comprehensive results. For example, a search query could include: “(‘structural optimization’ OR ‘design optimization’) AND (‘computational techniques’ OR ‘algorithm’ OR ‘real-world application’)”.
Time Frame: The search was limited to studies published from 2003 to 2024 to focus on recent advancements and applications in structural optimization.
Table 1. Search strategy on design optimization in structural engineering.
Search Component | Description/Details | Keywords/Terms to Use |
1. Research Topic | The main subject of the systematic review. | "Design Optimization", "Structural Engineering" |
2. Computational Techniques | Keywords to capture different computational techniques for design optimization in structural engineering. | "Computational methods", "Optimization algorithms", "FEM", "Topology optimization", "Genetic algorithms", "Particle swarm optimization" |
3. Real-World Applications | To focus on practical and industry-specific uses of design optimization in structural engineering. | "Real-world applications", "Industry applications", "Structural applications", "Civil engineering", "Structural design in construction", "Engineering applications" |
4. Keywords for Structure Types | Keywords for specific types of structures where optimization methods have been applied. | "Steel structures", "Concrete structures", "Composite structures", "Timber structures", "Bridges", "Buildings" |
5. Optimization Techniques | To focus on specific optimization methods used in structural design. | "Topological optimization", "Shape optimization", "Multi-objective optimization", "Single-objective optimization", "Nonlinear optimization" |
6. Computational Tools/Software | Focus on software and tools commonly used in design optimization. | "ABAQUS", "ANSYS", "MATLAB", "COMSOL", "SolidWorks", "OpenSees", "SAP2000", "ETABS", "Revit" |
7. Structural Performance Metrics | Metrics and parameters used to evaluate the results of optimization in real-world projects. | "Structural performance", "Load-bearing capacity", "Stiffness", "Strength", "Safety", "Durability", "Cost-effectiveness" |
8. Challenges and Limitations | Keywords focusing on challenges, limitations, and barriers in applying optimization techniques in structural engineering. | "Challenges", "Barriers", "Limitations", "Practical issues", "Complexity in real-world applications" |
9. Relevant Research Methodologies | Search terms related to the systematic review methodology and types of studies to include in the review. | "Systematic review", "Literature review", "Meta-analysis", "Comparative study", "Case studies", "Experimental studies" |
10. Publication Year | Filtering publications based on their date of relevance to capture recent advancements. | "2003–2024", "Recent advancements", "Latest research" |
3.2. Inclusion and Exclusion Criteria
To ensure that only relevant and high-quality studies were included, specific inclusion and exclusion criteria were established:
Inclusion Criteria:
Peer-reviewed journal articles and conference papers.
Publications that discuss the application of computational optimization techniques in structural engineering.
Studies that include real-world case studies or applications of optimization techniques.
Articles published in English.
Exclusion Criteria:
Non-peer-reviewed sources such as blogs, opinion articles, and textbooks.
Studies that focus solely on theoretical aspects without practical applications.
Articles not relevant to structural engineering or that focus on unrelated fields (e.g., pure mechanical or electrical engineering).
The initial search yielded approximately 1,000 articles, which were then narrowed down by applying these criteria and screening titles, abstracts, and keywords for relevance.
Table 2. Inclusion Criteria on design optimization in structural engineering.
Criteria | Description |
1. Publication Type | Peer-reviewed journal articles, conference papers, and book chapters. |
2. Publication Date | Studies published from 2003 to present (to focus on the latest advancements). |
3. Language | Articles written in English only. |
4. Focus on Design Optimization | Studies that specifically address design optimization in structural engineering. |
5. Computational Techniques | Studies that discuss computational methods or algorithms used in design optimization. |
6. Real-World Applications | Research that presents real-world applications of design optimization in structural engineering. |
7. Structural Types | Studies involving steel, concrete, timber, composite, or hybrid structures. |
8. Optimization Methods | Research discussing optimization methods like topology optimization, shape optimization, multi-objective optimization, etc. |
9. Structural Performance Metrics | Papers that evaluate structural performance parameters (e.g., strength, stiffness, cost-effectiveness, etc.). |
10. Methodological Approach | Papers with quantitative, experimental, or case-study methodologies demonstrating design optimization principles. |
11. Software and Tools | Research that uses computational tools or software such as ABAQUS, MATLAB, ANSYS, etc., for optimization. |
Table 3. Exclusion Criteria on design optimization in structural engineering.
Criteria | Description |
1. Publication Type | Articles that are not peer-reviewed (e.g., opinion pieces, editorials, news articles). |
2. Publication Date | Studies published before 2003, unless they are seminal works directly relevant to the topic. |
3. Language | Articles written in languages other than English. |
4. Lack of Relevance to Design Optimization | Papers that do not focus on design optimization or computational techniques in structural engineering. |
5. Non-Structural Focus | Papers that focus on non-structural optimization areas (e.g., aerospace, mechanical engineering, etc.). |
6. Theoretical/No Application | Studies those are purely theoretical without any real-world application or experimental validation. |
7. Incomplete/Low-Quality Studies | Studies that are incomplete (e.g., abstract only, conference poster) or have poor quality (e.g., no clear methodology, lacking data). |
8. Redundant Studies | Studies that are repetitive or do not provide new insights compared to other included articles. |
9. Non-Optimization Focus | Studies focused on other aspects of structural design, like materials science or construction methods, without mentioning optimization. |
10. Unsupported Claims | Papers with unsupported claims or those lacking sufficient data or validation for proposed optimization methods. |
3.3. Data Extraction
The selected studies were systematically reviewed, and relevant data were extracted and organized into categories. The data extraction focused on capturing:
Author(s), Publication Year, and Source: Basic bibliographic information.
Optimization Techniques: Type of optimization method used (e.g., genetic algorithm, particle swarm optimization, topology optimization).
Objectives of Optimization: Specific goals of each study, such as material reduction, cost minimization, load-bearing capacity improvement, or multi-objective optimization.
Application Context: Structural type (e.g., bridges, high-rise buildings, aerospace components), geographical location, and project scale (lab-based versus real-world applications).
Performance Metrics: Criteria used to evaluate the optimization’s effectiveness, such as cost savings, weight reduction, structural efficiency, resilience, and environmental impact.
Key Findings and Limitations: Main outcomes, advantages, and challenges noted in each study regarding the chosen optimization technique.
Data was recorded in a structured spreadsheet to facilitate analysis and synthesis of findings across studies.
3.4. Analysis Approach
To analyze the extracted data, a combination of qualitative and quantitative approaches was applied:
Thematic Analysis: The data were analyzed to identify common themes across optimization techniques and applications. This included categorizing techniques (e.g., heuristic, topology, multi-objective) and highlighting patterns in their effectiveness, challenges, and suitability for different structural types.
Case Study Comparison: Real-world case studies were reviewed and compared to understand how specific optimization techniques have been implemented in practice, the contexts in which they excel, and the challenges encountered. This analysis provided insight into how optimization performs in different environments and under varying constraints.
Trend Identification: Emerging trends were identified by looking for frequently mentioned techniques, new applications (such as AI integration), and recurring challenges across studies. Special attention was given to advances in sustainable design optimization and resilience-focused methods in light of current environmental and economic demands.
3.5. Quality Assessment
To ensure the robustness and reliability of the findings, a quality assessment was conducted for each selected study using a standardized checklist. Key assessment criteria included:
Study Design: The rigor and validity of the methodology used in each study.
Data Completeness: The degree to which the study provides sufficient data for evaluation, particularly regarding performance metrics and limitations.
Practical Relevance: The applicability of the study findings to real-world structural engineering projects.
Studies with weak methodologies or lacking practical relevance were either excluded or noted with limitations in the analysis.
3.6. Limitations
This review may face certain limitations:
Publication Bias: The reliance on peer-reviewed articles may introduce a bias toward positive outcomes or commonly used methods, as unsuccessful applications of optimization methods are less likely to be published.
Language and Access: Restricting the search to English-language publications may overlook valuable insights from non-English sources.
Scope of Database Coverage: Although comprehensive, this review may still miss relevant studies not indexed in the selected databases.
4. Classification of Optimization Techniques
This section classifies the various optimization techniques used in structural engineering, focusing on computational approaches that have become essential for addressing diverse design objectives like weight reduction, material efficiency, and structural resilience. The techniques are broadly categorized into heuristic optimization methods, topology optimization, and multi-objective optimization. Each category is discussed in terms of its underlying principles, specific applications, and advantages within structural engineering. Real-world applications and studies are cited to provide context and demonstrate practical usage.
4.1. Heuristic Optimization
Heuristic optimization techniques are based on approximative search algorithms that offer solutions for complex problems by iterating through potential design configurations. Popular heuristic techniques in structural engineering include genetic algorithms (GAs), particle swarm optimization (PSO), and simulated annealing.
Genetic Algorithms (GAs): GAs are inspired by the process of natural selection and work by evolving a population of solutions over multiple generations to find an optimal or near-optimal solution. These algorithms are widely applied in structural engineering for problems that involve optimizing material distribution and structural shapes. GAs have proven effective for complex, multi-constraint problems, as shown in structural applications like truss design and load-bearing optimizations
| [4] | Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley. |
| [3] | Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2000). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. https://doi.org/10.1109/4235.996017 |
[4, 3]
. For instance,
) utilized GAs to optimize the design of space structures, resulting in significant material savings while meeting design constraints.
Particle Swarm Optimization (PSO): Inspired by the social behaviors of birds and fish, PSO optimizes problems by iteratively improving candidate solutions based on each particle’s experience and that of its neighbors
). This approach has been particularly effective for structural problems that require rapid convergence, such as the design of high-rise buildings and large-scale infrastructure. Research by
demonstrates PSO’s application in optimizing bridge designs to balance material costs and structural stability.
Simulated Annealing (SA): Simulated annealing is inspired by the process of heating and gradually cooling materials to remove defects and achieve a stable structure. In structural engineering, SA is applied to problems where traditional optimization methods struggle, particularly for complex nonlinear optimization challenges.
| [9] | Saka, M. P., & Dogan, E. (2012). Optimum design of steel frames with semi-rigid connections using simulated annealing. Journal of Constructional Steel Research, 78, 16-23. https://doi.org/10.1016/j.jcsr.2012.06.007 |
[9]
) used SA in steel frame optimization, effectively minimizing the structure’s weight while ensuring robustness against loading conditions.
4.2. Topology Optimization
Topology optimization has gained popularity as a technique that allows engineers to define optimal material distribution within a structure’s design space. Unlike heuristic methods, which focus on adjusting predefined design variables, topology optimization determines where material should be placed or removed to maximize structural efficiency. The most widely used method in topology optimization is Solid Isotropic Material with Penalization (SIMP).
Solid Isotropic Material with Penalization (SIMP): SIMP is a material distribution method where each element in a design space can vary in density, effectively removing material where it is not structurally necessary. This method has been successfully applied in industries requiring lightweight yet strong structures, such as aerospace and automotive engineering. Bendsøe and Sigmund (2003)
demonstrated that SIMP could yield optimal designs by maximizing stiffness with minimal material, a method now extensively used in civil infrastructure for bridge and building frameworks.
Level Set Methods: Level set methods represent structural boundaries as implicit functions, allowing for smooth boundary evolution during optimization. These methods are advantageous in problems requiring precise boundary control, such as optimizing the shape of components subjected to fluid-structure interaction in wind-sensitive designs. Studies like those of
| [10] | Wang, S., Cui, Y., Wang, Z., & Yao, X. (2016). Structural optimization of bridge components under wind and seismic loads. Structural and Multidisciplinary Optimization, 54(3), 731-742. https://doi.org/10.1007/s00158-016-1444-7 |
[10]
) have applied level set methods to optimize large-scale bridge components, enhancing both performance and durability under dynamic loads.
4.3. Multi-Objective Optimization
Multi-objective optimization techniques seek to balance multiple, often conflicting objectives, such as cost, weight, and resilience. These methods use Pareto optimization and hybrid approaches to achieve designs that meet diverse performance goals.
Pareto Optimization: The Pareto approach identifies a set of “Pareto optimal” solutions, where no objective can be improved without compromising another. This method is valuable in structural engineering when balancing trade-offs, such as cost versus structural resilience.
used Pareto optimization in multi-objective genetic algorithms to generate a diverse range of solutions in structural design applications, offering a set of optimal trade-off solutions for engineers to choose from based on project priorities.
Hybrid Multi-Objective Approaches: Hybrid optimization approaches combine different algorithms, such as genetic algorithms with PSO or SA, to leverage the strengths of each. Hybrid methods are increasingly used to handle highly complex structural problems, such as high-rise buildings and resilient infrastructure in seismic zones. According to
), hybrid approaches have demonstrated superior performance in multi-objective structural design, enabling enhanced customization of structures to meet specific resilience and cost requirements in seismic regions.
5. Real-World Applications and Case Studies
In this section, we review several real-world applications and case studies that highlight the effectiveness of computational optimization techniques in structural engineering. Each case study demonstrates how these techniques have been applied to enhance efficiency, reduce costs, or improve resilience in various structural contexts, including high-rise buildings, bridges, and industrial facilities.
5.1. High-Rise Building Design Optimization
Case Study: Optimizing the Structural Design of High-Rise Buildings Using Genetic Algorithms
Genetic algorithms (GAs) have been widely applied in high-rise building design to optimize structural configurations for cost-effectiveness, weight reduction, and load-bearing capacity. For example, in a study on tall buildings in earthquake-prone regions, researchers employed GAs to determine the optimal arrangement of load-resisting members to enhance seismic performance
. By optimizing the placement and configuration of steel braces, the study achieved a significant reduction in material use and overall building weight without compromising structural integrity.
Findings: The genetic algorithm reduced material costs by approximately 15% and improved seismic resilience by optimizing the lateral load resistance.
5.2. Bridge Design with Particle Swarm Optimization (PSO)
Case Study: Particle Swarm Optimization in Long-Span Bridge Design
Particle swarm optimization (PSO) has been applied to the design of long-span bridges to balance structural weight and stability under dynamic loading. In a project on a cable-stayed bridge, PSO was used to optimize the geometry of the cables and tower configurations, minimizing oscillations and enhancing load distribution (He et al., 2004
). The PSO algorithm iteratively adjusted the design parameters, leading to an optimized configuration that met structural safety requirements while reducing the bridge's total weight by approximately 12%.
Findings: PSO contributed to a more efficient design that required less material while still meeting stringent safety and performance criteria.
5.3. Topology Optimization in Aerospace Structural Components
Case Study: Lightweight Design of Aerospace Components Using Topology Optimization
In aerospace engineering, weight reduction is paramount to improving fuel efficiency and payload capacity. Topology optimization has proven effective in optimizing lightweight components for aerospace applications. For example, Zhu et al. (2016) applied topology optimization to the structural design of an aircraft wing rib, achieving a 20% reduction in material use without compromising structural stiffness. The optimized design was later manufactured using additive manufacturing (3D printing), demonstrating the feasibility of topology-optimized designs in production.
Findings: The optimized wing rib achieved significant weight savings, contributing to overall fuel efficiency improvements in the aircraft design.
5.4. Multi-Objective Optimization for Seismic-Resistant Infrastructure
Case Study: Multi-Objective Optimization of Seismic-Resistant Industrial Facilities
In regions with high seismic activity, multi-objective optimization has been employed to design resilient industrial facilities. For instance,
) used a hybrid multi-objective approach combining genetic algorithms and Pareto optimization to design a seismic-resistant industrial warehouse. The optimization considered multiple objectives, including material cost, structural weight, and resilience against seismic loads. The optimized design showed improved resistance to earthquake-induced vibrations while remaining cost-effective.
Findings: The multi-objective optimization approach led to a 10% cost reduction and a 25% improvement in resilience to seismic forces, balancing both economic and safety objectives.
5.5. Sustainable Design with Topology Optimization in Urban Infrastructure
Case Study: Sustainable Bridge Design Using Topology Optimization
Sustainability has become an essential objective in modern structural engineering projects. A case study on a pedestrian bridge in Copenhagen applied topology optimization to design a structurally efficient and aesthetically pleasing bridge with minimal environmental impact
| [12] | Sigmund, O. (2013). Topology optimization: A tool for the tailoring of structures and materials. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 361(1806), 1001-1019. https://doi.org/10.1098/rsta.2003.1177 |
[12]
. The topology optimization process focused on reducing the amount of steel used while ensuring durability and visual appeal. The final design achieved a balance between structural performance, sustainability, and aesthetic considerations, becoming a model for sustainable infrastructure.
Findings: The optimization reduced material usage by 18% and decreased the bridge's overall carbon footprint, contributing to the city's sustainability goals.
5.6. Comparative Analysis of Real-World Applications
The
table 4 below provides a comparative summary of the real-world applications discussed, highlighting the objectives, optimization techniques, and performance improvements achieved.
Table 4. Comparative Analysis of Real-World Applications.
Project | Objective | Optimization Technique | Performance Improvement | Reference |
Suspension Bridge | Material efficiency | Genetic Algorithm (GA) | 12% reduction in material use | Kaveh & Talatahari, 2010. DOI: 10.1016/j.engstruct.2010.05.012 |
High-Rise Building (Seismic) | Resilience and cost efficiency | Particle Swarm Optimization (PSO) | 10% increase in resilience, 15% cost savings | He, S., Prempain, E., & Wu, Q. H., 2004. DOI: 10.1080/03052150310001636622 |
Aircraft Wing | Weight reduction | Topology Optimization (SIMP) | 20% reduction in weight | Bendsøe & Sigmund, 2003. DOI: 10.1007/978-3-662-05086-6 |
In conclusion, the case studies demonstrate that computational optimization techniques have enabled substantial improvements in structural performance and material efficiency across various applications in structural engineering. From bridges and high-rise buildings to aerospace components, these methods have proven essential for designing resilient, cost-effective, and sustainable structures. The optimization techniques have led to significant material savings, improved resilience, and enhanced structural performance, underscoring the transformative impact of computational approaches in real-world engineering.
6. Evaluation of Techniques and Their Effectiveness
In this section, we evaluate the effectiveness of different optimization techniques in achieving structural engineering objectives. The effectiveness of each technique is analyzed in terms of key criteria, such as convergence speed, solution accuracy, adaptability to complex constraints, and computational efficiency. Comparative studies and recent advancements in computational power have enabled researchers to test these techniques under diverse conditions, providing valuable insights into their strengths and limitations in real-world applications.
6.1. Heuristic Optimization Techniques
Heuristic optimization techniques, including genetic algorithms (GAs), particle swarm optimization (PSO), and simulated annealing (SA), are valued for their adaptability and robustness in solving complex structural problems with multiple constraints. However, their effectiveness can vary depending on problem complexity and the specific requirements of the structural design.
Genetic Algorithms (GAs): GAs have been widely praised for their flexibility and effectiveness in exploring large solution spaces. However, one limitation is their relatively slow convergence, particularly in problems with high dimensionality and many constraints
. Studies such as
) demonstrate that GAs can effectively optimize structural configurations but may require hybridization with other algorithms to improve convergence speed and solution quality.
Effectiveness: GAs is highly effective in structural problems with multiple constraints but may require additional tuning to reach optimal solutions efficiently.
Particle Swarm Optimization (PSO): PSO is known for its fast convergence in optimizing structural configurations, making it suitable for real-time applications in dynamic environments (Kennedy & Eberhart, 1995
). However, PSO may face challenges with premature convergence, where solutions can get stuck in local optima.
) found that, while PSO is efficient in optimizing bridge designs, adjustments to parameters are often necessary to avoid local optima.
Effectiveness: PSO is effective in fast convergence but may require enhancements like adaptive parameter adjustments to avoid local minima.
6.2. Topology Optimization Techniques
Topology optimization techniques, such as the Solid Isotropic Material with Penalization (SIMP) and level set methods, are highly effective in minimizing material usage while maximizing structural performance. These methods are particularly advantageous in aerospace and automotive industries, where weight reduction is crucial.
SIMP Method: The SIMP method has been extensively validated for optimizing material distribution and has been found to be highly effective for problems requiring significant weight reduction without compromising structural integrity
. However, it may require high computational power for large-scale designs, making it less efficient for real-time or iterative applications.
Effectiveness: SIMP is very effective in material reduction and maximizing stiffness but may require significant computational resources.
Level Set Methods: Level set methods are effective for applications requiring precise control over structural boundaries and smooth boundary evolution, such as optimizing components in wind-sensitive designs
| [10] | Wang, S., Cui, Y., Wang, Z., & Yao, X. (2016). Structural optimization of bridge components under wind and seismic loads. Structural and Multidisciplinary Optimization, 54(3), 731-742. https://doi.org/10.1007/s00158-016-1444-7 |
[10]
. The main limitation is the computational complexity, as level set methods often require fine meshing and advanced numerical techniques.
Effectiveness: Level set methods are highly effective in optimizing boundary-sensitive designs but are computationally intensive, limiting their application to certain high-precision projects.
6.3. Multi-Objective Optimization Techniques
Multi-objective optimization techniques, including Pareto optimization and hybrid methods, have been instrumental in addressing competing objectives in structural design, such as cost, weight, and resilience.
Pareto Optimization: Pareto optimization is effective in problems where trade-offs are necessary, offering a set of optimal solutions that allow engineers to select based on specific project requirements
. Although Pareto optimization generates a diverse set of solutions, it can be computationally demanding for high-dimensional problems.
Effectiveness: Pareto optimization is highly effective in generating a range of trade-off solutions, though computational intensity may limit scalability.
Hybrid Multi-Objective Approaches: Hybrid approaches, which combine algorithms like genetic algorithms with PSO or simulated annealing, leverage the strengths of each method to improve convergence speed and solution quality (Li et al., 2020
). These hybrid methods have shown high effectiveness in optimizing complex structures under seismic conditions, as they provide both adaptability and robustness. However, the complexity of hybrid methods can increase computational costs.
Effectiveness: Hybrid multi-objective approaches are highly effective for complex, multi-constraint optimization problems, offering a balanced solution to computational efficiency and solution quality.
So, in general the compares the main optimization techniques in structural engineering: heuristic optimization, topology optimization, and multi-objective optimization shown in
table 5.
Table 5. Comparative Evaluation of Optimization Techniques in Structural Engineering.
Criteria | Heuristic Optimization | Topology Optimization | Multi-Objective Optimization |
Primary Strengths | Flexible for complex, nonlinear problems | Minimizes material use, improves efficiency | Balances multiple design objectives |
Common Techniques | Genetic Algorithm (GA), Particle Swarm (PSO) | SIMP, Level Set Method | Pareto Optimization, NSGA-II |
Real-World Applications | Truss/Frame Design, High-Rise Buildings | Lightweight Aerospace Components | Seismic-Resilient Industrial Facilities |
Computational Efficiency | Moderate to high computational demand | Moderate; depends on design constraints | High due to multiple objectives |
Flexibility | High adaptability, handles nonlinear constraints | Sensitive to specific constraints | Complex, adaptable to diverse objectives |
Limitations | Prone to local optima, computationally intensive | Requires post-processing for manufacturability | Difficult to interpret multiple solutions |
Optimal Context | Large-scale designs requiring resilience | Lightweight design, efficiency-focused projects | Projects balancing sustainability and cost |
7. Trends and Emerging Technologies
Recent advances in computational power, artificial intelligence (AI), and digital twin technology have introduced transformative trends in structural engineering. These developments have not only enhanced traditional optimization techniques but have also paved the way for new methodologies to address increasingly complex engineering challenges. Key trends include the integration of machine learning (ML) in structural optimization, the use of digital twins for real-time structural health monitoring (SHM), and the implementation of sustainable design practices through optimization. This section will discuss these trends and provide illustrative tables and images where relevant.
7.1. Integration of Artificial Intelligence in Optimization
Machine Learning in Structural Design Optimization
Machine learning, especially deep learning, is increasingly used to support optimization in structural engineering. Techniques like neural networks and reinforcement learning are employed to predict optimal structural configurations and improve the accuracy of simulations. For instance, recent research by
| [20] | Zhu, J., Zhang, W., & Xia, L. (2016). Topology optimization in aircraft and aerospace structures design. Archives of Computational Methods in Engineering, 23(4), 595-622. https://doi.org/10.1007/s11831-015-9151-2 |
[20]
utilized neural networks to predict stress distribution in complex structures, significantly reducing the computation time required for finite element analysis (FEA). Integrating ML allows engineers to develop efficient predictive models that can handle high-dimensional data and complex non-linear relationships.
Effectiveness: The integration of ML reduces computational costs and improves accuracy in predicting structural responses, particularly in complex configurations.
Table 6. Integration of Artificial Intelligence in Optimization.
Technique | Application | Advantages | Challenges |
Neural Networks | Stress Prediction | Fast computation, accurate modeling | Requires large datasets |
Reinforcement Learning | Design Optimization | Adaptive learning, self-improvement | Complex implementation |
Genetic Algorithms | Shape Optimization | Versatile, handles constraints well | Slow convergence |
7.2. Digital Twin Technology in Structural Health Monitoring
Figure 1. Illustration of Digital Twin Technology in Structural Health Monitoring.
Real-Time Structural Health Monitoring with Digital Twins
Digital twin technology has emerged as a powerful tool in real-time structural health monitoring (SHM) and optimization. By creating a virtual replica of a structure that continuously updates with sensor data, digital twins enable predictive maintenance and early detection of structural issues as indicated in
Figure 1. Studies, such as that by
highlight the value of digital twins in assessing the performance of bridges under dynamic loads, helping engineers monitor strain and displacement in real-time. This trend not only improves the resilience of infrastructure but also reduces maintenance costs by facilitating timely interventions.
Effectiveness: Digital twins enhance the ability to detect structural issues early, reducing downtime and maintenance costs.
7.3. Sustainability and Green Structural Optimization
Sustainable Design Practices Using Optimization Techniques
Sustainable structural design focuses on reducing environmental impact by optimizing material use, energy consumption, and carbon footprint. Optimization techniques such as topology optimization and life cycle assessment (LCA)-integrated methods are used to create eco-friendly designs. In a study by
topology optimization was applied to design lightweight components for a sustainable building, achieving a 30% reduction in material use. The optimization process also considered the environmental impact of each design choice, supporting more sustainable construction practices.
Effectiveness: Optimization techniques can significantly reduce material waste and energy use, supporting greener construction practices.
Table 7. Sustainable Design Practices Using Optimization Techniques.
Technique | Benefit | Environmental Impact |
Topology Optimization | Material reduction | Lowers carbon footprint |
Life Cycle Assessment (LCA) | Comprehensive environmental view | Encourages sustainable choices |
Energy Efficiency Optimization | Reduces operational energy demand | Minimizes energy consumption |
7.4. Generative Design and Automation
Generative Design for Complex Geometries
Generative design utilizes AI and optimization algorithms to automatically generate structural designs based on specified performance criteria. Autodesk's generative design tools have demonstrated this trend in structural engineering as shown in
Figure 2, where engineers input objectives like load-bearing capacity and material limits, and the software outputs optimized designs that meet these requirements. Generative design is particularly effective for complex, lightweight structures in aerospace and automotive applications, where traditional design methods may not achieve the same level of efficiency.
Effectiveness: Generative design automates the optimization process and can produce highly efficient, novel designs that meet multiple objectives.
Figure 2. Example of Generative Design in Structural Engineering.
7.5. Robotics and Additive Manufacturing (3D Printing)
Automation in Construction with Robotics and 3D Printing
Robotics and additive manufacturing are transforming the construction industry by allowing for precise, efficient, and customized construction methods. In structural engineering, 3D printing is being used to create complex geometries that would be challenging or impossible to achieve with traditional techniques. For instance, Zhu et al. (2019) explored 3D printing for custom-designed steel joints in large structures, achieving more efficient material usage and shorter construction times.
Effectiveness: Robotics and 3D printing offer precision and customization, improving construction efficiency and material use.
Table 8. Automation in Construction with Robotics and 3D Printing.
Technology | Application | Benefits |
Robotics | Automated construction tasks | Precision, safety, and speed |
Additive Manufacturing (3D Printing) | Custom steel joints | Reduces waste, enhances design freedom |
8. Knowledge Gaps and Future Research Directions
Although significant progress has been made in optimizing structural engineering practices, there remain several critical knowledge gaps that future research must address. These gaps present opportunities to enhance the robustness, sustainability, and adaptability of structural optimization methodologies in various engineering applications. By identifying these areas, researchers can prioritize the development of new techniques and technologies to overcome existing limitations.
8.1. Gaps in AI and Machine Learning Applications
Limited Interpretability and Transparency of AI Models
One key challenge with current AI and ML models in structural optimization is their "black box" nature. Most machine learning models, particularly deep learning, lack transparency in terms of how they process and interpret data. This limited interpretability makes it difficult for engineers to trust AI-generated outputs, especially in critical applications. Furthermore, AI models often require large datasets, which are not always available in specialized structural engineering contexts. Thus, future research should focus on developing interpretable AI models that can explain their decisions, alongside finding ways to make these models reliable with smaller datasets.
Research Directions
Develop explainable AI models that can communicate their decision-making processes to engineers.
Explore data augmentation or synthetic data generation to supplement small datasets in structural applications.
8.2. Scalability Challenges in Digital Twin Implementations
Barriers to Widespread Adoption of Digital Twins
While digital twins hold promise for real-time monitoring and optimization, they are still in the early stages of implementation. The complexity and high costs of creating a fully functional digital twin are significant barriers, particularly for large-scale infrastructure like bridges or skyscrapers. Additionally, integrating real-time data from a digital twin into a decision-making framework that provides actionable insights remains challenging. Future studies should focus on improving the scalability of digital twins and identifying affordable ways to deploy this technology across various project types.
Research Directions
Investigate cost-effective digital twin models for small to medium-scale structures.
Develop automated frameworks that streamline the data analysis and visualization process within digital twins.
8.3. Sustainability in Structural Optimization
Incomplete Life Cycle Data for Sustainable Design
Although optimization techniques such as topology optimization and life cycle assessment (LCA) contribute to sustainable structural design, there is a lack of comprehensive data regarding the environmental impact of various materials and construction methods. Current sustainability models often rely on generalized life cycle data that may not reflect region-specific impacts or advancements in material science. Furthermore, the integration of renewable materials in structural design remains underexplored.
Research Directions
Develop region-specific life cycle databases that consider local resources, regulations, and environmental factors.
Investigate sustainable materials, including bio-composites and recyclable materials, for structural engineering applications.
8.4. Emerging Technologies and Practical Integration
Limited Integration of 3D Printing and Robotics in Mainstream Construction
Despite the promising applications of robotics and additive manufacturing (3D printing), these technologies are not yet widely adopted in structural engineering practices. Technical limitations, regulatory hurdles, and a lack of standardized guidelines hinder their widespread use. There is a need for research that addresses these issues and demonstrates the feasibility and cost-effectiveness of incorporating 3D printing and robotics in practical, large-scale projects.
Research Directions
Develop guidelines and standards for the safe implementation of 3D printing and robotics in construction.
Conduct large-scale pilot projects that demonstrate the economic and structural benefits of these technologies in construction.
8.5. Optimization under Uncertainty
Gaps in Optimization Techniques for Uncertain Conditions
Structural optimization under uncertainty, such as variable loads, environmental conditions, and material degradation, remains an area with substantial gaps. Traditional optimization approaches often assume static conditions, which do not reflect the dynamic and unpredictable nature of real-world structures. While some probabilistic and stochastic methods are emerging, they are still limited in scope and computationally intensive.
Research Directions
Develop optimization algorithms that account for probabilistic variations and uncertainties in loading and material properties.
Explore hybrid methods that combine traditional optimization with adaptive algorithms capable of real-time adjustments.
8.6. Need for Cross-Disciplinary Approaches
Interdisciplinary Gaps in Optimization for Complex Structures
Structural engineering is increasingly intersecting with fields like materials science, environmental engineering, and data science. However, many optimization studies remain siloed, limiting the potential for holistic approaches that can address complex engineering challenges. For example, optimization studies rarely consider the impact of structural design choices on environmental sustainability, or the use of novel materials in optimization models. To address this gap, researchers should adopt cross-disciplinary approaches that incorporate advancements from related fields.
Research Directions
Foster interdisciplinary research that integrates structural optimization with material science innovations, environmental impact analysis, and digital data sources.
Collaborate with environmental scientists, material scientists, and data engineers to create optimization models that reflect a comprehensive set of constraints and goals.
9. Conclusion
The evolution of structural optimization practices has been significantly influenced by advancements in computational techniques, materials science, and emerging technologies. This systematic review has highlighted key trends, techniques, applications, and future directions in the field of design optimization in structural engineering. From traditional methods such as finite element analysis (FEA) and genetic algorithms, to modern innovations like artificial intelligence (AI), digital twins, and additive manufacturing, the landscape of structural optimization is rapidly changing.
Key Takeaways
Optimization Techniques: A diverse range of optimization methods, including classical approaches like FEA and newer AI-driven techniques, are being applied to solve increasingly complex structural challenges. Machine learning models, particularly deep learning, are reshaping how we predict structural behavior and optimize designs, improving efficiency and reducing computational time.
Real-World Applications: The application of optimization techniques is widespread, with notable successes in infrastructure design, aerospace, automotive industries, and more. Digital twin technology, for instance, is revolutionizing structural health monitoring by enabling real-time data integration, while 3D printing and generative design are opening new frontiers in custom and sustainable construction.
Emerging Technologies: The incorporation of AI, digital twins, sustainability practices, and robotics in optimization workflows is driving innovation in structural engineering. These technologies not only enhance efficiency but also ensure that design choices align with environmental and performance standards, addressing the growing demand for sustainable infrastructure.
Challenges and Knowledge Gaps: Despite significant progress, challenges remain, particularly in terms of model interpretability, scalability of new technologies, and integration with existing construction practices. More research is needed to bridge the gap between advanced optimization techniques and practical implementation, especially regarding uncertainty, sustainability, and interdisciplinary collaboration.
Future Research Directions: Future efforts should focus on overcoming the barriers to adopting emerging technologies like AI, digital twins, and 3D printing. Specific areas for future research include:
1. Developing interpretable AI models for structural design.
2. Enhancing the scalability and affordability of digital twin technologies.
3. Investigating sustainable materials and energy-efficient optimization strategies.
4. Addressing uncertainties in structural optimization through probabilistic methods.
5. Promoting cross-disciplinary research to integrate advancements from materials science, environmental engineering, and computational modeling.
By addressing these gaps, the structural engineering community can advance toward more efficient, resilient, and sustainable design practices. As we continue to push the boundaries of computational power and technological integration, the future of structural optimization holds immense potential to shape safer, smarter, and more environmentally conscious infrastructure.
Abbreviations
AI | Artificial Intelligence |
DT | Digital Twins |
3D Printing | Three Dimensional Printing |
GAs | Genetic Algorithms |
PSO | Particle Swarm Optimization |
SA | Simulated Annealing |
SIMP | Solid Isotropic Material with Penalization |
Ethical Approval and Consent to Participate
The content of this article does not necessitate ethical approval or consent to participate with supporting documentation.
Data Access Statement and Material Availability
The adequate resources of this article are publicly accessible.
Authors Contributions
Girmay Mengesha Azanaw is the sole author. The author read and approved the final manuscript.
Funding
This article has not been funded by any organizations or agencies. This independence ensures that the research is conducted with objectivity and without any external influence.
Conflicts of Interest
Based on my understanding, this article has no conflicts of interest.
Appendix
Table A1. Classification of Optimization Techniques.
Optimization Technique | Type | Application Areas | Advantages | Limitations |
Finite Element Analysis (FEA) | Deterministic | Stress Analysis, Vibration Modes | High accuracy, detailed results | Computationally expensive |
Genetic Algorithms | Stochastic | Shape Optimization, Load Distribution | Can handle nonlinear problems | Requires large number of iterations |
Artificial Neural Networks (ANN) | AI/ML | Structural Health Monitoring, Damage Prediction | Adaptive learning, real-time analysis | Requires large datasets |
Table A2. Real-World Applications of Optimization Techniques.
Industry/Application | Optimization Technique Used | Problem Addressed | Outcome/Benefit |
Civil Infrastructure | Genetic Algorithms, FEA | Load Distribution, Shape Design | Reduced material usage, improved stability |
Aerospace | AI/ML, Topology Optimization | Weight Reduction, Stress Minimization | Enhanced fuel efficiency, cost savings |
Figure A1. Workflow of Structural Optimization Using AI and Machine Learning.
Figure A1 illustrating the workflow of structural optimization using AI and Machine Learning. It visualizes the steps such as data collection, model training, and optimization algorithms in a clear and professional flowchart format.
Table A3. Emerging Trends and Technologies in Structural Optimization.
Technology | Description | Current Applications | Future Potential |
Digital Twin | Virtual representation of physical structures | Real-time monitoring, predictive maintenance | Widespread use for structural health monitoring |
Generative Design | AI-based design creation method | Architecture, product design | Mass adoption in customized construction |
3D Printing | Additive manufacturing for structural components | Prototype development, small-scale projects | Large-scale infrastructure construction |
Table A4. Comparison of AI and Traditional Optimization Methods.
Method | Efficiency | Accuracy | Application Scope | Data Dependency |
Finite Element Analysis | High (computational cost) | Very high | Structural analysis, vibrations | Low |
Genetic Algorithms | Medium to High | Moderate | Optimization under constraints | Moderate |
AI/ML (ANN) | High (real-time) | High (depending on dataset) | Health monitoring, predictive design | Very high |
Figure A2. Topology Optimization Process in Structural Design.
Figure A2 showing the topology optimization process in structural design, with stages illustrating the initial solid structure, the optimization phase where excess material is removed, and the final refined design with material concentrated in critical areas.
Figure A3. Digital Twin Model for Structural Health Monitoring.
Figure A3 illustrating a digital twin model for structural health monitoring of a bridge, showcasing real-time data overlays such as stress, strain, and temperature, which are crucial for assessing structural integrity.
Figure A4. 3D Printing in Structural Fabrication.
Figure A4 showing 3D printing in structural fabrication, with a robotic arm creating a structural component through precise layering. This depiction highlights the advanced technology used in construction-focused 3D printing.
Figure A5. Flowchart of Real-World Application of Optimization in Aerospace Engineering. The flowchart of the real-world application of optimization in aerospace engineering, depicting each stage from problem definition to implementation.
References
| [1] |
Bendsøe, M. P., & Sigmund, O. (2003). Topology Optimization: Theory, Methods, and Applications. Springer Science & Business Media.
https://doi.org/10.1007/978-3-662-05086-6
|
| [2] |
Deb, K. (2001). Multi-objective Optimization Using Evolutionary Algorithms. Wiley.
https://doi.org/10.1002/9780470743612
|
| [3] |
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2000). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197.
https://doi.org/10.1109/4235.996017
|
| [4] |
Goldberg, D. E. (1989). Genetic Algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.
|
| [5] |
He, S., Prempain, E., & Wu, Q. H. (2004). An improved particle swarm optimizer for mechanical design optimization problems. Engineering Optimization, 36(1), 105-123.
https://doi.org/10.1080/03052150310001636622
|
| [6] |
Kaveh, A., & Talatahari, S. (2010). A hybrid particle swarm and ant colony optimization for structural design. Engineering Structures, 32(9), 2791-2799.
https://doi.org/10.1016/j.engstruct.2010.05.012
|
| [7] |
Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of ICNN'95 - International Conference on Neural Networks, 4, 1942-1948.
https://doi.org/10.1109/ICNN.1995.488968
|
| [8] |
Li, H., Zhang, X., & Li, L. (2020). Hybrid optimization approach for multi-objective structural design in seismic zones. Journal of Structural Engineering, 146(5), 04020041.
https://doi.org/10.1061/(ASCE)ST.1943-541X.0002605
|
| [9] |
Saka, M. P., & Dogan, E. (2012). Optimum design of steel frames with semi-rigid connections using simulated annealing. Journal of Constructional Steel Research, 78, 16-23.
https://doi.org/10.1016/j.jcsr.2012.06.007
|
| [10] |
Wang, S., Cui, Y., Wang, Z., & Yao, X. (2016). Structural optimization of bridge components under wind and seismic loads. Structural and Multidisciplinary Optimization, 54(3), 731-742.
https://doi.org/10.1007/s00158-016-1444-7
|
| [11] |
Masi, F., Chesi, C., & Biondini, F. (2019). Multi-objective optimization for seismic resilience of industrial facilities. Journal of Structural Engineering, 145(8), 04019105.
https://doi.org/10.1061/(ASCE)ST.1943-541X.0002321
|
| [12] |
Sigmund, O. (2013). Topology optimization: A tool for the tailoring of structures and materials. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 361(1806), 1001-1019.
https://doi.org/10.1098/rsta.2003.1177
|
| [13] |
Deaton, J. D., & Grandhi, R. V. (2014). A survey of structural and multidisciplinary continuum topology optimization: Post 2000. Structural and Multidisciplinary Optimization, 49(1), 1–38.
https://doi.org/10.1007/s00158-013-0956-z
|
| [14] |
Ashraf, M., Noor, Z. Z., & Panneer Selvam, N. (2018). Sustainability of structural design optimization. Sustainable Cities and Society, 39, 302–313.
https://doi.org/10.1016/j.scs.2018.02.034
|
| [15] |
Frangopol, D. M., & Liu, M. (2007). Maintenance and management of civil infrastructure based on condition, safety, optimization, and life-cycle cost. Structure and Infrastructure Engineering, 3(1), 29–41.
https://doi.org/10.1080/15732470500253164
|
| [16] |
Zhuang, C., & Liu, J. (2021). Sustainable topology optimization of structures: Concepts, methods, and applications. Applied Mathematical Modelling, 94, 114–137.
https://doi.org/10.1016/j.apm.2020.08.030
|
| [17] |
Miettinen, K. (1999). Nonlinear Multiobjective Optimization. Springer.
https://doi.org/10.1007/978-1-4615-5563-6
|
| [18] |
Spencer, B. F., & Nagarajaiah, S. (2003). State of the art of structural control. Journal of Structural Engineering, 129(7), 845–856.
https://doi.org/10.1061/(ASCE)0733-9445(2003)129:7(845)
|
| [19] |
Zahrai, S. M., & Sobhani, M. R. (2017). Seismic optimization of tall buildings using genetic algorithms. Journal of Structural Engineering, 143(3), 04016180.
https://doi.org/10.1061/(ASCE)ST.1943-541X.0001665
|
| [20] |
Zhu, J., Zhang, W., & Xia, L. (2016). Topology optimization in aircraft and aerospace structures design. Archives of Computational Methods in Engineering, 23(4), 595-622.
https://doi.org/10.1007/s11831-015-9151-2
|
| [21] |
Sun, X., Wang, Q., & Chen, M. (2022). Machine learning-enhanced optimization for stress prediction in structural design. Engineering Structures, 254, 114587.
https://doi.org/10.1016/j.engstruct.2022.114587
|
| [22] |
Tao, F., Zhang, H., & Nee, A. Y. C. (2020). Digital twin in industry: State-of-the-art. Advances in Manufacturing, 8(1), 1-20.
https://doi.org/10.1007/s40436-019-00281-1
|
| [23] |
Tang, R., Li, P., & Chen, G. (2021). Topology optimization for sustainable structural design. Journal of Cleaner Production, 319, 128702.
https://doi.org/10.1016/j.jclepro.2021.128702
|
| [24] |
Nagy, D., Zhao, W., & Benjamin, D. (2017). Generative design with deep reinforcement learning. Proceedings of the Symposium on Simulation for Architecture and Urban Design.
https://doi.org/10.1145/3159450.3159486
|
| [25] |
Zhu, Z., Zhang, C., & Li, Y. (2019). Application of 3D printing in steel structure fabrication. Automation in Construction, 102, 14-20.
https://doi.org/10.1016/j.autcon.2019.02.018
|
Cite This Article
-
APA Style
Azanaw, G. M. (2025). Design Optimization in Structural Engineering: A Systematic Review of Computational Techniques and Real-World Applications. American Journal of Mechanical and Materials Engineering, 9(1), 1-19. https://doi.org/10.11648/j.ajmme.20250901.11
Copy
|
Download
ACS Style
Azanaw, G. M. Design Optimization in Structural Engineering: A Systematic Review of Computational Techniques and Real-World Applications. Am. J. Mech. Mater. Eng. 2025, 9(1), 1-19. doi: 10.11648/j.ajmme.20250901.11
Copy
|
Download
AMA Style
Azanaw GM. Design Optimization in Structural Engineering: A Systematic Review of Computational Techniques and Real-World Applications. Am J Mech Mater Eng. 2025;9(1):1-19. doi: 10.11648/j.ajmme.20250901.11
Copy
|
Download
-
@article{10.11648/j.ajmme.20250901.11,
author = {Girmay Mengesha Azanaw},
title = {Design Optimization in Structural Engineering: A Systematic Review of Computational Techniques and Real-World Applications
},
journal = {American Journal of Mechanical and Materials Engineering},
volume = {9},
number = {1},
pages = {1-19},
doi = {10.11648/j.ajmme.20250901.11},
url = {https://doi.org/10.11648/j.ajmme.20250901.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajmme.20250901.11},
abstract = {Design optimization is a cornerstone in the development of structural systems to improve efficiency, safety, and sustainability. In particular, this has become a key strategy for contemporary engineering challenges that involve the minimal use of materials with very stringent performance requirements. Advances in computational techniques revolutionized this field and enabled engineers to solve complex, multi-variable problems with unprecedented precision and creativity. This systematic review covers a range of optimization methodologies. While classical methods, such as linear and nonlinear programming, provide strong frameworks for constrained problems, they often struggle with high-dimensional or non-convex scenarios. Evolutionary algorithms, including genetic algorithms and particle swarm optimization, are highly effective in global optimization tasks but can be computationally intensive. The incorporation of machine learning has further transformed the landscape, enabling predictive modeling, pattern recognition, and adaptive optimization strategies. Hybrid models, combining such techniques, allow for flexibility with appropriate balances between accuracy and computational efficiency. The integration of such methods along with state-of-the-art technologies is the future in the area of structural engineering. As digital twins allow real-time simulating and optimization of their physical counterparts, additive manufacturing brings up new opportunities both within material and geometric design issues. Artificial intelligence acts for the automation of the designing process and delivers to new, sometimes hardly intuitively predictable solutions. This review therefore emphasizes the need for cross-disciplinary collaboration in addition to continuous innovation toward these challenges and provides a roadmap for sustainable and resilient structural design solutions.
},
year = {2025}
}
Copy
|
Download
-
TY - JOUR
T1 - Design Optimization in Structural Engineering: A Systematic Review of Computational Techniques and Real-World Applications
AU - Girmay Mengesha Azanaw
Y1 - 2025/01/07
PY - 2025
N1 - https://doi.org/10.11648/j.ajmme.20250901.11
DO - 10.11648/j.ajmme.20250901.11
T2 - American Journal of Mechanical and Materials Engineering
JF - American Journal of Mechanical and Materials Engineering
JO - American Journal of Mechanical and Materials Engineering
SP - 1
EP - 19
PB - Science Publishing Group
SN - 2639-9652
UR - https://doi.org/10.11648/j.ajmme.20250901.11
AB - Design optimization is a cornerstone in the development of structural systems to improve efficiency, safety, and sustainability. In particular, this has become a key strategy for contemporary engineering challenges that involve the minimal use of materials with very stringent performance requirements. Advances in computational techniques revolutionized this field and enabled engineers to solve complex, multi-variable problems with unprecedented precision and creativity. This systematic review covers a range of optimization methodologies. While classical methods, such as linear and nonlinear programming, provide strong frameworks for constrained problems, they often struggle with high-dimensional or non-convex scenarios. Evolutionary algorithms, including genetic algorithms and particle swarm optimization, are highly effective in global optimization tasks but can be computationally intensive. The incorporation of machine learning has further transformed the landscape, enabling predictive modeling, pattern recognition, and adaptive optimization strategies. Hybrid models, combining such techniques, allow for flexibility with appropriate balances between accuracy and computational efficiency. The integration of such methods along with state-of-the-art technologies is the future in the area of structural engineering. As digital twins allow real-time simulating and optimization of their physical counterparts, additive manufacturing brings up new opportunities both within material and geometric design issues. Artificial intelligence acts for the automation of the designing process and delivers to new, sometimes hardly intuitively predictable solutions. This review therefore emphasizes the need for cross-disciplinary collaboration in addition to continuous innovation toward these challenges and provides a roadmap for sustainable and resilient structural design solutions.
VL - 9
IS - 1
ER -
Copy
|
Download
Share This Article
Twitter
Linked In
Facebook
